Analysis of Combinatorial Regulation: Scaling of Partnerships between Regulators with the Number of Governed Targets
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{"title"=>"Analysis of combinatorial regulation: Scaling of partnerships between regulators with the number of governed targets", "type"=>"journal", "authors"=>[{"first_name"=>"Nitin", "last_name"=>"Bhardwaj", "scopus_author_id"=>"10046658400"}, {"first_name"=>"Matthew B.", "last_name"=>"Carson", "scopus_author_id"=>"16401061500"}, {"first_name"=>"Alexej", "last_name"=>"Abyzov", "scopus_author_id"=>"8732206000"}, {"first_name"=>"Koon Kiu", "last_name"=>"Yan", "scopus_author_id"=>"36864205000"}, {"first_name"=>"Hui", "last_name"=>"Lu", "scopus_author_id"=>"7404843285"}, {"first_name"=>"Mark B.", "last_name"=>"Gerstein", "scopus_author_id"=>"24755400900"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"20523742", "sgr"=>"77955477557", "doi"=>"10.1371/journal.pcbi.1000755", "scopus"=>"2-s2.0-77955477557", "pui"=>"359337491", "isbn"=>"1553-7358", "issn"=>"1553734X"}, "id"=>"1f31a344-2745-3371-81ce-8aeca627d7b3", "abstract"=>"<title>Author Summary</title> <p>A regulatory network consists of regulators such as transcription factors or kinases that control the expression or activity of their target genes. Almost always, there are multiple regulators partnering together to control their targets. Compared to more commonplace contexts, these regulators can be thought of as managers in a social or corporate setting controlling their common subordinates. One interesting question that we address here in this study is how the number of governing regulators scales with the number of governed targets. We build and analyze co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We use a simple framework across five species that demonstrate a wide range of evolution: <italic>Escherichia coli</italic> to human. The analysis reveals many properties of partnership networks and shows that the number of co-regulatory partnerships follows an exponential saturation curve with the number of targets. To gain more intuition, we explore more commonplace contexts and find that exponential saturation relationship also exists in several social networks. Finally, we propose a simple model to explain this relationship that also exists in a simulated evolutionary environment.</p>", "link"=>"http://www.mendeley.com/research/analysis-combinatorial-regulation-scaling-partnerships-between-regulators-number-governed-targets", "reader_count"=>61, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>25, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Other"=>2, "Student > Master"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>25, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Other"=>2, "Student > Master"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>1, "Agricultural and Biological Sciences"=>48, "Medicine and Dentistry"=>2, "Arts and Humanities"=>1, "Physics and Astronomy"=>1, "Chemistry"=>1, "Computer Science"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>48}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Argentina"=>1, "Korea (South)"=>1, "United States"=>4, "Norway"=>1, "Denmark"=>1, "United Kingdom"=>1, "France"=>1, "Germany"=>3, "Spain"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/847293"], "description"=>"<p>We first placed an edge between two TFs (or kinases) if they co-regulated (or co-phosphorylated) at least one common target gene. 1,000 random networks of the same degree distribution were then generated. A co-regulation coefficient (CC) for each pair of regulators was defined as the ratio of the average number of genes co-regulated in real network versus random networks. Only those edges with CC>1 were retained (solid green lines in the last network). In this paper, we study the scaling of partners of each regulator (green edges) with the number of targets (outgoing gray edges).</p>", "links"=>[], "tags"=>["co-regulation"], "article_id"=>517741, "categories"=>["Biological Sciences", "Computational Biology"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Obtaining_a_co_regulation_network_from_a_regulatory_network_/517741", "title"=>"Obtaining a co-regulation network from a regulatory network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-27 02:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/421642"], "description"=>"<div><p>Through combinatorial regulation, regulators partner with each other to control common targets and this allows a small number of regulators to govern many targets. One interesting question is that given this combinatorial regulation, how does the number of regulators scale with the number of targets? Here, we address this question by building and analyzing co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We carry out analyses across five diverse species: <em>Escherichia coli</em> to human. These reveal many properties of partnership networks, such as the absence of a classical power-law degree distribution despite the existence of nodes with many partners. We also find that the number of co-regulatory partnerships follows an exponential saturation curve in relation to the number of targets. (For <em>E. coli</em> and <em>Bacillus subtilis</em>, only the beginning linear part of this curve is evident due to arrangement of genes into operons.) To gain intuition into the saturation process, we relate the biological regulation to more commonplace social contexts where a small number of individuals can form an intricate web of connections on the internet. Indeed, we find that the size of partnership networks saturates even as the complexity of their output increases. We also present a variety of models to account for the saturation phenomenon. In particular, we develop a simple analytical model to show how new partnerships are acquired with an increasing number of target genes; with certain assumptions, it reproduces the observed saturation. Then, we build a more general simulation of network growth and find agreement with a wide range of real networks. Finally, we perform various down-sampling calculations on the observed data to illustrate the robustness of our conclusions.</p></div>", "links"=>[], "tags"=>["combinatorial", "scaling", "partnerships", "regulators", "governed", "targets"], "article_id"=>143276, "categories"=>["Biological Sciences"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755", "stats"=>{"downloads"=>7, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Analysis_of_Combinatorial_Regulation_Scaling_of_Partnerships_between_Regulators_with_the_Number_of_Governed_Targets/143276", "title"=>"Analysis of Combinatorial Regulation: Scaling of Partnerships between Regulators with the Number of Governed Targets", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-05-27 00:54:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/847588"], "description"=>"<p>(a) <i>E. coli</i> and (b) <i>B. subtilis</i>. The exponential saturation curve (in red) shows a slightly better fit than the linear curve (in black) for both species.</p>", "links"=>[], "tags"=>["partners"], "article_id"=>518037, "categories"=>["Biological Sciences", "Computational Biology"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755.g004", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_number_of_partners_vs_the_number_of_target_operons_/518037", "title"=>"The number of partners vs. the number of target operons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-27 02:13:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/847357"], "description"=>"<p>(a–e) The transcription network of five species, (f) the phosphorylation network in yeast. With the exception of <i>E. coli</i>, which shows inhomogeneous connectivity (only a few regulators with a large number of partners and large number of regulators with a few partners), all other species display homogenous connectivity.</p>", "links"=>[], "tags"=>["networks"], "article_id"=>517809, "categories"=>["Biological Sciences", "Computational Biology"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Connectivity_of_the_partnership_networks_in_several_organisms_/517809", "title"=>"Connectivity of the partnership networks in several organisms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-27 02:10:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/847974"], "description"=>"<p>The sizes of the regulatory networks (transcription and phosphorylation) for each species.</p>", "links"=>[], "tags"=>["sizes", "networks"], "article_id"=>518429, "categories"=>["Biological Sciences", "Computational Biology"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755.t001", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_sizes_of_the_regulatory_networks_transcription_and_phosphorylation_for_each_species_/518429", "title"=>"The sizes of the regulatory networks (transcription and phosphorylation) for each species.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-05-27 02:20:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/847455"], "description"=>"<p>(a–e) The transcription network of five species, (f) the phosphorylation network in yeast, (g) the transcription network of <i>B. subtilis</i> and (h) the generative model. Black and gray lines correspond to real and random networks respectively. Random networks were generated by shuffling the edges in real networks while maintaining the in- and out-degree of each node. The best fit line and corresponding R<sup>2</sup> value is indicated for each sub-graph.</p>", "links"=>[], "tags"=>["partners", "genes"], "article_id"=>517902, "categories"=>["Biological Sciences", "Computational Biology"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_number_of_partners_vs_the_number_of_target_genes_for_each_regulator_/517902", "title"=>"The number of partners vs. the number of target genes for each regulator.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-27 02:11:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/847680"], "description"=>"<p>(a) A schematic of the process of analyzing the number of targets and partners for a node of interest (black node, labeled <i>c</i>). It can be a gene and its targets are the genes it regulates and its partners are other regulators that control at least one common target. In social contexts studied here, a node can also be a blog with other blogs that it links to as its targets, and the other blogs that link to same target blogs as its partners. Similarly, it can also be an email user whose targets are the users he/she sends an email to and her/his partners are other users that email at least one common user. We study the scaling of partners (y-axis) with the number of targets (x-axis). (b) The number of blogs a user links his/her blogs to (x-axis) vs. the number of blogs which point links to the same blogs (y-axis). Each data point corresponds to a blog in the blogs network (Left panel). The number of recipients a user sends an email to vs. the number of other users who email the same recipients (Right Panel). Each data point corresponds to a user (who sends an email) in the email network.</p>", "links"=>[], "tags"=>["directed"], "article_id"=>518135, "categories"=>["Biological Sciences", "Computational Biology"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_using_directed_social_networks_/518135", "title"=>"A comparison using directed social networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-27 02:15:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/847825"], "description"=>"<p>(a) Initially, there are no co-regulatory interactions between regulators. (b) Upon growth, the new regulator must (shown in green) begin regulating an already regulated gene (the yellow box marked with a cross) in order to gain a co-regulation partnership (dashed line). (c) In order to gain another partnership, a regulator must regulate genes regulated by other regulators (the left yellow box marked with a cross) besides its partners. The number in each box represents the current number of targets for that regulator.</p>", "links"=>[], "tags"=>["describing", "co-regulation"], "article_id"=>518276, "categories"=>["Biological Sciences", "Computational Biology"], "users"=>["Nitin Bhardwaj", "Matthew B. Carson", "Alexej Abyzov", "Koon-Kiu Yan", "Hui Lu", "Mark B. Gerstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000755.g006", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_model_describing_the_growth_of_co_regulation_networks_/518276", "title"=>"A model describing the growth of co-regulation networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-27 02:17:56"}

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