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class="label">Followers</p><p class="data">6</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">5</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">5</p></div></a><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="35487604" href="https://www.academia.edu/Documents/in/Embodied_and_Enactive_Cognition"><div 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data-props="{"color":"gray","children":["Human-Robot Interaction"]}" data-trace="false" data-dom-id="Pill-react-component-b0ad0520-098a-4ed5-a124-caa1ed0b4202"></div> <div id="Pill-react-component-b0ad0520-098a-4ed5-a124-caa1ed0b4202"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="35487604" href="https://www.academia.edu/Documents/in/Information_Theory"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Information Theory"]}" data-trace="false" data-dom-id="Pill-react-component-c0406cbc-a17c-43a9-946f-536f8994b7ed"></div> <div id="Pill-react-component-c0406cbc-a17c-43a9-946f-536f8994b7ed"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="35487604" href="https://www.academia.edu/Documents/in/Language_Acquisition"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Language Acquisition"]}" data-trace="false" data-dom-id="Pill-react-component-6bef011d-2516-4164-bbbb-e590afc80984"></div> <div id="Pill-react-component-6bef011d-2516-4164-bbbb-e590afc80984"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="35487604" href="https://www.academia.edu/Documents/in/Algebra"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Algebra"]}" data-trace="false" data-dom-id="Pill-react-component-6369d692-003a-48d5-be5c-4f0baa60a922"></div> <div id="Pill-react-component-6369d692-003a-48d5-be5c-4f0baa60a922"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div 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 J. Knabe</h3></div><div class="js-work-strip profile--work_container" data-work-id="16097946"><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/16097946/Regulation_of_gene_regulation_smooth_binding_with_dynamic_affinity_affects_evolvability"><img alt="Research paper thumbnail of Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability" class="work-thumbnail" src="https://attachments.academia-assets.com/42749944/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/16097946/Regulation_of_gene_regulation_smooth_binding_with_dynamic_affinity_affects_evolvability">Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability</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://independent.academia.edu/MariaSchilstra">Maria J Schilstra</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JKnabe">J. Knabe</a></span></div><div class="wp-workCard_item"><span>2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a378405e499219e2a931a4b25d803538" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42749944,"asset_id":16097946,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42749944/download_file?st=MTczMjQ0NDY2MSw4LjIyMi4yMDguMTQ2&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="16097946"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16097946"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16097946; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16097946]").text(description); $(".js-view-count[data-work-id=16097946]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16097946; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16097946']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16097946, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "a378405e499219e2a931a4b25d803538" } } $('.js-work-strip[data-work-id=16097946]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16097946,"title":"Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability","translated_title":"","metadata":{"grobid_abstract":"Understanding the evolvability of simple differentiating multicellular systems is a fundamental problem in the biology of genetic regulatory networks and in computational applications inspired by the metaphor of growing and developing networks of cells. 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Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6138fa4872bd6771e439e519ffceb42a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527799,"asset_id":16317001,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527799/download_file?st=MTczMjQ0NDY2MSw4LjIyMi4yMDguMTQ2&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="16317001"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16317001"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16317001; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16317001]").text(description); $(".js-view-count[data-work-id=16317001]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16317001; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16317001']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16317001, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "6138fa4872bd6771e439e519ffceb42a" } } $('.js-work-strip[data-work-id=16317001]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16317001,"title":"Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)"},"translated_abstract":null,"internal_url":"https://www.academia.edu/16317001/Regulation_of_gene_regulation_smooth_binding_with_dynamic_affinity_affects_evolvability","translated_internal_url":"","created_at":"2015-09-30T03:05:27.665-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35425023,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":6458907,"work_id":16317001,"tagging_user_id":35425023,"tagged_user_id":35487604,"co_author_invite_id":1436636,"email":"j***e@herts.ac.uk","display_order":0,"name":"J. 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Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>2007 IEEE Symposium on Artificial Life</span><span>, 2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="290c764c401db369c3cb9cae0db4b6ae" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527810,"asset_id":16316988,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527810/download_file?st=MTczMjQ0NDY2MSw4LjIyMi4yMDguMTQ2&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="16316988"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316988"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316988; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16316988]").text(description); $(".js-view-count[data-work-id=16316988]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16316988; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16316988']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16316988, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "290c764c401db369c3cb9cae0db4b6ae" } } $('.js-work-strip[data-work-id=16316988]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16316988,"title":"The Essential Motif that wasn't there: Topological and Lesioning Analysis of Evolved Genetic Regulatory Networks","translated_title":"","metadata":{"grobid_abstract":"Networks that abstractly model natural Genetic Regulatory Networks (GRNs) are evolved to show a range of dynamical behaviors. Specifically one group was evolved to show differentiation, i.e. to be able to perform an additional behavior as compared to the original, single target behavior. These GRNs are then analyzed and compared with measures used in the biological sciences. Having huge numbers of GRNs available for not only analysis but also \"metabolic\" inspection, we find that evolutionary niches (target functions) do not necessarily mold network structure uniquely. Our results suggest that variability operators can have a stronger influence on network topologies than selection pressures, especially when many topologies can create similar dynamics. Furthermore, damaging the most significantly represented motif (whether in differentiating or non-differentiating GRNs) is found not to have a significantly bigger impact on function than random lesions, suggesting that particular motifs are not as important in the robust functioning of networks as might perhaps be expected.","publication_date":{"day":null,"month":null,"year":2007,"errors":{}},"publication_name":"2007 IEEE Symposium on Artificial Life","grobid_abstract_attachment_id":42527810},"translated_abstract":null,"internal_url":"https://www.academia.edu/16316988/The_Essential_Motif_that_wasnt_there_Topological_and_Lesioning_Analysis_of_Evolved_Genetic_Regulatory_Networks","translated_internal_url":"","created_at":"2015-09-30T03:05:25.099-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35425023,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":6458908,"work_id":16316988,"tagging_user_id":35425023,"tagged_user_id":35487604,"co_author_invite_id":1436636,"email":"j***e@herts.ac.uk","display_order":0,"name":"J. 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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="16316987"><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/16316987/Genetic_algorithms_and_their_application_to_in_silico_evolution_of_genetic_regulatory_networks"><img alt="Research paper thumbnail of Genetic algorithms and their application to in silico evolution of genetic regulatory networks" class="work-thumbnail" src="https://attachments.academia-assets.com/42527827/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/16316987/Genetic_algorithms_and_their_application_to_in_silico_evolution_of_genetic_regulatory_networks">Genetic algorithms and their application to in silico evolution of genetic regulatory networks</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://dhbw-stuttgart.academia.edu/KWegner">K. Wegner</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JKnabe">J. Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://dhbw-stuttgart.academia.edu/KatjaWengler">Katja Wengler</a></span></div><div class="wp-workCard_item"><span>Methods in molecular biology (Clifton, N.J.)</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution 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">A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through &quot;mutation&quot; and &quot;recombination&quot; in a &quot;population&quot; of possible solutions to a problem, encoded as strings of characters in &quot;genomes,&quot; and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the &quot;fitter&quot; individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to fin...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c51b302e619e47194c113c36309e8b5f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527827,"asset_id":16316987,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527827/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&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="16316987"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316987"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316987; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16316987]").text(description); $(".js-view-count[data-work-id=16316987]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16316987; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16316987']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16316987, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "c51b302e619e47194c113c36309e8b5f" } } $('.js-work-strip[data-work-id=16316987]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16316987,"title":"Genetic algorithms and their application to in silico evolution of genetic regulatory networks","translated_title":"","metadata":{"abstract":"A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. 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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="16316955"><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/16316955/Do_motifs_reflect_evolved_function_No_convergent_evolution_of_genetic_regulatory_network_subgraph_topologies"><img alt="Research paper thumbnail of Do motifs reflect evolved function?—No convergent evolution of genetic regulatory network subgraph topologies" class="work-thumbnail" src="https://attachments.academia-assets.com/42527855/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/16316955/Do_motifs_reflect_evolved_function_No_convergent_evolution_of_genetic_regulatory_network_subgraph_topologies">Do motifs reflect evolved function?—No convergent evolution of genetic regulatory network subgraph topologies</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://independent.academia.edu/JKnabe">J. Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>Biosystems</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f79de2a93e8309bf9345c9157493425c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527855,"asset_id":16316955,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527855/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&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="16316955"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316955"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316955; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16316955]").text(description); $(".js-view-count[data-work-id=16316955]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16316955; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16316955']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16316955, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "f79de2a93e8309bf9345c9157493425c" } } $('.js-work-strip[data-work-id=16316955]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16316955,"title":"Do motifs reflect evolved function?—No convergent evolution of genetic regulatory network subgraph topologies","translated_title":"","metadata":{"grobid_abstract":"Methods that analyse the topological structure of networks have recently become quite popular. Whether motifs (subgraph patterns that occur more often than in randomized networks) have specific functions as elementary computational circuits has been cause for debate. 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Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>Proceedings of the …</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as activ...</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 study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b99cdacf8e76310fe828e654d65f144a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527839,"asset_id":16316910,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527839/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&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="16316910"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316910"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316910; 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Knabe</a></span></div><div class="wp-workCard_item"><span>2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a378405e499219e2a931a4b25d803538" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42749944,"asset_id":16097946,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42749944/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ0NDY2MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16097946"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16097946"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16097946; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16097946]").text(description); $(".js-view-count[data-work-id=16097946]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16097946; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16097946']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16097946, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "a378405e499219e2a931a4b25d803538" } } $('.js-work-strip[data-work-id=16097946]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16097946,"title":"Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability","translated_title":"","metadata":{"grobid_abstract":"Understanding the evolvability of simple differentiating multicellular systems is a fundamental problem in the biology of genetic regulatory networks and in computational applications inspired by the metaphor of growing and developing networks of cells. We compare the evolvability of a static network model to a more realistic regulatory model with dynamic structure. In the former model, each regulatory protein-binding site is always influenced by exactly one gene product. In the latter model, binding is only more likely to occur the better the match between site and gene product is (smooth binding) and, in addition, affinity dynamically changes under the action of specificity factors during a cell's lifetime. On evolutionary timescales, this means that often the strength of influences between nodes is perturbed instead of direct changes being made to network connectivity. A main result is that for evolutionary search spaces of increasing sizes evolved performance drops much more strongly in the classical network model as compared to the smooth binding model. 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Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6138fa4872bd6771e439e519ffceb42a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527799,"asset_id":16317001,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527799/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ0NDY2MSw4LjIyMi4yMDguMTQ2&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="16317001"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16317001"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16317001; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16317001]").text(description); $(".js-view-count[data-work-id=16317001]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16317001; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16317001']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16317001, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "6138fa4872bd6771e439e519ffceb42a" } } $('.js-work-strip[data-work-id=16317001]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16317001,"title":"Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)"},"translated_abstract":null,"internal_url":"https://www.academia.edu/16317001/Regulation_of_gene_regulation_smooth_binding_with_dynamic_affinity_affects_evolvability","translated_internal_url":"","created_at":"2015-09-30T03:05:27.665-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35425023,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":6458907,"work_id":16317001,"tagging_user_id":35425023,"tagged_user_id":35487604,"co_author_invite_id":1436636,"email":"j***e@herts.ac.uk","display_order":0,"name":"J. 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Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>2007 IEEE Symposium on Artificial Life</span><span>, 2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="290c764c401db369c3cb9cae0db4b6ae" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527810,"asset_id":16316988,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527810/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ0NDY2MSw4LjIyMi4yMDguMTQ2&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="16316988"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316988"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316988; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16316988]").text(description); $(".js-view-count[data-work-id=16316988]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16316988; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16316988']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16316988, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "290c764c401db369c3cb9cae0db4b6ae" } } $('.js-work-strip[data-work-id=16316988]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16316988,"title":"The Essential Motif that wasn't there: Topological and Lesioning Analysis of Evolved Genetic Regulatory Networks","translated_title":"","metadata":{"grobid_abstract":"Networks that abstractly model natural Genetic Regulatory Networks (GRNs) are evolved to show a range of dynamical behaviors. Specifically one group was evolved to show differentiation, i.e. to be able to perform an additional behavior as compared to the original, single target behavior. These GRNs are then analyzed and compared with measures used in the biological sciences. Having huge numbers of GRNs available for not only analysis but also \"metabolic\" inspection, we find that evolutionary niches (target functions) do not necessarily mold network structure uniquely. Our results suggest that variability operators can have a stronger influence on network topologies than selection pressures, especially when many topologies can create similar dynamics. Furthermore, damaging the most significantly represented motif (whether in differentiating or non-differentiating GRNs) is found not to have a significantly bigger impact on function than random lesions, suggesting that particular motifs are not as important in the robust functioning of networks as might perhaps be expected.","publication_date":{"day":null,"month":null,"year":2007,"errors":{}},"publication_name":"2007 IEEE Symposium on Artificial Life","grobid_abstract_attachment_id":42527810},"translated_abstract":null,"internal_url":"https://www.academia.edu/16316988/The_Essential_Motif_that_wasnt_there_Topological_and_Lesioning_Analysis_of_Evolved_Genetic_Regulatory_Networks","translated_internal_url":"","created_at":"2015-09-30T03:05:25.099-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35425023,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":6458908,"work_id":16316988,"tagging_user_id":35425023,"tagged_user_id":35487604,"co_author_invite_id":1436636,"email":"j***e@herts.ac.uk","display_order":0,"name":"J. 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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="16316987"><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/16316987/Genetic_algorithms_and_their_application_to_in_silico_evolution_of_genetic_regulatory_networks"><img alt="Research paper thumbnail of Genetic algorithms and their application to in silico evolution of genetic regulatory networks" class="work-thumbnail" src="https://attachments.academia-assets.com/42527827/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/16316987/Genetic_algorithms_and_their_application_to_in_silico_evolution_of_genetic_regulatory_networks">Genetic algorithms and their application to in silico evolution of genetic regulatory networks</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://dhbw-stuttgart.academia.edu/KWegner">K. Wegner</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JKnabe">J. Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://dhbw-stuttgart.academia.edu/KatjaWengler">Katja Wengler</a></span></div><div class="wp-workCard_item"><span>Methods in molecular biology (Clifton, N.J.)</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution 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">A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through &quot;mutation&quot; and &quot;recombination&quot; in a &quot;population&quot; of possible solutions to a problem, encoded as strings of characters in &quot;genomes,&quot; and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the &quot;fitter&quot; individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to fin...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c51b302e619e47194c113c36309e8b5f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527827,"asset_id":16316987,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527827/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&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="16316987"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316987"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316987; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16316987]").text(description); $(".js-view-count[data-work-id=16316987]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16316987; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16316987']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16316987, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "c51b302e619e47194c113c36309e8b5f" } } $('.js-work-strip[data-work-id=16316987]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16316987,"title":"Genetic algorithms and their application to in silico evolution of genetic regulatory networks","translated_title":"","metadata":{"abstract":"A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. 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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="16316955"><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/16316955/Do_motifs_reflect_evolved_function_No_convergent_evolution_of_genetic_regulatory_network_subgraph_topologies"><img alt="Research paper thumbnail of Do motifs reflect evolved function?—No convergent evolution of genetic regulatory network subgraph topologies" class="work-thumbnail" src="https://attachments.academia-assets.com/42527855/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/16316955/Do_motifs_reflect_evolved_function_No_convergent_evolution_of_genetic_regulatory_network_subgraph_topologies">Do motifs reflect evolved function?—No convergent evolution of genetic regulatory network subgraph topologies</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://independent.academia.edu/JKnabe">J. Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>Biosystems</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f79de2a93e8309bf9345c9157493425c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527855,"asset_id":16316955,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527855/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&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="16316955"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316955"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316955; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16316955]").text(description); $(".js-view-count[data-work-id=16316955]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16316955; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16316955']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16316955, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "f79de2a93e8309bf9345c9157493425c" } } $('.js-work-strip[data-work-id=16316955]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16316955,"title":"Do motifs reflect evolved function?—No convergent evolution of genetic regulatory network subgraph topologies","translated_title":"","metadata":{"grobid_abstract":"Methods that analyse the topological structure of networks have recently become quite popular. Whether motifs (subgraph patterns that occur more often than in randomized networks) have specific functions as elementary computational circuits has been cause for debate. 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Knabe</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://herts.academia.edu/MariaSchilstra">Maria Schilstra</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://majapasovic.academia.edu/ChrystopherNehaniv">Chrystopher L Nehaniv</a></span></div><div class="wp-workCard_item"><span>Proceedings of the …</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as activ...</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 study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b99cdacf8e76310fe828e654d65f144a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42527839,"asset_id":16316910,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42527839/download_file?st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ0NDY2Miw4LjIyMi4yMDguMTQ2&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="16316910"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16316910"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16316910; 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