Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update
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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/621031"], "description"=>"<p>The activity level of four signaling molecules (MAPK1,2, JNK, AKT, and p70s6K) proteins in EGF/IGF/Insulin cell signaling network were simulated under activation of three ligand (EGF, insulin, and IGF-1) at 100 iterations. If the value of difference between the experimental and simulation data is bigger than zero, the corresponding box is colored in red; if best agreement, the box is black; but if the value of the difference between simulation and experimental data be smaller than zero, the box is green.</p>", "links"=>[], "tags"=>["molecular biology", "Computational biology", "neuroscience", "computer science"], "article_id"=>291526, "categories"=>["Information And Computing Sciences", "Molecular Biology", "Biological Sciences", "Neuroscience"], "users"=>["Isar Nassiri", "Ali Masoudi-Nejad", "Mahdi Jalili", "Ali Moeini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039643.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_the_experimental_results_with_those_obtained_through_simulation_/291526", "title"=>"Comparison between the experimental results with those obtained through simulation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-21 00:25:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/322292", "https://ndownloader.figshare.com/files/322313", "https://ndownloader.figshare.com/files/322334"], "description"=>"<div><p>Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.</p> </div>", "links"=>[], "tags"=>["nonparametric", "simulation", "transduction", "networks", "semi-synchronized"], "article_id"=>123619, "categories"=>["Information And Computing Sciences", "Molecular Biology", "Biological Sciences", "Neuroscience"], "users"=>["Isar Nassiri", "Ali Masoudi-Nejad", "Mahdi Jalili", "Ali Moeini"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0039643.s001", "https://dx.doi.org/10.1371/journal.pone.0039643.s002", "https://dx.doi.org/10.1371/journal.pone.0039643.s003"], "stats"=>{"downloads"=>10, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Nonparametric_Simulation_of_Signal_Transduction_Networks_with_Semi_Synchronized_Update/123619", "title"=>"Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-06-21 01:00:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/620795"], "description"=>"<p>Rows represent the measures of three assayed intracellular molecules, and columns represent 4 different ligands. For each combination of ligand and target molecule, one of 4 kinase inhibitors was applied, as indicated in the schematic diagram below the data. The numbers on the right of the figure indicate the maximum values of the corresponding row. The Figure was created using Data-Rail program <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039643#pone.0039643-Alexopoulos1\" target=\"_blank\">[4]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039643#pone.0039643-SaezRodriguez1\" target=\"_blank\">[48]</a>.</p>", "links"=>[], "tags"=>["dataset", "proteins", "hepatocyte"], "article_id"=>291289, "categories"=>["Information And Computing Sciences", "Molecular Biology", "Biological Sciences", "Neuroscience"], "users"=>["Isar Nassiri", "Ali Masoudi-Nejad", "Mahdi Jalili", "Ali Moeini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039643.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Signaling_dataset_of_three_proteins_from_human_hepatocyte_cells_/291289", "title"=>"Signaling dataset of three proteins from human hepatocyte cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-21 00:21:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/621136"], "description"=>"<p>The P-values were calculated by Wilcoxon test and used to test the changes in simulation results after perturbation. The upward arrow (↑) indicates that the perturbation caused a rise in the proportion of molecules in active form; the straight line (-) indicates no change, and the downward arrow (↓) indicates decrease in the proportion of molecules in active form.</p>", "links"=>[], "tags"=>["simulation", "signaling", "molecules", "activation", "egf", "inhibition"], "article_id"=>291622, "categories"=>["Information And Computing Sciences", "Molecular Biology", "Biological Sciences", "Neuroscience"], "users"=>["Isar Nassiri", "Ali Masoudi-Nejad", "Mahdi Jalili", "Ali Moeini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039643.t001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_comparison_between_the_simulation_and_experimental_results_of_five_signaling_molecules_under_activation_of_EGF_and_inhibition_of_TSC2_/291622", "title"=>"The comparison between the simulation and experimental results of five signaling molecules under activation of EGF and inhibition of TSC2.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-21 00:27:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/620901"], "description"=>"<p>(A) human cancer cell signaling, and (B) mouse hippocampus CA1 neural networks were weighted by similarity measure. Nodes were labeled according to their position in the cell, including cell membrane, adducin, cell adhesion, centrosome, cytoskeleton, endothelial, endoplasmic reticulum, cytosolic, extracellular space, golgi apparatus, lysosome, mitochondria, nucleus, ribosome and vesicles. VOSveiwer program was used for visualizing connectivity-based clustering patterns <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039643#pone.0039643-Nees1\" target=\"_blank\">[35]</a>. This tool provides visualization of similarities, where objects with high similarity are located close to each other and those with low similarity are located far from each other.</p>", "links"=>[], "tags"=>["weighted", "cellular", "signaling", "networks", "applying"], "article_id"=>291398, "categories"=>["Information And Computing Sciences", "Molecular Biology", "Biological Sciences", "Neuroscience"], "users"=>["Isar Nassiri", "Ali Masoudi-Nejad", "Mahdi Jalili", "Ali Moeini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039643.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cluster_density_view_of_the_weighted_cellular_signaling_networks_by_applying_similarity_measure_/291398", "title"=>"Cluster density view of the weighted cellular signaling networks by applying similarity measure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-21 00:23:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/620648"], "description"=>"<p> and are connected with one directed edge (C<sub>ij</sub> = 1). Node and are also connected with four common node (V<sub>m−1</sub>,V<sub>m−2</sub>,V<sub>m−3</sub>,V<sub>m−4</sub>), and in total with eight distinct neighbors (V<sub>m−1</sub>,V<sub>m−2</sub>,V<sub>m−3</sub>,V<sub>m−4</sub>,V<sub>a</sub>, V<sub>b</sub>, V<sub>c</sub>, V<sub>d</sub>). The NSI will then be NSI<sub>ij</sub>  = 5/9 = 0.55.</p>", "links"=>[], "tags"=>["normalize", "similarityindex"], "article_id"=>291142, "categories"=>["Information And Computing Sciences", "Molecular Biology", "Biological Sciences", "Neuroscience"], "users"=>["Isar Nassiri", "Ali Masoudi-Nejad", "Mahdi Jalili", "Ali Moeini"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039643.g001", "stats"=>{"downloads"=>0, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Weighting_of_the_edge_by_Normalize_SimilarityIndex_NSI_/291142", "title"=>"Weighting of the edge by Normalize SimilarityIndex (NSI).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-21 00:19:02"}

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Relative Metric

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