Mathematical Model of a Telomerase Transcriptional Regulatory Network Developed by Cell-Based Screening: Analysis of Inhibitor Effects and Telomerase Expression Mechanisms
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{"title"=>"Mathematical Model of a Telomerase Transcriptional Regulatory Network Developed by Cell-Based Screening: Analysis of Inhibitor Effects and Telomerase Expression Mechanisms", "type"=>"journal", "authors"=>[{"first_name"=>"Alan E.", "last_name"=>"Bilsland", "scopus_author_id"=>"6507875517"}, {"first_name"=>"Katrina", "last_name"=>"Stevenson", "scopus_author_id"=>"12446187400"}, {"first_name"=>"Yu", "last_name"=>"Liu", "scopus_author_id"=>"56060558200"}, {"first_name"=>"Stacey", "last_name"=>"Hoare", "scopus_author_id"=>"7005941433"}, {"first_name"=>"Claire J.", "last_name"=>"Cairney", "scopus_author_id"=>"15848033400"}, {"first_name"=>"Jon", "last_name"=>"Roffey", "scopus_author_id"=>"6602918422"}, {"first_name"=>"W. Nicol", "last_name"=>"Keith", "scopus_author_id"=>"55662852800"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84895749343", "pmid"=>"24550717", "sgr"=>"84895749343", "doi"=>"10.1371/journal.pcbi.1003448", "isbn"=>"1553-7358", "issn"=>"15537358", "pui"=>"372548676"}, "id"=>"c61492f1-f11a-363c-a206-38c653b0689c", "abstract"=>"Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3'-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability.", "link"=>"http://www.mendeley.com/research/mathematical-model-telomerase-transcriptional-regulatory-network-developed-cellbased-screening-analy", "reader_count"=>29, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Librarian"=>2, "Student > Doctoral Student"=>1, "Researcher"=>5, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1386411"], "description"=>"<p>(A), topology and steady states of the basal <i>TERT</i> model. Transfection screening data were used to assign activating or repressive network interactions according to the direction of regulation of each promoter and using the cut-offs of minimum fold-change 1.5 up- or down-regulation of promoter activity and p-value (ANOVA)<0.01. Topology of the final model was visualised in Cytoscape <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi.1003448-Kohl1\" target=\"_blank\">[105]</a>. Arrows indicate activation, T-shape indicates repression. Left and right panels show steady states 1 and 2, respectively. Red colour indicates the node is on, green colour indicates the node is off in each steady state. (B), core statespace structure of the model. Statespace was calculated by brute force and visualised in Pajek <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi.1003448-Batagelj1\" target=\"_blank\">[103]</a>. Basins of attraction were extracted as weak components of the statespace. To visualise the core structure, all nodes with in-degree ≥1 were extracted as new networks from each weak component and visualised with transient states in blue and attractor states in yellow. Left panel corresponds to state 1, right panel corresponds to state 2.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "statespace", "transcriptional", "neighbourhood"], "article_id"=>935021, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.g002", "stats"=>{"downloads"=>2, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topology_steady_states_and_statespace_structure_of_the_TERT_transcriptional_neighbourhood_model_/935021", "title"=>"Topology, steady states, and statespace structure of the <i>TERT</i> transcriptional neighbourhood model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386410"], "description"=>"<p>(A), overexpression of <i>TERT</i> activators. A2780 cells were transfected with the luciferase reporters shown on the vertical axis. Each reporter was co-transfected alongside vector control or transcription factor expression plasmid shown in the right hand boxes. Each bar type represents a different expression vector relative to control. 48 h post-transfection, promoter activities were analysed by luciferase assay. (B), overexpression of <i>TERT</i> repressors, transfected as in (A). (C), overexpression of <i>E2F1</i> against the promoter panel, transfected as above. Because of the very strong self-regulatory effect on its own promoter, <i>E2F1</i> is shown on a different scale and separately from the other <i>TERT</i> repressors. Mean ± SEM of 3 experiments (ns: not significant; *: p<0.5; **: p<0.01).</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "transcriptional", "neighbourhood", "a2780", "cells", "transfection"], "article_id"=>935020, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.g001", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Definition_of_the_TERT_transcriptional_neighbourhood_in_A2780_cells_by_transfection_screening_/935020", "title"=>"Definition of the <i>TERT</i> transcriptional neighbourhood in A2780 cells by transfection screening.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386424"], "description"=>"<div><p>Cancer cells depend on transcription of telomerase reverse transcriptase (<i>TERT</i>). Many transcription factors affect <i>TERT</i>, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of <i>TERT</i> transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 <i>TERT</i> regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of <i>TERT</i> transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted <i>TERT</i> repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous <i>TERT</i> expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable <i>TERT</i> repression dependent on noise and expression of <i>JUN</i>, corresponding with observations from a previous study. <i>MYC</i> expression is critical in <i>TERT</i> activation in the model, consistent with its well known function in endogenous <i>TERT</i> regulation. Loss of <i>MYC</i> caused complete <i>TERT</i> suppression in our model, substantially rescued only by co-suppression of <i>AR</i>. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in <i>TERT</i> promoter mutation. RNAi targeting <i>AR</i>, <i>JUN</i>, <i>MXD1</i>, <i>SP3</i>, or <i>TP53</i>, showed that AR suppression does rescue endogenous <i>TERT</i> expression following <i>MYC</i> knockdown in these cells and <i>SP3</i> or <i>TP53</i> siRNA also cause partial recovery. The model therefore successfully predicted several aspects of <i>TERT</i> regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme <i>TERT</i> for transcriptional stability.</p></div>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "telomerase", "transcriptional", "developed", "cell-based", "inhibitor"], "article_id"=>935034, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mathematical_Model_of_a_Telomerase_Transcriptional_Regulatory_Network_Developed_by_Cell_Based_Screening_Analysis_of_Inhibitor_Effects_and_Telomerase_Expression_Mechanisms_/935034", "title"=>"Mathematical Model of a Telomerase Transcriptional Regulatory Network Developed by Cell-Based Screening: Analysis of Inhibitor Effects and Telomerase Expression Mechanisms", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386419"], "description"=>"<p>IUPAC names and CAS numbers of the compounds used in the study.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "names", "cas", "numbers", "compounds"], "article_id"=>935029, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.t005", "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_IUPAC_names_and_CAS_numbers_of_the_compounds_used_in_the_study_/935029", "title"=>"IUPAC names and CAS numbers of the compounds used in the study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386418"], "description"=>"<p>Candidate novel interactions included from the screen.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "interactions", "included"], "article_id"=>935028, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.t004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Candidate_novel_interactions_included_from_the_screen_/935028", "title"=>"Candidate novel interactions included from the screen.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386415"], "description"=>"<p>(A), structure of FFL types I–IV. Structures visualised in Pajek <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi.1003448-Batagelj1\" target=\"_blank\">[103]</a>. Bold lines indicate activation, dashed lines indicate repression. X, Y represent generalised transcription factors, Z represents a regulated gene. (B), activation and repression modules in the <i>TERT</i> transcriptional neighbourhood model. Subnetworks were extracted from the main model and visualised in Pajek <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi.1003448-Batagelj1\" target=\"_blank\">[103]</a>. Extraction was achieved as described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#s4\" target=\"_blank\">materials and methods</a>. As an indicator of topological importance, node betweenness centralities were calculated and are given in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi-1003448-t006\" target=\"_blank\">table 6</a>. Additionally, we calculated flow betweenness which is not dependent only on geodesics <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi.1003448-Freeman1\" target=\"_blank\">[77]</a>. (C), Effect of single- and double-node targeting on <i>TERT</i> on-states. Rule-sets for each node were modified in turn individually (black bars) to simulate constitutive repression or activation. For each rule-set change, statespace was derived and the proportion of system states evolving to attractor states with <i>TERT</i> stably on was quantified. The analysis was repeated for each node in the context of double knockouts with <i>MYC</i> also suppressed in each case (grey bars). (D), <i>MYC</i> dependent <i>TERT</i> repression and reversal by <i>AR</i>. A2780 were transfected with 200 nM non-specific control siRNA (Con), 100 nM <i>MYC</i> with 100 nM non-specific (<i>MYC</i>), or 100 nM <i>MYC</i> and 100 nM each specific siRNA. Cells were harvested after 48 h and RNA extracted for analysis of <i>TERT</i> expression normalised to RPS15 by RT-QPCR. Mean ± SEM of <i>TERT</i> expression in treated cells relative to control from three experiments (ns: not significant; *: p<0.05; **: p<0.01). (E), Knockdown of <i>TERT</i> regulatory transcription factors by RNAi. A2780 were transfected with 100 nM each specific siRNA (RNAi) or non-specific control (NS) and harvested after 48 h. 20 µg protein samples were analysed by western blotting against the respective targets. ERK counter-blots were also performed. Each experiment was performed twice. Representative blots are shown.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "robust"], "article_id"=>935025, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.g005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topological_analysis_of_the_TERT_model_and_prediction_of_robust_MYC_dependent_TERT_repression_/935025", "title"=>"Topological analysis of the <i>TERT</i> model, and prediction of robust <i>MYC</i> dependent <i>TERT</i> repression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386414"], "description"=>"<p>(A), Effect of BIO on the transcription factor promoter panel and simulation in the model. Top panel: A2780 cells were transfected with each luciferase reporter. 32 h later transfectants were treated for 16 h prior to luciferase assay with DMSO or 5 µM BIO. Luciferase assay results meeting model cut-off of FC>1.5, p<0.01 (Fos and <i>STAT3</i>) were modelled as rule table modifications. Lower panel: heat-map representation of new steady states obtained by setting Fos to be constitutively suppressed and <i>STAT3</i> to be constitutively active. Red colour indicates the node is on, green colour indicates the node is off. (B), noise simulation by the bit-flip method under basal or BIO modified rules in the model. State coherence <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi.1003448-Willadsen1\" target=\"_blank\">[73]</a> of each attractor under the model basal rule-set (top) or BIO simulation (bottom) was evaluated as described in the text. Heat-maps of both attractors under either rule-set are shown, with colouration as above. Horizontal arrows between attractor states indicate that a transient state change of the adjacent node caused a shift to the alternate steady state. Vertical and diagonal arrows indicate the state changes resulting from the rule-set change (basal → BIO, or the reverse).</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "gsk3", "inhibition"], "article_id"=>935024, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.g004", "stats"=>{"downloads"=>1, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulated_effects_of_GSK3_inhibition_and_network_noise_on_TERT_transcription_/935024", "title"=>"Simulated effects of GSK3 inhibition and network noise on <i>TERT</i> transcription.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386412"], "description"=>"<p>A2780 cells were transfected with each luciferase reporter shown and 32(A), 5 µM SU6656, (B), 10 µM FR180204. Left panels show mean ± SEM of 3 experiments (ns: not significant; *: p<0.5; **: p<0.01). Central panels: luciferase assay results meeting model cut-off of FC>1.5, p<0.01 were modelled as rule table modifications. Heat-map representation of new model steady states obtained by setting rule tables for constitutive activation or suppression at those nodes significantly affected in the luciferase assay. Red colour indicates the node is on, green colour indicates the node is off. Right panels: analysis of <i>TERT</i> expression after repeat inhibitor treatments. Control and treated samples from treatment time points shown were analysed by RT-QPCR for <i>TERT</i> expression normalised to RPS15. Mean ± SEM of <i>TERT</i> expression in treated cells relative to control from three experiments (ns: not significant; **: p<0.01).</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "inhibitor", "transcriptional"], "article_id"=>935022, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modelling_inhibitor_effects_on_the_TERT_transcriptional_neighbourhood_/935022", "title"=>"Modelling inhibitor effects on the <i>TERT</i> transcriptional neighbourhood.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386423"], "description"=>"<p>Common, non-concordant interactions in literature and data-derived models.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "non-concordant", "interactions", "data-derived"], "article_id"=>935033, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.t003", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Common_non_concordant_interactions_in_literature_and_data_derived_models_/935033", "title"=>"Common, non-concordant interactions in literature and data-derived models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386421"], "description"=>"<p>Accession numbers of all human genes from the model.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "numbers", "genes"], "article_id"=>935031, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.t001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accession_numbers_of_all_human_genes_from_the_model_/935031", "title"=>"Accession numbers of all human genes from the model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386422"], "description"=>"<p>Common, concordant interactions in literature and data-derived models.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "concordant", "interactions", "data-derived"], "article_id"=>935032, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.t002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Common_concordant_interactions_in_literature_and_data_derived_models_/935032", "title"=>"Common, concordant interactions in literature and data-derived models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386420"], "description"=>"<p>Node betweenness centrality values for the activation and repression modules.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "betweenness", "centrality", "activation", "repression"], "article_id"=>935030, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.t006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Node_betweenness_centrality_values_for_the_activation_and_repression_modules_/935030", "title"=>"Node betweenness centrality values for the activation and repression modules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386417"], "description"=>"<p>(A), influence of activation module dominance on <i>TERT</i> on-state multiplicity. Topology of the model was altered by a series of 600 random attacks deleting activation and repression module interactions with increasing probability. Direct interactions with <i>TERT</i> were left unaltered in all attacks. The remaining sub-networks were extracted from each model variant as described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#s4\" target=\"_blank\">materials and methods</a> and the number of edges in each were counted to determine the edge ratio AM/RM. The statespace of each model was calculated and the number of stable on states present for the <i>TERT</i> node was quantified and plotted against the calculated AM/RM edge ratio for each variant network. Significance of edge ratio population differences was tested in Matlab by Wilcoxon rank-sum test (**: p<0.01). (B), influence of AM dominance in random networks. A series of 300 (15 node) networks was generated with semi-random edge seeding and increasing edge density. All networks were constrained to have one regulated node which was connected downstream of all others. The number of activators and repressors of the node was allowed to vary randomly. Statespace and AM/RM edge ratios were calculated for each network and compared as in (A), calculating number of stable on-states for the fully connected node. Significance of edge ratio population differences was tested in Matlab by Wilcoxon rank-sum test (**: p<0.01).</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "on-state", "multiplicity"], "article_id"=>935027, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topological_control_of_TERT_on_state_multiplicity_in_the_model_/935027", "title"=>"Topological control of <i>TERT</i> on-state multiplicity in the model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-13 03:38:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1386416"], "description"=>"<p>(A), overexpression of <i>ETS2</i> against the promoter panel. A2780 cells were transfected with the luciferase reporters shown. Each reporter was co-transfected alongside vector control or pCMV-<i>ETS2</i>. 48 h post-transfection, promoter activities were analysed by luciferase assay. Mean ± SEM of three experiments (ns: not significant; *: p<0.05; **: p<0.01). (B), regulation of the <i>ETS2</i> promoter by the transcription factor panel. A2780 cells were co-transfected with <i>ETS2</i>-luciferase reporter alongside vector control or expression vectors shown. 48 h post-transfection, promoter activities were analysed by luciferase assay. Mean ± SEM of three experiments (ns: not significant; *: p<0.05; **: p<0.01). (C), effect of <i>ETS2</i> expression on the <i>TERT</i> promoter under <i>MYC</i> inhibition. A2780 cells were co-transfected with the <i>TERT</i>-luciferase reporter and with pCMV control or pCMV-<i>ETS2</i> with non-targeting or <i>MYC</i>-specific siRNA. 48 h post-transfection, promoter activities were analysed by luciferase assay. Mean ± SEM of three experiments (D), interactions in the <i>ETS2</i> subnetwork added into the model with cutoffs FC>1.5, p<0.01 from the transfection data. The subnetwork was visualised in Cytoscape <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003448#pcbi.1003448-Kohl1\" target=\"_blank\">[105]</a>. Arrows indicate activation, T-shape indicates repression. (E), Effect of single- and double-node targeting on <i>TERT</i> on-states in the <i>ETS2</i> modified model. Rule-sets for each node were modified in turn individually (black bars) to simulate constitutive repression or activation. For each rule-set change, statespace was derived and the proportion of system states evolving to attractor states with <i>TERT</i> stably on was quantified. The analysis was repeated in the background of the <i>MYC</i> suppressed rule-set (grey bars).</p>", "links"=>[], "tags"=>["Computational biology", "Molecular cell biology", "systems biology", "oncology", "ets", "transcription"], "article_id"=>935026, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Alan E. Bilsland", "Katrina Stevenson", "Yu Liu", "Stacey Hoare", "Claire J. Cairney", "Jon Roffey", "W. Nicol Keith"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003448.g006", "stats"=>{"downloads"=>1, "page_views"=>44, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_of_Ets_family_transcription_factor_gain_of_function_at_the_TERT_promoter_/935026", "title"=>"Simulation of Ets family transcription factor gain of function at the <i>TERT</i> promoter.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-13 03:38:06"}

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Relative Metric

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