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class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Giuseppe Nicosia</h3></div><div class="js-work-strip profile--work_container" data-work-id="44534766"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534766/Large_Scale_Agent_Based_Modeling_of_the_Humoral_and_Cellular_Immune_Response"><img alt="Research paper thumbnail of Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response" class="work-thumbnail" src="https://attachments.academia-assets.com/64981183/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534766/Large_Scale_Agent_Based_Modeling_of_the_Humoral_and_Cellular_Immune_Response">Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Immune System is, together with Central Nervous System, one of the most important and complex...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Immune System is, together with Central Nervous System, one of the most important and complex unit of our organism. Despite great advances in recent years that shed light on its understanding and in the unraveling of key mechanisms behind its functions, there are still many areas of the Immune System that remain object of active research. The development of in-silico models, bridged with proper biological considerations, have recently improved the understanding of important complex systems [1,2]. In this paper, after introducing major role players and principal functions of the mammalian Immune System, we present two computational approaches to its modeling; i.e., two in-silico Immune Systems. (i) A large-scale model, with a complexity of representation of 10 6 − 10 8 cells (e.g., APC, T, B and Plasma cells) and molecules (e.g., immunocomplexes), is here presented, and its evolution in time is shown to be mimicking an important region of a real immune response. (ii) Additionally, a viral infection model, stochastic and lightweight , is here presented as well: its seamless design from biological considerations, its modularity and its fast simulation times are strength points when compared to (i). Finally we report, with the intent of moving towards the virtual lymph note, a cost-benefits comparison among Immune System models presented in this paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d06f580e73dcb590ec351468af8720c3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981183,"asset_id":44534766,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981183/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534766"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534766"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534766; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534767"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534767/Experimental_Analysis_of_the_Aging_Operator_for_Static_and_Dynamic_Optimisation_Problems"><img alt="Research paper thumbnail of Experimental Analysis of the Aging Operator for Static and Dynamic Optimisation Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/64981171/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534767/Experimental_Analysis_of_the_Aging_Operator_for_Static_and_Dynamic_Optimisation_Problems">Experimental Analysis of the Aging Operator for Static and Dynamic Optimisation Problems</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://cambridge.academia.edu/GiuseppeNicosia">Giuseppe Nicosia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MarioCastrogiovanni">Mario Castrogiovanni</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work presents an analysis of the static Aging operator for different evolutionary algorithms...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This work presents an analysis of the static Aging operator for different evolutionary algorithms: two immunological algorithms (OptIA and Clonalg), a standard genetic algorithm SGA, and Differential Evolution (DE) algorithm. The algorithms were tested against standard benchmarks in both unconstrained and dynamic optimisation problems. This work analyses whether the aging operator improves the results when applied to evolutionary algorithms. With the exception of DE, the results report that every algorithm shows an improvement in performance when used in combination with Aging.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="86331b3f4dfaf9fd88a4deaa28e5787b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981171,"asset_id":44534767,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981171/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534767"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534767"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534767; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534765"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534765/Design_of_Robust_Space_Trajectories"><img alt="Research paper thumbnail of Design of Robust Space Trajectories" class="work-thumbnail" src="https://attachments.academia-assets.com/64981185/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534765/Design_of_Robust_Space_Trajectories">Design of Robust Space Trajectories</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we apply a novel black-box optimisation algorithm to the Global Trajectory optimisa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper we apply a novel black-box optimisation algorithm to the Global Trajectory optimisation Problem provided by the European Space Agency (ESA). The proposed algorithm, called SAGES, has been applied to instances of seven trajectory design problems, comparing it with the known best solutions. The numerical results show clear improvements on the majority of the problems and, in order to investigate deeply the problems, a sensitivity and solutions robustness analysis has been performed, measuring the influence of each single variable to the objective function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5af0050c0a7415e3b7bd4ec08474a22e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981185,"asset_id":44534765,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981185/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534765"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534765; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534764"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534764/New_Coupled_EM_and_Circuit_Simulation_Flow_for_Integrated_Spiral_Inductor_by_Introducing_Symbolic_Simplified_Expressions"><img alt="Research paper thumbnail of New Coupled EM and Circuit Simulation Flow for Integrated Spiral Inductor by Introducing Symbolic Simplified Expressions" class="work-thumbnail" src="https://attachments.academia-assets.com/64981170/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534764/New_Coupled_EM_and_Circuit_Simulation_Flow_for_Integrated_Spiral_Inductor_by_Introducing_Symbolic_Simplified_Expressions">New Coupled EM and Circuit Simulation Flow for Integrated Spiral Inductor by Introducing Symbolic Simplified Expressions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Microelectronics component and circuit design requires long computation time; to reduce this time...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Microelectronics component and circuit design requires long computation time; to reduce this time, the use of simplification techniques has been introduced. In order to obtain a first validation of the method, a first test case is presented; the simplification techniques have been applied to the analytical expression of Y parameters of an inductor equivalent circuit. The resulting expressions have been used in the fitting process in order to reproduce the behaviour of a simulated inductor. Five different optimization algorithms, both deterministic (POWELL and DIRECT) and stochastic (CRS, CRS ENHANCED and OPTIA) have been tested for the fitting. The result of the introduction of the simplification techniques has been the reduction of the running time during the fitting. From an optimization point of view, the best results have been obtained by the stochastic algorithms CRS, and OPTIA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e310ee34b9682b55e87a8f6c20703a04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981170,"asset_id":44534764,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981170/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534764"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534764"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534764; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534764]").text(description); $(".js-view-count[data-work-id=44534764]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534764; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534764']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e310ee34b9682b55e87a8f6c20703a04" } } $('.js-work-strip[data-work-id=44534764]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":44534764,"title":"New Coupled EM and Circuit Simulation Flow for Integrated Spiral Inductor by Introducing Symbolic Simplified Expressions","internal_url":"https://www.academia.edu/44534764/New_Coupled_EM_and_Circuit_Simulation_Flow_for_Integrated_Spiral_Inductor_by_Introducing_Symbolic_Simplified_Expressions","owner_id":35764061,"coauthors_can_edit":true,"owner":{"id":35764061,"first_name":"Giuseppe","middle_initials":null,"last_name":"Nicosia","page_name":"GiuseppeNicosia","domain_name":"cambridge","created_at":"2015-10-06T11:52:44.191-07:00","display_name":"Giuseppe Nicosia","url":"https://cambridge.academia.edu/GiuseppeNicosia"},"attachments":[{"id":64981170,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64981170/thumbnails/1.jpg","file_name":"Nicosia_ISIE08.pdf","download_url":"https://www.academia.edu/attachments/64981170/download_file","bulk_download_file_name":"New_Coupled_EM_and_Circuit_Simulation_Fl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64981170/Nicosia_ISIE08-libre.pdf?1605800224=\u0026response-content-disposition=attachment%3B+filename%3DNew_Coupled_EM_and_Circuit_Simulation_Fl.pdf\u0026Expires=1740435971\u0026Signature=BHfmMYCET1AckS3VvO4~1svstarMiNcAa9IdtDmkhPqV0vPH2k3sjakwoLwqxJX8gx24TrTNNUPJxstN4sljzKvdtW0CY~87WD4gLJ-2pG5OxVHQHDLM8gmV80zdc0OrcKss9LxfD5TiX~mHjkfS3d~eVYXnEd4hOQVZw3RY-PlUbJxiXK5YzLrfvZkU0uv8CmY9Zoyx78wkxv~3n7n5196o~8-a-jd36OztKoUhMCgQ65NAYLtsPYa7lm2ZGfwB69IeVnok2ABwJoi6DFFWddSQoE6iWC59CJ1RN3MX5lrPFU2~CUEuZZ2B0TT1pyy6bejGX8~TEtMOi17ClDTx9Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534763"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534763/A_Cross_Format_Framework_for_Consistent_Information_Integration_among_Molecular_Pathways_and_Ontologies"><img alt="Research paper thumbnail of A Cross-Format Framework for Consistent Information Integration among Molecular Pathways and Ontologies" class="work-thumbnail" src="https://attachments.academia-assets.com/64981172/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534763/A_Cross_Format_Framework_for_Consistent_Information_Integration_among_Molecular_Pathways_and_Ontologies">A Cross-Format Framework for Consistent Information Integration among Molecular Pathways and Ontologies</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://cambridge.academia.edu/GiuseppeNicosia">Giuseppe Nicosia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/CDewey1">C. Dewey</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The information coming from biomedical on-tologies and runnable pathways is expanding continuousl...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The information coming from biomedical on-tologies and runnable pathways is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. Having different objectives and different abstraction levels, most of these resources "speak" different languages. Employing an extensible collection of interpreters, we propose a system that abstracts the information from different resources and combines them together into a common meta-format. Preserving the resource independence, we provide an alignment service that can be used for multiple purposes. Two recent examples are: 1) The new web application Cytosolve uses an embedded version of this system to provide congruous parallel simulation of multiple models; 2) Using the BioModels.net database, a searchable dictionary of equivalent molecular reaction paths was built. Finally, the enriched knowledge can be exported in OWL and queried by semantically enabled tools such as Protégé. In this approach, we see a valuable tool to integrate and test information originating from different sources, while preserving the independence of the model curation process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="52d7a4b86a667c7e5ab41addc7a51498" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981172,"asset_id":44534763,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981172/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534763"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534763"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534763; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534763]").text(description); $(".js-view-count[data-work-id=44534763]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534763; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534763']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534762"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534762/Real_Coded_Clonal_Selection_Algorithm_for_Unconstrained_Global_Optimization_using_a_Hybrid_Inversely_Proportional_Hypermutation_Operator"><img alt="Research paper thumbnail of Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hypermutation Operator" class="work-thumbnail" src="https://attachments.academia-assets.com/64981176/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534762/Real_Coded_Clonal_Selection_Algorithm_for_Unconstrained_Global_Optimization_using_a_Hybrid_Inversely_Proportional_Hypermutation_Operator">Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hypermutation Operator</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="752a9b4ad1ebde886b796a91a8235021" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981176,"asset_id":44534762,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981176/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534762"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534762"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534762; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534762]").text(description); $(".js-view-count[data-work-id=44534762]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534762; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534762']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "752a9b4ad1ebde886b796a91a8235021" } } $('.js-work-strip[data-work-id=44534762]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":44534762,"title":"Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hypermutation Operator","internal_url":"https://www.academia.edu/44534762/Real_Coded_Clonal_Selection_Algorithm_for_Unconstrained_Global_Optimization_using_a_Hybrid_Inversely_Proportional_Hypermutation_Operator","owner_id":35764061,"coauthors_can_edit":true,"owner":{"id":35764061,"first_name":"Giuseppe","middle_initials":null,"last_name":"Nicosia","page_name":"GiuseppeNicosia","domain_name":"cambridge","created_at":"2015-10-06T11:52:44.191-07:00","display_name":"Giuseppe Nicosia","url":"https://cambridge.academia.edu/GiuseppeNicosia"},"attachments":[{"id":64981176,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64981176/thumbnails/1.jpg","file_name":"Nicosia_SAC06_opt.pdf","download_url":"https://www.academia.edu/attachments/64981176/download_file","bulk_download_file_name":"Real_Coded_Clonal_Selection_Algorithm_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64981176/Nicosia_SAC06_opt-libre.pdf?1605800224=\u0026response-content-disposition=attachment%3B+filename%3DReal_Coded_Clonal_Selection_Algorithm_fo.pdf\u0026Expires=1740435971\u0026Signature=Fu0wAU0e2xPvRmEW1E-0T6DYkMsjwA~Jpge933UCodCiDvoUmp5e7M4w4qdud1LvWE2st6LkYwHaK6P8yZLJKFs2Q3iU0p-WTnEJfGXCP4~66SGaVSZk8E4OC0OoonG79ELWmN-PvdqtQrazc8T0SvBPGneuZLbFDiKvx6Ui5KRXX-9BvtWMhWCQT4wF2Wv-PWSFR3YHZKjMDbF1eYrBkh-9iUI9r6T-fkEuLRHeTabU~xOK5C4PZtjSkcPoL36AqgrX6Om5qdQQ1WkIwR5m6C8ONoIhczWs3twxTSfYmvSUj7wquk6EwoOsY0-JGlFFc6QgmeJvon1rSxiBRxxj-A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534756"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534756/Vol_596_urn_nbn_de_0074_596_3_ORES_2010_Ontology_Repositories_and_Editors_for_the_Semantic_Web_Proceedings_of_the_1st_Workshop_on_Ontology_Repositories_and_Editors_for_the_Semantic_Web_OREMP_Ontology_Reasoning_Engine_for_Molecular_Pathways"><img alt="Research paper thumbnail of Vol-596 urn:nbn:de:0074-596-3 ORES-2010 Ontology Repositories and Editors for the Semantic Web Proceedings of the 1st Workshop on Ontology Repositories and Editors for the Semantic Web OREMP: Ontology Reasoning Engine for Molecular Pathways" class="work-thumbnail" src="https://attachments.academia-assets.com/64981169/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534756/Vol_596_urn_nbn_de_0074_596_3_ORES_2010_Ontology_Repositories_and_Editors_for_the_Semantic_Web_Proceedings_of_the_1st_Workshop_on_Ontology_Repositories_and_Editors_for_the_Semantic_Web_OREMP_Ontology_Reasoning_Engine_for_Molecular_Pathways">Vol-596 urn:nbn:de:0074-596-3 ORES-2010 Ontology Repositories and Editors for the Semantic Web Proceedings of the 1st Workshop on Ontology Repositories and Editors for the Semantic Web OREMP: Ontology Reasoning Engine for Molecular Pathways</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The information about molecular processes is shared continuously in the form of runnable pathway ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The information about molecular processes is shared continuously in the form of runnable pathway collections, and biomedical ontologies provide a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Here we propose a strategy that integrates these resources by means of extended ontologies built on top of a common meta-format. Information sharing, integration and discovery are the primary features provided by the system; additionally, two current field applications of the system are reported.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="423a6569dd6f7e22814a13a4bfbb760a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981169,"asset_id":44534756,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981169/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534756"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534756"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534756; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534761"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534761/A_Design_for_Yield_Algorithm_to_Assess_and_Improve_the_Structural_and_Energetic_Robustness_of_Proteins_and_Drugs"><img alt="Research paper thumbnail of A Design-for-Yield Algorithm to Assess and Improve the Structural and Energetic Robustness of Proteins and Drugs" class="work-thumbnail" src="https://attachments.academia-assets.com/64981186/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534761/A_Design_for_Yield_Algorithm_to_Assess_and_Improve_the_Structural_and_Energetic_Robustness_of_Proteins_and_Drugs">A Design-for-Yield Algorithm to Assess and Improve the Structural and Energetic Robustness of Proteins and Drugs</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Robustness is a property that pervades all aspects of nature. The ability of a system to adapt to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Robustness is a property that pervades all aspects of nature. The ability of a system to adapt to perturbations due to internal and external agents, aging, wear, or to environmental changes is one of the driving forces of evolution. At the molecular level, understanding the ro-bustness of a protein has a great impact on the in-silico design of polypep-tide chains and drugs. The chance of computationally checking the ability of a protein to preserve its structure in the native state may lead to the design of new compounds that can work in a living cell more effectively. Inspired by the well known robustness analysis framework used in Electronic Design Automation, we introduce a formal definition of robustness for proteins and a dimensionless quantity, called yield, to quantify the robustness of a protein. Then, we introduce a new robustness-centered protein design algorithm called Design-For-Yield. The aim of the algorithm is to discover new conformations with a specific functionality and high yield values. We present extensive characterizations of the robust-ness properties of many peptides, proteins, and drugs. Finally, we apply the DFY algorithm on the Crambin protein (1CRN) and on the Oxic-itin drug (DB00107). The obtained results confirm that the algorithm is able to discover a Crambin-like protein that is 23.61% more robust than the wild type. Concerning the Oxicitin drug a new protein sequence and the corresponding protein structure was discovered with an improved robustness of 3% at the global level.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bdb83414343a1ab28b1f100017b7c973" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981186,"asset_id":44534761,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981186/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534761"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534761"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534761; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534761]").text(description); $(".js-view-count[data-work-id=44534761]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534761; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534761']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534760"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534760/Generalized_Pattern_Search_and_Mesh_Adaptive_Direct_Search_Algorithms_for_Protein_Structure_Prediction"><img alt="Research paper thumbnail of Generalized Pattern Search and Mesh Adaptive Direct Search Algorithms for Protein Structure Prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/64981178/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534760/Generalized_Pattern_Search_and_Mesh_Adaptive_Direct_Search_Algorithms_for_Protein_Structure_Prediction">Generalized Pattern Search and Mesh Adaptive Direct Search Algorithms for Protein Structure Prediction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Proteins are the most important molecular entities of a living organism and understanding their f...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Proteins are the most important molecular entities of a living organism and understanding their functions is an important task to treat diseases and synthesize new drugs. It is largely known that the function of a protein is strictly related to its spatial conformation: to tackle this problem, we have proposed a new approach based on a class of pattern search algorithms that is largely used in optimization of real world applications. The obtained results are interesting in terms of the quality of the structures (RMSD-Cα) and energy values found.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="550559234b48b22a207a71d9c6b89793" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981178,"asset_id":44534760,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981178/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534760"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534760"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534760; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "550559234b48b22a207a71d9c6b89793" } } $('.js-work-strip[data-work-id=44534760]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":44534760,"title":"Generalized Pattern Search and Mesh Adaptive Direct Search Algorithms for Protein Structure Prediction","internal_url":"https://www.academia.edu/44534760/Generalized_Pattern_Search_and_Mesh_Adaptive_Direct_Search_Algorithms_for_Protein_Structure_Prediction","owner_id":35764061,"coauthors_can_edit":true,"owner":{"id":35764061,"first_name":"Giuseppe","middle_initials":null,"last_name":"Nicosia","page_name":"GiuseppeNicosia","domain_name":"cambridge","created_at":"2015-10-06T11:52:44.191-07:00","display_name":"Giuseppe Nicosia","url":"https://cambridge.academia.edu/GiuseppeNicosia"},"attachments":[{"id":64981178,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64981178/thumbnails/1.jpg","file_name":"Nicosia_WABI07.pdf","download_url":"https://www.academia.edu/attachments/64981178/download_file","bulk_download_file_name":"Generalized_Pattern_Search_and_Mesh_Adap.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64981178/Nicosia_WABI07-libre.pdf?1605800225=\u0026response-content-disposition=attachment%3B+filename%3DGeneralized_Pattern_Search_and_Mesh_Adap.pdf\u0026Expires=1740435971\u0026Signature=O00Ju7ftYsRBSPtilGjAng5bTNrdztui6vQJP62SAyHKXT9XkWjOsJ2zQkufH8Ou4vNDS~TWCxTQjh6n9L~sxNUKCvXXrDj4j7W3EUYe5lz8ZdKN9WA2xiTxLSwlBZXnXMJaHUUm7B96u7a4soHJ~rZQHzpw9uiqdQcFID9lFRjhPPy1A7qphoyFLJZNtJus47S3RUSBI2-jYxpQz~ikp~auq2Xxi6-BWO7Qyn~on3-Oa6SLq1bJpnHAqJLWgvAyyoXxRK-Y13ni0rOjTQwiurEq2l8As0d09~ULR9qSV73Zptdqi6LHGFwWPUgZT059-Wup0dow5u56vhr3qMTs4w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534759"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534759/Nominal_Yield_Area_Tradeoff_in_Automatic_Synthesis_of_Analog_Circuits_A_Genetic_Programming_Approach_using_Immune_Inspired_Operators"><img alt="Research paper thumbnail of Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators" class="work-thumbnail" src="https://attachments.academia-assets.com/64981182/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534759/Nominal_Yield_Area_Tradeoff_in_Automatic_Synthesis_of_Analog_Circuits_A_Genetic_Programming_Approach_using_Immune_Inspired_Operators">Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The synthesis of analog circuits is a complex and expensive task; whilst there are various approa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The synthesis of analog circuits is a complex and expensive task; whilst there are various approaches for the synthesis of digital circuits, analog design is intrinsically more difficult since analog circuits process voltages in a continuous range. In the field of analog circuit design, the genetic programming approach has received great attention, affording the possibility to design and optimize a circuit at the same time. However, these algorithms have limited industrial relevance, since they work with ideal components. Starting from the well known results of Koza and co-authors, we introduce a new evolutionary algorithm, called elitist Immune Programming (EIP), that is able to synthesize an analog circuit using industrial components series in order to produce reliable and low cost circuits. The algorithm has been used for the synthesis of low-pass filters; the results were compared with the genetic programming, and the analysis shows that EIP is able to design better circuits in terms of frequency response and number of components. In addition we conduct a complete yield analysis of the discovered circuits, and discover that EIP circuits attain a higher yield than the circuits generated via a genetic programming approach, and, in particular, the algorithm discovers a Pareto Front which respects nominal performance (sizing), number of components (area) and yield (robustness).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03c2af5e69821bdf983f59ed9130af53" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981182,"asset_id":44534759,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981182/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534759"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534759"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534759; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534752"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534752/Analysis_and_Optimization_of_C_3_Photosynthetic_Carbon_Metabolism"><img alt="Research paper thumbnail of Analysis and Optimization of C 3 Photosynthetic Carbon Metabolism" class="work-thumbnail" src="https://attachments.academia-assets.com/64981175/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534752/Analysis_and_Optimization_of_C_3_Photosynthetic_Carbon_Metabolism">Analysis and Optimization of C 3 Photosynthetic Carbon Metabolism</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://cambridge.academia.edu/GiuseppeNicosia">Giuseppe Nicosia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AlessioPapini">Alessio Papini</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have studied the C3 photosynthetic carbon metabolism centering our investigation on the follow...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have studied the C3 photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the C3 photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robust-ness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the 135% from 15.486 µmol m −2 s −1 to 36.382 µmol m −2 s −1 , and improving the previous best-found photosynthetic productivity value (27.261 µmol m −2 s −1 , 76% of enhancement). Optimized enzyme concentrations express a maximal local robustness (100%) and a high global robustness (97.2%), satisfactory properties for a possible "in vitro" manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the trade-off between the maximization of the leaf CO2 uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three c i concentrations referring to the estimate of CO2 concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aa8d4aed19da2e2175cc31e7e573826e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981175,"asset_id":44534752,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981175/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534752"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534752"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534752; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534758"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534758/Global_Optimization_for_Algebraic_Geometry_Computing_Runge_Kutta_Methods"><img alt="Research paper thumbnail of Global Optimization for Algebraic Geometry -Computing Runge-Kutta Methods" class="work-thumbnail" src="https://attachments.academia-assets.com/64981187/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534758/Global_Optimization_for_Algebraic_Geometry_Computing_Runge_Kutta_Methods">Global Optimization for Algebraic Geometry -Computing Runge-Kutta Methods</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This research work presents a new evolutionary optimization algorithm, EVO-RUNGE-KUTTA in theoret...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This research work presents a new evolutionary optimization algorithm, EVO-RUNGE-KUTTA in theoretical mathematics with applications in scientific computing. We illustrate the application of EVO-RUNGE-KUTTA, a two-phase optimization algorithm, to a problem of pure algebra, the study of the parameterization of an algebraic variety, an open problem in algebra. Results show the design and optimization of particular algebraic varieties, the Runge-Kutta methods of order q. The mapping between algebraic geometry and evolutionary optimization is direct, and we expect that many open problems in pure algebra will be modelled as constrained global optimization problems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4ec224accebd6267e8268ffaf82178b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981187,"asset_id":44534758,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981187/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534758"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534758"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534758; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534757"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534757/An_Advanced_Clonal_Selection_Algorithm_with_Ad_Hoc_Network_Based_Hypermutation_Operators_for_Synthesis_of_Topology_and_Sizing_of_Analog_Electrical_Circuits"><img alt="Research paper thumbnail of An Advanced Clonal Selection Algorithm with Ad-Hoc Network-Based Hypermutation Operators for Synthesis of Topology and Sizing of Analog Electrical Circuits" class="work-thumbnail" src="https://attachments.academia-assets.com/64981174/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534757/An_Advanced_Clonal_Selection_Algorithm_with_Ad_Hoc_Network_Based_Hypermutation_Operators_for_Synthesis_of_Topology_and_Sizing_of_Analog_Electrical_Circuits">An Advanced Clonal Selection Algorithm with Ad-Hoc Network-Based Hypermutation Operators for Synthesis of Topology and Sizing of Analog Electrical Circuits</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In electronics, there are two major classes of circuits, analog and digital electrical circuits. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In electronics, there are two major classes of circuits, analog and digital electrical circuits. While digital circuits use discrete voltage levels, analog circuits use a continuous range of voltage. The synthesis of analog circuits is known to be a complex optimization task, due to the continuous behaviour of the output and the lack of automatic design tools; actually, the design process is almost entirely demanded to the engineers. In this research work, we introduce a new clonal selection algorithm, the elitist Immune Programming, (eIP) which uses a new class of hypermu-tation operators and a network-based coding. The eIP algorithm is designed for the synthesis of topology and sizing of analog electrical circuits; in particular, it has been used for the design of passive filters. To assess the effectiveness of the designed algorithm, the obtained results have been compared with the passive filter discovered by Koza and co-authors using the Genetic Programming (GP) algorithm. The circuits obtained by eIP algorithm are better than the one found by GP in terms of frequency response and number of components required to build it.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3aef3ca73bbd4aca824b2d44f8f38ea3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981174,"asset_id":44534757,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981174/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534757"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534757"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534757; 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We compared deterministic optimization algorithms and evolutionary algorithms in terms of robustness of the resulting parameters including all sources of uncertainty into the statistical representation of reference data and evaluating the obtained solutions in terms of confident limits. The experimental results obtained show as evolutionary algorithms are more robust with respect of deterministic optimization algorithms in particular the algorithm Differential Evolution (DE) showed the best performance over the minimization of the fitting function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7f878748cac12f86c360314777e36858" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981193,"asset_id":44534751,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981193/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534751"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534751"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534751; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534755"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534755/Analysis_of_an_Evolutionary_Algorithm_with_Hypermacromutation_and_Stop_at_First_Constructive_Mutation_Heuristic_for_Solving_Trap_Functions"><img alt="Research paper thumbnail of Analysis of an Evolutionary Algorithm with Hypermacromutation and Stop at First Constructive Mutation Heuristic for Solving Trap Functions" class="work-thumbnail" src="https://attachments.academia-assets.com/64981188/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534755/Analysis_of_an_Evolutionary_Algorithm_with_Hypermacromutation_and_Stop_at_First_Constructive_Mutation_Heuristic_for_Solving_Trap_Functions">Analysis of an Evolutionary Algorithm with Hypermacromutation and Stop at First Constructive Mutation Heuristic for Solving Trap Functions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary algorithm (EA) to optimize basic and complex trap functions. The designed evolutionary algorithm uses perturbation operators based on hypermacromutation and stop at first constructive mutation heuristic. The experimental and theoretical results show that the algorithm successfully achieves its goal in facing this computational problem. The low number of evaluations to solutions expected through the theoretical analysis of the EA have been fully confirmed by the experimental results. To our knowledge the designed EA is the state-of-art algorithm to face trap function problems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66783255610369c195413ddcf1b20dab" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981188,"asset_id":44534755,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981188/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534755"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534755"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534755; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534750"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534750/Pareto_epsilon_Dominance_and_Identifiable_Solutions_for_BioCAD_Modeling"><img alt="Research paper thumbnail of Pareto epsilon-Dominance and Identifiable Solutions for BioCAD Modeling" class="work-thumbnail" src="https://attachments.academia-assets.com/64981173/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534750/Pareto_epsilon_Dominance_and_Identifiable_Solutions_for_BioCAD_Modeling">Pareto epsilon-Dominance and Identifiable Solutions for BioCAD Modeling</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a framework to design metabolic pathways in which many objectives are optimized simult...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a framework to design metabolic pathways in which many objectives are optimized simultaneously. This allows to characterize the energy signature in models of algal and mitochondrial metabolism. The optimal design and assessment of the model is achieved through a multi-objective optimization technique driven by epsilon-dominance and iden-tifiability analysis. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance , regulating the granularity of the approximation of the Pareto front. Our framework is also suitable for black-box analysis, enabling to investigate and optimize any biological pathway modeled with ODEs, DAEs, FBA and GPR.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c3c511c00eae9ec8781a54af75b23e8a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981173,"asset_id":44534750,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981173/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534750"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534750"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534750; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534753"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534753/On_the_Convergence_of_Immune_Algorithms"><img alt="Research paper thumbnail of On the Convergence of Immune Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/64981180/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534753/On_the_Convergence_of_Immune_Algorithms">On the Convergence of Immune Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Immune Algorithms have been used widely and successfully in many computational intelligence areas...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Immune Algorithms have been used widely and successfully in many computational intelligence areas including optimization. Given the large number of variants of each operator of this class of algorithms, this paper presents a study of the convergence properties of Immune Algorithms in general, conducted by examining conditions which are sufficient to prove their convergence to the global optimum of an optimization problem. Furthermore problem independent upper bounds for the number of generations required to guarantee that the solution is found with a defined probability are derived in a similar manner as performed previously, in literature, for genetic algorithms. Again the independence of the function to be optimised leads to an upper bound which is not of practical interest, confirming the general idea that when deriving time bounds for Evolutionary Algorithms the problem class to be optimised needs to be considered.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1bb0abd2c22d320f688d7b48c0fb07c8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981180,"asset_id":44534753,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981180/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534753"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534753"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534753; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534754"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534754/Clonal_Selection_Algorithms_A_Comparative_Case_Study_Using_Effective_Mutation_Potentials"><img alt="Research paper thumbnail of Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials" class="work-thumbnail" src="https://attachments.academia-assets.com/64981177/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534754/Clonal_Selection_Algorithms_A_Comparative_Case_Study_Using_Effective_Mutation_Potentials">Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLON...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="edfba97c83a4798bf89732700c248ac8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981177,"asset_id":44534754,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981177/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534754"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534754"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534754; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534749"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534749/Multi_objective_Optimisation_Sensitivity_and_Robustness_Analysis_in_FBA_Modelling"><img alt="Research paper thumbnail of Multi-objective Optimisation, Sensitivity and Robustness Analysis in FBA Modelling" class="work-thumbnail" src="https://attachments.academia-assets.com/64981179/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534749/Multi_objective_Optimisation_Sensitivity_and_Robustness_Analysis_in_FBA_Modelling">Multi-objective Optimisation, Sensitivity and Robustness Analysis in FBA Modelling</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, we propose a computational framework to design in silico robust bacteria able to ov...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this work, we propose a computational framework to design in silico robust bacteria able to overproduce multiple metabolites. To this end, we search the optimal genetic manipulations, in terms of knockout , which also guarantee the growth of the organism. We introduce a multi-objective optimisation algorithm, called Genetic Design through Multi-Objective (GDMO), and test it in several organisms to maximise the production of key intermediate metabolites such as succinate and acetate. We obtain a vast set of Pareto optimal solutions; each of them represents an organism strain. For each solution, we evaluate the fragility by calculating three robustness indexes and by exploring reactions and metabolite interactions. Finally, we perform the Sensitivity Analysis of the metabolic model, which finds the inputs with the highest influence on the outputs of the model. We show that our methodology provides effective vision of the achievable synthetic strain landscape and a powerful design pipeline.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="07c587c87960f519fbdf65df429bd3a6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981179,"asset_id":44534749,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981179/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534749"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534749"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534749; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534748"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534748/Detecting_Constituent_Sequences_by_Means_of_HP_Pattern_Based_Grammars_to_Synthesize_Proteins_Inferring_Sequence_Structure_Function_Relationship"><img alt="Research paper thumbnail of Detecting Constituent Sequences by Means of HP Pattern-Based Grammars to Synthesize Proteins: Inferring Sequence-Structure-Function Relationship" class="work-thumbnail" src="https://attachments.academia-assets.com/64981184/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534748/Detecting_Constituent_Sequences_by_Means_of_HP_Pattern_Based_Grammars_to_Synthesize_Proteins_Inferring_Sequence_Structure_Function_Relationship">Detecting Constituent Sequences by Means of HP Pattern-Based Grammars to Synthesize Proteins: Inferring Sequence-Structure-Function Relationship</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The detection of protein characters that could reveal how protein chains are constituted, is an i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The detection of protein characters that could reveal how protein chains are constituted, is an important step to understand the main functions of specific classes of proteins. We made use of the concept of "HP Pattern-Based" grammars to study the connection between protein chains and protein functions. In order to consider the structure of the proteins the HP models were used. Amino acid sequences were treated as a formal language, and it was built a set of HP Pattern-Based grammars to describe this language by means the Teiresias pattern discovery tool. First, this methodology was tested on the class of Antimi-crobial peptides (AmPs). The deduced derivation rules of HP Pattern-Based Grammars were validated by the regular grammar designed by [11] which was used to create new, unnatural, AmPs sequences. Then, our approach was applied to characterize a function of the Pleckstrin Homo-logy domain(PH Domain) which represents an important three dimensional domain which bind to phosphoinositides. Nowadays, interactions among PH domain amino acids and inositol phosphate are not well characterized. For the first time, by means of an HP Pattern-Based grammar, we highlight that this binding function can be described in terms of hydrophocity patterns. Our approach points out some fundamental aspects regarding the relationship between sequence, structure and function of proteins.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="385fc353868b5ca98ec537ca3ddb3ab4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981184,"asset_id":44534748,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981184/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534748"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534748"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534748; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3692297" id="papers"><div class="js-work-strip profile--work_container" data-work-id="44534766"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534766/Large_Scale_Agent_Based_Modeling_of_the_Humoral_and_Cellular_Immune_Response"><img alt="Research paper thumbnail of Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response" class="work-thumbnail" src="https://attachments.academia-assets.com/64981183/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534766/Large_Scale_Agent_Based_Modeling_of_the_Humoral_and_Cellular_Immune_Response">Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Immune System is, together with Central Nervous System, one of the most important and complex...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Immune System is, together with Central Nervous System, one of the most important and complex unit of our organism. Despite great advances in recent years that shed light on its understanding and in the unraveling of key mechanisms behind its functions, there are still many areas of the Immune System that remain object of active research. The development of in-silico models, bridged with proper biological considerations, have recently improved the understanding of important complex systems [1,2]. In this paper, after introducing major role players and principal functions of the mammalian Immune System, we present two computational approaches to its modeling; i.e., two in-silico Immune Systems. (i) A large-scale model, with a complexity of representation of 10 6 − 10 8 cells (e.g., APC, T, B and Plasma cells) and molecules (e.g., immunocomplexes), is here presented, and its evolution in time is shown to be mimicking an important region of a real immune response. (ii) Additionally, a viral infection model, stochastic and lightweight , is here presented as well: its seamless design from biological considerations, its modularity and its fast simulation times are strength points when compared to (i). Finally we report, with the intent of moving towards the virtual lymph note, a cost-benefits comparison among Immune System models presented in this paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d06f580e73dcb590ec351468af8720c3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981183,"asset_id":44534766,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981183/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534766"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534766"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534766; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534767"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534767/Experimental_Analysis_of_the_Aging_Operator_for_Static_and_Dynamic_Optimisation_Problems"><img alt="Research paper thumbnail of Experimental Analysis of the Aging Operator for Static and Dynamic Optimisation Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/64981171/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534767/Experimental_Analysis_of_the_Aging_Operator_for_Static_and_Dynamic_Optimisation_Problems">Experimental Analysis of the Aging Operator for Static and Dynamic Optimisation Problems</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://cambridge.academia.edu/GiuseppeNicosia">Giuseppe Nicosia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MarioCastrogiovanni">Mario Castrogiovanni</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work presents an analysis of the static Aging operator for different evolutionary algorithms...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This work presents an analysis of the static Aging operator for different evolutionary algorithms: two immunological algorithms (OptIA and Clonalg), a standard genetic algorithm SGA, and Differential Evolution (DE) algorithm. The algorithms were tested against standard benchmarks in both unconstrained and dynamic optimisation problems. This work analyses whether the aging operator improves the results when applied to evolutionary algorithms. With the exception of DE, the results report that every algorithm shows an improvement in performance when used in combination with Aging.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="86331b3f4dfaf9fd88a4deaa28e5787b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981171,"asset_id":44534767,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981171/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534767"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534767"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534767; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534765"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534765/Design_of_Robust_Space_Trajectories"><img alt="Research paper thumbnail of Design of Robust Space Trajectories" class="work-thumbnail" src="https://attachments.academia-assets.com/64981185/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534765/Design_of_Robust_Space_Trajectories">Design of Robust Space Trajectories</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we apply a novel black-box optimisation algorithm to the Global Trajectory optimisa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper we apply a novel black-box optimisation algorithm to the Global Trajectory optimisation Problem provided by the European Space Agency (ESA). The proposed algorithm, called SAGES, has been applied to instances of seven trajectory design problems, comparing it with the known best solutions. The numerical results show clear improvements on the majority of the problems and, in order to investigate deeply the problems, a sensitivity and solutions robustness analysis has been performed, measuring the influence of each single variable to the objective function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5af0050c0a7415e3b7bd4ec08474a22e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981185,"asset_id":44534765,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981185/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534765"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534765; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534764"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534764/New_Coupled_EM_and_Circuit_Simulation_Flow_for_Integrated_Spiral_Inductor_by_Introducing_Symbolic_Simplified_Expressions"><img alt="Research paper thumbnail of New Coupled EM and Circuit Simulation Flow for Integrated Spiral Inductor by Introducing Symbolic Simplified Expressions" class="work-thumbnail" src="https://attachments.academia-assets.com/64981170/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534764/New_Coupled_EM_and_Circuit_Simulation_Flow_for_Integrated_Spiral_Inductor_by_Introducing_Symbolic_Simplified_Expressions">New Coupled EM and Circuit Simulation Flow for Integrated Spiral Inductor by Introducing Symbolic Simplified Expressions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Microelectronics component and circuit design requires long computation time; to reduce this time...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Microelectronics component and circuit design requires long computation time; to reduce this time, the use of simplification techniques has been introduced. In order to obtain a first validation of the method, a first test case is presented; the simplification techniques have been applied to the analytical expression of Y parameters of an inductor equivalent circuit. The resulting expressions have been used in the fitting process in order to reproduce the behaviour of a simulated inductor. Five different optimization algorithms, both deterministic (POWELL and DIRECT) and stochastic (CRS, CRS ENHANCED and OPTIA) have been tested for the fitting. The result of the introduction of the simplification techniques has been the reduction of the running time during the fitting. From an optimization point of view, the best results have been obtained by the stochastic algorithms CRS, and OPTIA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e310ee34b9682b55e87a8f6c20703a04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981170,"asset_id":44534764,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981170/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534764"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534764"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534764; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534763"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534763/A_Cross_Format_Framework_for_Consistent_Information_Integration_among_Molecular_Pathways_and_Ontologies"><img alt="Research paper thumbnail of A Cross-Format Framework for Consistent Information Integration among Molecular Pathways and Ontologies" class="work-thumbnail" src="https://attachments.academia-assets.com/64981172/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534763/A_Cross_Format_Framework_for_Consistent_Information_Integration_among_Molecular_Pathways_and_Ontologies">A Cross-Format Framework for Consistent Information Integration among Molecular Pathways and Ontologies</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://cambridge.academia.edu/GiuseppeNicosia">Giuseppe Nicosia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/CDewey1">C. Dewey</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The information coming from biomedical on-tologies and runnable pathways is expanding continuousl...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The information coming from biomedical on-tologies and runnable pathways is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. Having different objectives and different abstraction levels, most of these resources "speak" different languages. Employing an extensible collection of interpreters, we propose a system that abstracts the information from different resources and combines them together into a common meta-format. Preserving the resource independence, we provide an alignment service that can be used for multiple purposes. Two recent examples are: 1) The new web application Cytosolve uses an embedded version of this system to provide congruous parallel simulation of multiple models; 2) Using the BioModels.net database, a searchable dictionary of equivalent molecular reaction paths was built. Finally, the enriched knowledge can be exported in OWL and queried by semantically enabled tools such as Protégé. In this approach, we see a valuable tool to integrate and test information originating from different sources, while preserving the independence of the model curation process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="52d7a4b86a667c7e5ab41addc7a51498" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981172,"asset_id":44534763,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981172/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534763"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534763"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534763; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "52d7a4b86a667c7e5ab41addc7a51498" } } $('.js-work-strip[data-work-id=44534763]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":44534763,"title":"A Cross-Format Framework for Consistent Information Integration among Molecular Pathways and Ontologies","internal_url":"https://www.academia.edu/44534763/A_Cross_Format_Framework_for_Consistent_Information_Integration_among_Molecular_Pathways_and_Ontologies","owner_id":35764061,"coauthors_can_edit":true,"owner":{"id":35764061,"first_name":"Giuseppe","middle_initials":null,"last_name":"Nicosia","page_name":"GiuseppeNicosia","domain_name":"cambridge","created_at":"2015-10-06T11:52:44.191-07:00","display_name":"Giuseppe Nicosia","url":"https://cambridge.academia.edu/GiuseppeNicosia"},"attachments":[{"id":64981172,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64981172/thumbnails/1.jpg","file_name":"Nicosia_WCB10.pdf","download_url":"https://www.academia.edu/attachments/64981172/download_file","bulk_download_file_name":"A_Cross_Format_Framework_for_Consistent.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64981172/Nicosia_WCB10-libre.pdf?1605800224=\u0026response-content-disposition=attachment%3B+filename%3DA_Cross_Format_Framework_for_Consistent.pdf\u0026Expires=1740435971\u0026Signature=CjVvi~p3OFLShb1a7UTithR1tAqKq7LqW~WoqJaFefhTkF--ZRAYN30gaCVVIkOMHuGL6M9Os4Y5by4ZONTCIz3~3bB3ZW7kr3eWjsbghswzx5ScuygycEGl8wan1Lgl3r4slOnTVndIjVv5zDcS1NjXmpTr8~Tlv2fvp3rq~Hxl6SD5K8ObzkQ6UVroasXo-qR9Fup1cwSj9a4caK-003vItjIkrKTCq2VfSRjQEjhcIp7QdsTrZyrDtB5kCR9~fYShYs2p-MVoB90c7kIOplauCnOF6PJjInJ4DrQD-uRIegqnDH-kYif-057NfziD4MUmW2vhzgdEBVpsZ0Lpgw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534762"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534762/Real_Coded_Clonal_Selection_Algorithm_for_Unconstrained_Global_Optimization_using_a_Hybrid_Inversely_Proportional_Hypermutation_Operator"><img alt="Research paper thumbnail of Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hypermutation Operator" class="work-thumbnail" src="https://attachments.academia-assets.com/64981176/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534762/Real_Coded_Clonal_Selection_Algorithm_for_Unconstrained_Global_Optimization_using_a_Hybrid_Inversely_Proportional_Hypermutation_Operator">Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hypermutation Operator</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="752a9b4ad1ebde886b796a91a8235021" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981176,"asset_id":44534762,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981176/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534762"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534762"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534762; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534762]").text(description); $(".js-view-count[data-work-id=44534762]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534762; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534762']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "752a9b4ad1ebde886b796a91a8235021" } } $('.js-work-strip[data-work-id=44534762]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":44534762,"title":"Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hypermutation Operator","internal_url":"https://www.academia.edu/44534762/Real_Coded_Clonal_Selection_Algorithm_for_Unconstrained_Global_Optimization_using_a_Hybrid_Inversely_Proportional_Hypermutation_Operator","owner_id":35764061,"coauthors_can_edit":true,"owner":{"id":35764061,"first_name":"Giuseppe","middle_initials":null,"last_name":"Nicosia","page_name":"GiuseppeNicosia","domain_name":"cambridge","created_at":"2015-10-06T11:52:44.191-07:00","display_name":"Giuseppe Nicosia","url":"https://cambridge.academia.edu/GiuseppeNicosia"},"attachments":[{"id":64981176,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64981176/thumbnails/1.jpg","file_name":"Nicosia_SAC06_opt.pdf","download_url":"https://www.academia.edu/attachments/64981176/download_file","bulk_download_file_name":"Real_Coded_Clonal_Selection_Algorithm_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64981176/Nicosia_SAC06_opt-libre.pdf?1605800224=\u0026response-content-disposition=attachment%3B+filename%3DReal_Coded_Clonal_Selection_Algorithm_fo.pdf\u0026Expires=1740435971\u0026Signature=Fu0wAU0e2xPvRmEW1E-0T6DYkMsjwA~Jpge933UCodCiDvoUmp5e7M4w4qdud1LvWE2st6LkYwHaK6P8yZLJKFs2Q3iU0p-WTnEJfGXCP4~66SGaVSZk8E4OC0OoonG79ELWmN-PvdqtQrazc8T0SvBPGneuZLbFDiKvx6Ui5KRXX-9BvtWMhWCQT4wF2Wv-PWSFR3YHZKjMDbF1eYrBkh-9iUI9r6T-fkEuLRHeTabU~xOK5C4PZtjSkcPoL36AqgrX6Om5qdQQ1WkIwR5m6C8ONoIhczWs3twxTSfYmvSUj7wquk6EwoOsY0-JGlFFc6QgmeJvon1rSxiBRxxj-A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534756"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534756/Vol_596_urn_nbn_de_0074_596_3_ORES_2010_Ontology_Repositories_and_Editors_for_the_Semantic_Web_Proceedings_of_the_1st_Workshop_on_Ontology_Repositories_and_Editors_for_the_Semantic_Web_OREMP_Ontology_Reasoning_Engine_for_Molecular_Pathways"><img alt="Research paper thumbnail of Vol-596 urn:nbn:de:0074-596-3 ORES-2010 Ontology Repositories and Editors for the Semantic Web Proceedings of the 1st Workshop on Ontology Repositories and Editors for the Semantic Web OREMP: Ontology Reasoning Engine for Molecular Pathways" class="work-thumbnail" src="https://attachments.academia-assets.com/64981169/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534756/Vol_596_urn_nbn_de_0074_596_3_ORES_2010_Ontology_Repositories_and_Editors_for_the_Semantic_Web_Proceedings_of_the_1st_Workshop_on_Ontology_Repositories_and_Editors_for_the_Semantic_Web_OREMP_Ontology_Reasoning_Engine_for_Molecular_Pathways">Vol-596 urn:nbn:de:0074-596-3 ORES-2010 Ontology Repositories and Editors for the Semantic Web Proceedings of the 1st Workshop on Ontology Repositories and Editors for the Semantic Web OREMP: Ontology Reasoning Engine for Molecular Pathways</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The information about molecular processes is shared continuously in the form of runnable pathway ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The information about molecular processes is shared continuously in the form of runnable pathway collections, and biomedical ontologies provide a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Here we propose a strategy that integrates these resources by means of extended ontologies built on top of a common meta-format. Information sharing, integration and discovery are the primary features provided by the system; additionally, two current field applications of the system are reported.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="423a6569dd6f7e22814a13a4bfbb760a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981169,"asset_id":44534756,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981169/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534756"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534756"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534756; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534756]").text(description); $(".js-view-count[data-work-id=44534756]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534756; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534756']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "423a6569dd6f7e22814a13a4bfbb760a" } } $('.js-work-strip[data-work-id=44534756]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":44534756,"title":"Vol-596 urn:nbn:de:0074-596-3 ORES-2010 Ontology Repositories and Editors for the Semantic Web Proceedings of the 1st Workshop on Ontology Repositories and Editors for the Semantic Web OREMP: Ontology Reasoning Engine for Molecular Pathways","internal_url":"https://www.academia.edu/44534756/Vol_596_urn_nbn_de_0074_596_3_ORES_2010_Ontology_Repositories_and_Editors_for_the_Semantic_Web_Proceedings_of_the_1st_Workshop_on_Ontology_Repositories_and_Editors_for_the_Semantic_Web_OREMP_Ontology_Reasoning_Engine_for_Molecular_Pathways","owner_id":35764061,"coauthors_can_edit":true,"owner":{"id":35764061,"first_name":"Giuseppe","middle_initials":null,"last_name":"Nicosia","page_name":"GiuseppeNicosia","domain_name":"cambridge","created_at":"2015-10-06T11:52:44.191-07:00","display_name":"Giuseppe Nicosia","url":"https://cambridge.academia.edu/GiuseppeNicosia"},"attachments":[{"id":64981169,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64981169/thumbnails/1.jpg","file_name":"Nicosia_ESWC10.pdf","download_url":"https://www.academia.edu/attachments/64981169/download_file","bulk_download_file_name":"Vol_596_urn_nbn_de_0074_596_3_ORES_2010.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64981169/Nicosia_ESWC10-libre.pdf?1605800224=\u0026response-content-disposition=attachment%3B+filename%3DVol_596_urn_nbn_de_0074_596_3_ORES_2010.pdf\u0026Expires=1740435971\u0026Signature=AXjqH7uu7mQCIenIQ2bH5eb-EJdeI6Q1WG~AR-YThLBi7BuRol00S1KXCQIZgNvs3tYcT-Cp~-idpriwAfs2C0yTsIBpaIMY3aCBS93YcPaV-2j2bYFaAIxDEISx3dQ1vNfXT76FvKFNFVEAFUT3ptqSX77M6a7PkCpsRD9ZrfMoOEQiaqf~8gFeXnzD2SteM~x-lX9GZspzoAaJxFZESSXw83AY7MAy6XSvgIQALz4rN5LZHSIZXWzA1~CdYnQfUYg4Y7Mw~VtqcphzH9~8jznDCdDN4H3kdrTHd-xIPexD8DyX7Hon7ZifckQiQ5vGgS96dpCAEFDDZPGdC-R3pA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534761"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534761/A_Design_for_Yield_Algorithm_to_Assess_and_Improve_the_Structural_and_Energetic_Robustness_of_Proteins_and_Drugs"><img alt="Research paper thumbnail of A Design-for-Yield Algorithm to Assess and Improve the Structural and Energetic Robustness of Proteins and Drugs" class="work-thumbnail" src="https://attachments.academia-assets.com/64981186/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534761/A_Design_for_Yield_Algorithm_to_Assess_and_Improve_the_Structural_and_Energetic_Robustness_of_Proteins_and_Drugs">A Design-for-Yield Algorithm to Assess and Improve the Structural and Energetic Robustness of Proteins and Drugs</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Robustness is a property that pervades all aspects of nature. The ability of a system to adapt to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Robustness is a property that pervades all aspects of nature. The ability of a system to adapt to perturbations due to internal and external agents, aging, wear, or to environmental changes is one of the driving forces of evolution. At the molecular level, understanding the ro-bustness of a protein has a great impact on the in-silico design of polypep-tide chains and drugs. The chance of computationally checking the ability of a protein to preserve its structure in the native state may lead to the design of new compounds that can work in a living cell more effectively. Inspired by the well known robustness analysis framework used in Electronic Design Automation, we introduce a formal definition of robustness for proteins and a dimensionless quantity, called yield, to quantify the robustness of a protein. Then, we introduce a new robustness-centered protein design algorithm called Design-For-Yield. The aim of the algorithm is to discover new conformations with a specific functionality and high yield values. We present extensive characterizations of the robust-ness properties of many peptides, proteins, and drugs. Finally, we apply the DFY algorithm on the Crambin protein (1CRN) and on the Oxic-itin drug (DB00107). The obtained results confirm that the algorithm is able to discover a Crambin-like protein that is 23.61% more robust than the wild type. Concerning the Oxicitin drug a new protein sequence and the corresponding protein structure was discovered with an improved robustness of 3% at the global level.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bdb83414343a1ab28b1f100017b7c973" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981186,"asset_id":44534761,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981186/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534761"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534761"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534761; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534761]").text(description); $(".js-view-count[data-work-id=44534761]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534761; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534761']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534760"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534760/Generalized_Pattern_Search_and_Mesh_Adaptive_Direct_Search_Algorithms_for_Protein_Structure_Prediction"><img alt="Research paper thumbnail of Generalized Pattern Search and Mesh Adaptive Direct Search Algorithms for Protein Structure Prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/64981178/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534760/Generalized_Pattern_Search_and_Mesh_Adaptive_Direct_Search_Algorithms_for_Protein_Structure_Prediction">Generalized Pattern Search and Mesh Adaptive Direct Search Algorithms for Protein Structure Prediction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Proteins are the most important molecular entities of a living organism and understanding their f...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Proteins are the most important molecular entities of a living organism and understanding their functions is an important task to treat diseases and synthesize new drugs. It is largely known that the function of a protein is strictly related to its spatial conformation: to tackle this problem, we have proposed a new approach based on a class of pattern search algorithms that is largely used in optimization of real world applications. The obtained results are interesting in terms of the quality of the structures (RMSD-Cα) and energy values found.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="550559234b48b22a207a71d9c6b89793" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981178,"asset_id":44534760,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981178/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534760"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534760"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534760; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534759"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534759/Nominal_Yield_Area_Tradeoff_in_Automatic_Synthesis_of_Analog_Circuits_A_Genetic_Programming_Approach_using_Immune_Inspired_Operators"><img alt="Research paper thumbnail of Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators" class="work-thumbnail" src="https://attachments.academia-assets.com/64981182/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534759/Nominal_Yield_Area_Tradeoff_in_Automatic_Synthesis_of_Analog_Circuits_A_Genetic_Programming_Approach_using_Immune_Inspired_Operators">Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The synthesis of analog circuits is a complex and expensive task; whilst there are various approa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The synthesis of analog circuits is a complex and expensive task; whilst there are various approaches for the synthesis of digital circuits, analog design is intrinsically more difficult since analog circuits process voltages in a continuous range. In the field of analog circuit design, the genetic programming approach has received great attention, affording the possibility to design and optimize a circuit at the same time. However, these algorithms have limited industrial relevance, since they work with ideal components. Starting from the well known results of Koza and co-authors, we introduce a new evolutionary algorithm, called elitist Immune Programming (EIP), that is able to synthesize an analog circuit using industrial components series in order to produce reliable and low cost circuits. The algorithm has been used for the synthesis of low-pass filters; the results were compared with the genetic programming, and the analysis shows that EIP is able to design better circuits in terms of frequency response and number of components. In addition we conduct a complete yield analysis of the discovered circuits, and discover that EIP circuits attain a higher yield than the circuits generated via a genetic programming approach, and, in particular, the algorithm discovers a Pareto Front which respects nominal performance (sizing), number of components (area) and yield (robustness).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03c2af5e69821bdf983f59ed9130af53" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981182,"asset_id":44534759,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981182/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534759"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534759"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534759; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534752"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534752/Analysis_and_Optimization_of_C_3_Photosynthetic_Carbon_Metabolism"><img alt="Research paper thumbnail of Analysis and Optimization of C 3 Photosynthetic Carbon Metabolism" class="work-thumbnail" src="https://attachments.academia-assets.com/64981175/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534752/Analysis_and_Optimization_of_C_3_Photosynthetic_Carbon_Metabolism">Analysis and Optimization of C 3 Photosynthetic Carbon Metabolism</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://cambridge.academia.edu/GiuseppeNicosia">Giuseppe Nicosia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AlessioPapini">Alessio Papini</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have studied the C3 photosynthetic carbon metabolism centering our investigation on the follow...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have studied the C3 photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the C3 photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robust-ness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the 135% from 15.486 µmol m −2 s −1 to 36.382 µmol m −2 s −1 , and improving the previous best-found photosynthetic productivity value (27.261 µmol m −2 s −1 , 76% of enhancement). Optimized enzyme concentrations express a maximal local robustness (100%) and a high global robustness (97.2%), satisfactory properties for a possible "in vitro" manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the trade-off between the maximization of the leaf CO2 uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three c i concentrations referring to the estimate of CO2 concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aa8d4aed19da2e2175cc31e7e573826e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981175,"asset_id":44534752,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981175/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534752"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534752"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534752; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534758"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534758/Global_Optimization_for_Algebraic_Geometry_Computing_Runge_Kutta_Methods"><img alt="Research paper thumbnail of Global Optimization for Algebraic Geometry -Computing Runge-Kutta Methods" class="work-thumbnail" src="https://attachments.academia-assets.com/64981187/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534758/Global_Optimization_for_Algebraic_Geometry_Computing_Runge_Kutta_Methods">Global Optimization for Algebraic Geometry -Computing Runge-Kutta Methods</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This research work presents a new evolutionary optimization algorithm, EVO-RUNGE-KUTTA in theoret...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This research work presents a new evolutionary optimization algorithm, EVO-RUNGE-KUTTA in theoretical mathematics with applications in scientific computing. We illustrate the application of EVO-RUNGE-KUTTA, a two-phase optimization algorithm, to a problem of pure algebra, the study of the parameterization of an algebraic variety, an open problem in algebra. Results show the design and optimization of particular algebraic varieties, the Runge-Kutta methods of order q. The mapping between algebraic geometry and evolutionary optimization is direct, and we expect that many open problems in pure algebra will be modelled as constrained global optimization problems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4ec224accebd6267e8268ffaf82178b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981187,"asset_id":44534758,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981187/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534758"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534758"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534758; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534757"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534757/An_Advanced_Clonal_Selection_Algorithm_with_Ad_Hoc_Network_Based_Hypermutation_Operators_for_Synthesis_of_Topology_and_Sizing_of_Analog_Electrical_Circuits"><img alt="Research paper thumbnail of An Advanced Clonal Selection Algorithm with Ad-Hoc Network-Based Hypermutation Operators for Synthesis of Topology and Sizing of Analog Electrical Circuits" class="work-thumbnail" src="https://attachments.academia-assets.com/64981174/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534757/An_Advanced_Clonal_Selection_Algorithm_with_Ad_Hoc_Network_Based_Hypermutation_Operators_for_Synthesis_of_Topology_and_Sizing_of_Analog_Electrical_Circuits">An Advanced Clonal Selection Algorithm with Ad-Hoc Network-Based Hypermutation Operators for Synthesis of Topology and Sizing of Analog Electrical Circuits</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In electronics, there are two major classes of circuits, analog and digital electrical circuits. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In electronics, there are two major classes of circuits, analog and digital electrical circuits. While digital circuits use discrete voltage levels, analog circuits use a continuous range of voltage. The synthesis of analog circuits is known to be a complex optimization task, due to the continuous behaviour of the output and the lack of automatic design tools; actually, the design process is almost entirely demanded to the engineers. In this research work, we introduce a new clonal selection algorithm, the elitist Immune Programming, (eIP) which uses a new class of hypermu-tation operators and a network-based coding. The eIP algorithm is designed for the synthesis of topology and sizing of analog electrical circuits; in particular, it has been used for the design of passive filters. To assess the effectiveness of the designed algorithm, the obtained results have been compared with the passive filter discovered by Koza and co-authors using the Genetic Programming (GP) algorithm. The circuits obtained by eIP algorithm are better than the one found by GP in terms of frequency response and number of components required to build it.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3aef3ca73bbd4aca824b2d44f8f38ea3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981174,"asset_id":44534757,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981174/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534757"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534757"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534757; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534751"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534751/Robust_Parameter_Identification_for_Biological_Circuit_Calibration"><img alt="Research paper thumbnail of Robust Parameter Identification for Biological Circuit Calibration" class="work-thumbnail" src="https://attachments.academia-assets.com/64981193/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534751/Robust_Parameter_Identification_for_Biological_Circuit_Calibration">Robust Parameter Identification for Biological Circuit Calibration</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The aim of this work is to compare some deter-ministic optimization algorithms and evolutionary a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The aim of this work is to compare some deter-ministic optimization algorithms and evolutionary algorithms on parameter estimation in a biological circuit design problem: the negative feedback loop between the tumor suppressor pS3 and the oncogene Mdm2. We compared deterministic optimization algorithms and evolutionary algorithms in terms of robustness of the resulting parameters including all sources of uncertainty into the statistical representation of reference data and evaluating the obtained solutions in terms of confident limits. The experimental results obtained show as evolutionary algorithms are more robust with respect of deterministic optimization algorithms in particular the algorithm Differential Evolution (DE) showed the best performance over the minimization of the fitting function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7f878748cac12f86c360314777e36858" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981193,"asset_id":44534751,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981193/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534751"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534751"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534751; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534755"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534755/Analysis_of_an_Evolutionary_Algorithm_with_Hypermacromutation_and_Stop_at_First_Constructive_Mutation_Heuristic_for_Solving_Trap_Functions"><img alt="Research paper thumbnail of Analysis of an Evolutionary Algorithm with Hypermacromutation and Stop at First Constructive Mutation Heuristic for Solving Trap Functions" class="work-thumbnail" src="https://attachments.academia-assets.com/64981188/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534755/Analysis_of_an_Evolutionary_Algorithm_with_Hypermacromutation_and_Stop_at_First_Constructive_Mutation_Heuristic_for_Solving_Trap_Functions">Analysis of an Evolutionary Algorithm with Hypermacromutation and Stop at First Constructive Mutation Heuristic for Solving Trap Functions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary algorithm (EA) to optimize basic and complex trap functions. The designed evolutionary algorithm uses perturbation operators based on hypermacromutation and stop at first constructive mutation heuristic. The experimental and theoretical results show that the algorithm successfully achieves its goal in facing this computational problem. The low number of evaluations to solutions expected through the theoretical analysis of the EA have been fully confirmed by the experimental results. To our knowledge the designed EA is the state-of-art algorithm to face trap function problems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66783255610369c195413ddcf1b20dab" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981188,"asset_id":44534755,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981188/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534755"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534755"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534755; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534750"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534750/Pareto_epsilon_Dominance_and_Identifiable_Solutions_for_BioCAD_Modeling"><img alt="Research paper thumbnail of Pareto epsilon-Dominance and Identifiable Solutions for BioCAD Modeling" class="work-thumbnail" src="https://attachments.academia-assets.com/64981173/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534750/Pareto_epsilon_Dominance_and_Identifiable_Solutions_for_BioCAD_Modeling">Pareto epsilon-Dominance and Identifiable Solutions for BioCAD Modeling</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a framework to design metabolic pathways in which many objectives are optimized simult...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a framework to design metabolic pathways in which many objectives are optimized simultaneously. This allows to characterize the energy signature in models of algal and mitochondrial metabolism. The optimal design and assessment of the model is achieved through a multi-objective optimization technique driven by epsilon-dominance and iden-tifiability analysis. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance , regulating the granularity of the approximation of the Pareto front. Our framework is also suitable for black-box analysis, enabling to investigate and optimize any biological pathway modeled with ODEs, DAEs, FBA and GPR.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c3c511c00eae9ec8781a54af75b23e8a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981173,"asset_id":44534750,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981173/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534750"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534750"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534750; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534753"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534753/On_the_Convergence_of_Immune_Algorithms"><img alt="Research paper thumbnail of On the Convergence of Immune Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/64981180/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534753/On_the_Convergence_of_Immune_Algorithms">On the Convergence of Immune Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Immune Algorithms have been used widely and successfully in many computational intelligence areas...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Immune Algorithms have been used widely and successfully in many computational intelligence areas including optimization. Given the large number of variants of each operator of this class of algorithms, this paper presents a study of the convergence properties of Immune Algorithms in general, conducted by examining conditions which are sufficient to prove their convergence to the global optimum of an optimization problem. Furthermore problem independent upper bounds for the number of generations required to guarantee that the solution is found with a defined probability are derived in a similar manner as performed previously, in literature, for genetic algorithms. Again the independence of the function to be optimised leads to an upper bound which is not of practical interest, confirming the general idea that when deriving time bounds for Evolutionary Algorithms the problem class to be optimised needs to be considered.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1bb0abd2c22d320f688d7b48c0fb07c8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981180,"asset_id":44534753,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981180/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534753"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534753"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534753; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534754"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534754/Clonal_Selection_Algorithms_A_Comparative_Case_Study_Using_Effective_Mutation_Potentials"><img alt="Research paper thumbnail of Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials" class="work-thumbnail" src="https://attachments.academia-assets.com/64981177/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534754/Clonal_Selection_Algorithms_A_Comparative_Case_Study_Using_Effective_Mutation_Potentials">Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLON...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="edfba97c83a4798bf89732700c248ac8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981177,"asset_id":44534754,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981177/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534754"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534754"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534754; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534754]").text(description); $(".js-view-count[data-work-id=44534754]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534754; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534754']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534749"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534749/Multi_objective_Optimisation_Sensitivity_and_Robustness_Analysis_in_FBA_Modelling"><img alt="Research paper thumbnail of Multi-objective Optimisation, Sensitivity and Robustness Analysis in FBA Modelling" class="work-thumbnail" src="https://attachments.academia-assets.com/64981179/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534749/Multi_objective_Optimisation_Sensitivity_and_Robustness_Analysis_in_FBA_Modelling">Multi-objective Optimisation, Sensitivity and Robustness Analysis in FBA Modelling</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, we propose a computational framework to design in silico robust bacteria able to ov...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this work, we propose a computational framework to design in silico robust bacteria able to overproduce multiple metabolites. To this end, we search the optimal genetic manipulations, in terms of knockout , which also guarantee the growth of the organism. We introduce a multi-objective optimisation algorithm, called Genetic Design through Multi-Objective (GDMO), and test it in several organisms to maximise the production of key intermediate metabolites such as succinate and acetate. We obtain a vast set of Pareto optimal solutions; each of them represents an organism strain. For each solution, we evaluate the fragility by calculating three robustness indexes and by exploring reactions and metabolite interactions. Finally, we perform the Sensitivity Analysis of the metabolic model, which finds the inputs with the highest influence on the outputs of the model. We show that our methodology provides effective vision of the achievable synthetic strain landscape and a powerful design pipeline.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="07c587c87960f519fbdf65df429bd3a6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981179,"asset_id":44534749,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981179/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534749"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534749"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534749; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44534749]").text(description); $(".js-view-count[data-work-id=44534749]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44534749; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='44534749']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "07c587c87960f519fbdf65df429bd3a6" } } $('.js-work-strip[data-work-id=44534749]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":44534749,"title":"Multi-objective Optimisation, Sensitivity and Robustness Analysis in FBA Modelling","internal_url":"https://www.academia.edu/44534749/Multi_objective_Optimisation_Sensitivity_and_Robustness_Analysis_in_FBA_Modelling","owner_id":35764061,"coauthors_can_edit":true,"owner":{"id":35764061,"first_name":"Giuseppe","middle_initials":null,"last_name":"Nicosia","page_name":"GiuseppeNicosia","domain_name":"cambridge","created_at":"2015-10-06T11:52:44.191-07:00","display_name":"Giuseppe Nicosia","url":"https://cambridge.academia.edu/GiuseppeNicosia"},"attachments":[{"id":64981179,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64981179/thumbnails/1.jpg","file_name":"Nicosia_CMSB_2012.pdf","download_url":"https://www.academia.edu/attachments/64981179/download_file","bulk_download_file_name":"Multi_objective_Optimisation_Sensitivity.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64981179/Nicosia_CMSB_2012-libre.pdf?1605800267=\u0026response-content-disposition=attachment%3B+filename%3DMulti_objective_Optimisation_Sensitivity.pdf\u0026Expires=1740435971\u0026Signature=WKasVsCTlVYuiC5jo-sLZc7yUHYetgupEsrzWwMzJ1UXwILzrkcwPWhmGpZyJ1olaUlLvQFx4KvHPSHrOCXtQFsU69UmBHAHQHuQp2XnjfIbHUztOxQp77sKoydeTQ02IT6odfjE5qdmsuTouge5RuNZfjw2i1ajGEehhq0Jjh4J-rPG8dmq23b-WMF-bROZxN8aLMAZ9XDAGB1rfcBJ2N~8A9gz~n-T5h3JkfZBQitsZn81PiocuTy1FihwxuiP-awaWehiRfOnupv7~-kk3OccruMgMhdW2Iz7nuGIHWJ6JnXb9Vvbw5K91FeFHXLBg1xaHgHQYZUCHNXP4Y5ysw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="44534748"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/44534748/Detecting_Constituent_Sequences_by_Means_of_HP_Pattern_Based_Grammars_to_Synthesize_Proteins_Inferring_Sequence_Structure_Function_Relationship"><img alt="Research paper thumbnail of Detecting Constituent Sequences by Means of HP Pattern-Based Grammars to Synthesize Proteins: Inferring Sequence-Structure-Function Relationship" class="work-thumbnail" src="https://attachments.academia-assets.com/64981184/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/44534748/Detecting_Constituent_Sequences_by_Means_of_HP_Pattern_Based_Grammars_to_Synthesize_Proteins_Inferring_Sequence_Structure_Function_Relationship">Detecting Constituent Sequences by Means of HP Pattern-Based Grammars to Synthesize Proteins: Inferring Sequence-Structure-Function Relationship</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The detection of protein characters that could reveal how protein chains are constituted, is an i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The detection of protein characters that could reveal how protein chains are constituted, is an important step to understand the main functions of specific classes of proteins. We made use of the concept of "HP Pattern-Based" grammars to study the connection between protein chains and protein functions. In order to consider the structure of the proteins the HP models were used. Amino acid sequences were treated as a formal language, and it was built a set of HP Pattern-Based grammars to describe this language by means the Teiresias pattern discovery tool. First, this methodology was tested on the class of Antimi-crobial peptides (AmPs). The deduced derivation rules of HP Pattern-Based Grammars were validated by the regular grammar designed by [11] which was used to create new, unnatural, AmPs sequences. Then, our approach was applied to characterize a function of the Pleckstrin Homo-logy domain(PH Domain) which represents an important three dimensional domain which bind to phosphoinositides. Nowadays, interactions among PH domain amino acids and inositol phosphate are not well characterized. For the first time, by means of an HP Pattern-Based grammar, we highlight that this binding function can be described in terms of hydrophocity patterns. Our approach points out some fundamental aspects regarding the relationship between sequence, structure and function of proteins.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="385fc353868b5ca98ec537ca3ddb3ab4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64981184,"asset_id":44534748,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64981184/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="44534748"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44534748"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44534748; 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