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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14130">arXiv:2411.14130</a> <span> [<a href="https://arxiv.org/pdf/2411.14130">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> Evidence of epigenetic oncogenesis: a turning point in cancer research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Capp%2C+J">Jean-Pascal Capp</a>, <a href="/search/q-bio?searchtype=author&query=Aliaga%2C+B">Beno卯t Aliaga</a>, <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.14130v1-abstract-short" style="display: inline;"> In cancer research, the term epigenetics was used in the 1970s in its modern sense encompassing non-genetic events modifying the chromatin state, mainly to oppose the emerging oncogene paradigm. However, starting from the establishment of this prominent concept, the importance of these epigenetic phenomena in cancer rarely led to questioning the causal role of genetic alterations. Only in the last… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14130v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14130v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14130v1-abstract-full" style="display: none;"> In cancer research, the term epigenetics was used in the 1970s in its modern sense encompassing non-genetic events modifying the chromatin state, mainly to oppose the emerging oncogene paradigm. However, starting from the establishment of this prominent concept, the importance of these epigenetic phenomena in cancer rarely led to questioning the causal role of genetic alterations. Only in the last 10 years, the accumulation of problematic data, better experimental technologies, and some ambitious models pushed the idea that epigenetics could be at least as important as genetics in early oncogenesis. Until this year, a direct demonstration of epigenetic oncogenesis was still lacking. Now Parreno, Cavalli and colleagues, using a refined experimental model in the fruit fly Drosophila melanogaster, enforced the initiation of tumours solely by imposing a transient loss of Polycomb repression, leading to a purely epigenetic oncogenesis phenomenon. Despite a few caveats that we discuss, this pioneering work represents a major breakpoint in cancer research that leads us to consider the theoretical and conceptual implications on oncogenesis and to search for links between this artificial experimental model and naturally occurring processes, while revisiting cancer theories that were previously proposed as alternatives to the oncogene-centered paradigm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14130v1-abstract-full').style.display = 'none'; document.getElementById('2411.14130v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.07252">arXiv:2410.07252</a> <span> [<a href="https://arxiv.org/pdf/2410.07252">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> Multilayer network approaches to omics data integration in Digital Twins for cancer research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chenel%2C+H">Hugo Chenel</a>, <a href="/search/q-bio?searchtype=author&query=Marku%2C+M">Malvina Marku</a>, <a href="/search/q-bio?searchtype=author&query=James%2C+T">Tim James</a>, <a href="/search/q-bio?searchtype=author&query=Zinovyev%2C+A">Andrei Zinovyev</a>, <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.07252v1-abstract-short" style="display: inline;"> This review examines current and potential applications of DTs in healthcare, focusing on the integration of multi-omics data using multilayer network approaches in cancer research. We discuss methodologies, tools and platforms commonly used for this integration, while highlighting case studies, challenges, and research gaps. Finally, we advocate the need for incorporating diverse data types to pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07252v1-abstract-full').style.display = 'inline'; document.getElementById('2410.07252v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.07252v1-abstract-full" style="display: none;"> This review examines current and potential applications of DTs in healthcare, focusing on the integration of multi-omics data using multilayer network approaches in cancer research. We discuss methodologies, tools and platforms commonly used for this integration, while highlighting case studies, challenges, and research gaps. Finally, we advocate the need for incorporating diverse data types to produce more effective DTs for personalization of cancer treatments and in silico clinical trials. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07252v1-abstract-full').style.display = 'none'; document.getElementById('2410.07252v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.08542">arXiv:2210.08542</a> <span> [<a href="https://arxiv.org/pdf/2210.08542">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> </div> <p class="title is-5 mathjax"> From time-series transcriptomics to gene regulatory networks: a review on inference methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Marku%2C+M">Malvina Marku</a>, <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.08542v2-abstract-short" style="display: inline;"> Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the always increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08542v2-abstract-full').style.display = 'inline'; document.getElementById('2210.08542v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.08542v2-abstract-full" style="display: none;"> Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the always increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated overview of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims and experimental data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08542v2-abstract-full').style.display = 'none'; document.getElementById('2210.08542v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.10005">arXiv:1901.10005</a> <span> [<a href="https://arxiv.org/pdf/1901.10005">pdf</a>, <a href="https://arxiv.org/format/1901.10005">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1371/journal.pone.0231059">10.1371/journal.pone.0231059 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Unveiling new disease, pathway, and gene associations via multi-scale neural networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gaudelet%2C+T">Thomas Gaudelet</a>, <a href="/search/q-bio?searchtype=author&query=Malod-Dognin%2C+N">Noel Malod-Dognin</a>, <a href="/search/q-bio?searchtype=author&query=Sanchez-Valle%2C+J">Jon Sanchez-Valle</a>, <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a>, <a href="/search/q-bio?searchtype=author&query=Valencia%2C+A">Alfonso Valencia</a>, <a href="/search/q-bio?searchtype=author&query=Przulj%2C+N">Natasa Przulj</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.10005v3-abstract-short" style="display: inline;"> Diseases involve complex processes and modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, biological knowledge pertaining to a disease can be derived from a patient cell's profile, improving our diagnosis ability, as well as our grasp of disease risks. This knowledge can be used for drug re-purposi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.10005v3-abstract-full').style.display = 'inline'; document.getElementById('1901.10005v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.10005v3-abstract-full" style="display: none;"> Diseases involve complex processes and modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, biological knowledge pertaining to a disease can be derived from a patient cell's profile, improving our diagnosis ability, as well as our grasp of disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk. Here, we look at differential gene expression obtained from microarray technology for patients diagnosed with various diseases. Based on this data and cellular multi-scale organization, we aim to uncover disease--disease links, as well as disease-gene and disease--pathways associations. We propose neural networks with structures inspired by the multi-scale organization of a cell. We show that these models are able to correctly predict the diagnosis for the majority of the patients. Through the analysis of the trained models, we predict and validate disease-disease, disease-pathway, and disease-gene associations with comparisons to known interactions and literature search, proposing putative explanations for the novel predictions that come from our study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.10005v3-abstract-full').style.display = 'none'; document.getElementById('1901.10005v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> PLOS ONE, 15(4), p.e0231059 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1512.00268">arXiv:1512.00268</a> <span> [<a href="https://arxiv.org/pdf/1512.00268">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a>, <a href="/search/q-bio?searchtype=author&query=Carrillo-de-Santa-Pau%2C+E">Enrique Carrillo-de-Santa-Pau</a>, <a href="/search/q-bio?searchtype=author&query=Javierre%2C+B+M">Biola Maria Javierre</a>, <a href="/search/q-bio?searchtype=author&query=Juan%2C+D">David Juan</a>, <a href="/search/q-bio?searchtype=author&query=Fraser%2C+P">Peter Fraser</a>, <a href="/search/q-bio?searchtype=author&query=Spivakov%2C+M">Mikhail Spivakov</a>, <a href="/search/q-bio?searchtype=author&query=Valencia%2C+A">Alfonso Valencia</a>, <a href="/search/q-bio?searchtype=author&query=Rico%2C+D">Daniel Rico</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1512.00268v4-abstract-short" style="display: inline;"> Network analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1512.00268v4-abstract-full').style.display = 'inline'; document.getElementById('1512.00268v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1512.00268v4-abstract-full" style="display: none;"> Network analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromatin marks mediate genomic contacts. We use high-resolution Promoter Capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse embryonic stem cells to investigate promoter-centered chromatin interaction networks and calculate the presence of specific epigenomic features in the chromatin fragments constituting the nodes of the network. We estimate the association of these features to the topology of four chromatin interaction networks and identify features localized in connected areas of the network. Polycomb Group proteins and associated histone marks are the features with the highest chromatin assortativity in promoter-centred networks. We then ask which features distinguish contacts amongst promoters from contacts between promoters and other genomic elements. We observe higher chromatin assortativity of the actively elongating form of RNA Polymerase 2 (RNAPII) compared to inactive forms only in interactions between promoters and other elements. Contacts among promoters, and between promoters and other elements have different characteristic epigenomic features. We identify a possible role for the elongating form of RNAPII in mediating interactions among promoters, enhancers and transcribed gene bodies. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing the comparison of chromatin interaction networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1512.00268v4-abstract-full').style.display = 'none'; document.getElementById('1512.00268v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 May, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 December, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Expanded analyses, supplementary information now at Figshare (https://figshare.com/s/c331ded300fefd8f3ec0), abstract changed</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1309.3772">arXiv:1309.3772</a> <span> [<a href="https://arxiv.org/pdf/1309.3772">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1186/s12862-015-0298-0">10.1186/s12862-015-0298-0 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Boolean Gene Regulatory Model of heterosis and speciation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Emmrich%2C+P+M+F">Peter M. F. Emmrich</a>, <a href="/search/q-bio?searchtype=author&query=Roberts%2C+H+E">Hannah E. Roberts</a>, <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1309.3772v3-abstract-short" style="display: inline;"> Modelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress res… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1309.3772v3-abstract-full').style.display = 'inline'; document.getElementById('1309.3772v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1309.3772v3-abstract-full" style="display: none;"> Modelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress resistance. Heterosis has become a very useful tool in global agriculture, but its genetic basis remains controversial and its effects hard to predict. We have taken a computational approach to studying heterosis, developing a simulation of evolution, independent reassortment of alleles and hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework. Fitness is measured as the ability of a network to respond to external inputs in a pre-defined way. Our model reproduced common experimental observations on heterosis using only biologically justified parameters. Hybrid vigor was observed and its extent was seen to increase as parental populations diverged, up until a point of sudden collapse of hybrid fitness. We also reproduce, for the first time in a model, the fact that hybrid vigor cannot easily be fixed by within a breeding line, currently an important limitation of the use of hybrid crops. The simulation allowed us to study the effects of three standard models for the genetic basis of heterosis and the level of detail in our model allows us to suggest possible warning signs of the impending collapse of hybrid vigor in breeding. In addition, the simulation provides a framework that can be extended to study other aspects of heterosis and alternative evolutionary scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1309.3772v3-abstract-full').style.display = 'none'; document.getElementById('1309.3772v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 March, 2015; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 September, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">See online version for supplementary material</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> BMC Evolutionary Biology 2015, 15:24 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1303.4981">arXiv:1303.4981</a> <span> [<a href="https://arxiv.org/pdf/1303.4981">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1039/C3MB25548D">10.1039/C3MB25548D <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Stress induces remodelling of yeast interaction and co-expression networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lehtinen%2C+S">Sonja Lehtinen</a>, <a href="/search/q-bio?searchtype=author&query=Marsellach%2C+F+X">Francesc Xavier Marsellach</a>, <a href="/search/q-bio?searchtype=author&query=Codlin%2C+S">Sandra Codlin</a>, <a href="/search/q-bio?searchtype=author&query=Schmidt%2C+A">Alexander Schmidt</a>, <a href="/search/q-bio?searchtype=author&query=Cl%C3%A9ment-Ziza%2C+M">Mathieu Cl茅ment-Ziza</a>, <a href="/search/q-bio?searchtype=author&query=Beyer%2C+A">Andreas Beyer</a>, <a href="/search/q-bio?searchtype=author&query=B%C3%A4hler%2C+J">J眉rg B盲hler</a>, <a href="/search/q-bio?searchtype=author&query=Orengo%2C+C">Christine Orengo</a>, <a href="/search/q-bio?searchtype=author&query=Pancaldi%2C+V">Vera Pancaldi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1303.4981v1-abstract-short" style="display: inline;"> Network analysis provides a powerful framework for the interpretation of genome-wide data. While static network approaches have proved fruitful, there is increasing interest in the insights gained from the analysis of cellular networks under different conditions. In this work, we study the effect of stress on cellular networks in fission yeast. Stress elicits a sophisticated and large scale cellul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1303.4981v1-abstract-full').style.display = 'inline'; document.getElementById('1303.4981v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1303.4981v1-abstract-full" style="display: none;"> Network analysis provides a powerful framework for the interpretation of genome-wide data. While static network approaches have proved fruitful, there is increasing interest in the insights gained from the analysis of cellular networks under different conditions. In this work, we study the effect of stress on cellular networks in fission yeast. Stress elicits a sophisticated and large scale cellular response, involving a shift of resources from cell growth and metabolism towards protection and maintenance. Previous work has suggested that these changes can be appreciated at the network level. In this paper, we study two types of cellular networks: gene co-regulation networks and weighted protein interaction networks. We show that in response to oxidative stress, the co-regulation networks re-organize towards a more modularised structure: while sets of genes become more tightly co-regulated, co-regulation between these modules is decreased. This shift translates into longer average shortest path length, increased transitivity, and decreased modular overlap in these networks. We also find a similar change in structure in the weighted protein interaction network in response to both oxidative stress and nitrogen starvation, confirming and extending previous findings. These changes in network structure could represent an increase in network robustness and/or the emergence of more specialised functional modules. Additionally, we find stress induces tighter co-regulation of non-coding RNAs, decreased functional importance of splicing factors, as well as changes in the centrality of genes involved in chromatin organization, cytoskeleton organization, cell division, and protein turnover. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1303.4981v1-abstract-full').style.display = 'none'; document.getElementById('1303.4981v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 7 figures + supplementary materials, Molecular Biosystems, Received 30 Nov 2012, Accepted 27 Feb 2013</span> 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