{"database":"Cell Collective","file_versions":[],"scores":{"citationCount":47617,"reanalysisCount":0,"viewCount":0,"searchCount":0},"additional":{"omics_type":["Models"],"submitter":["Tomas Helikar"],"version_name":[""],"full_dataset_link":["https://cellcollective.org/#2171/t-cell-receptor-signaling"],"model_score":["92.8552"],"default_version":["1"],"ModelFormat":["SBML"],"submitter_affiliation":[""],"submitter_email":[""],"version_id":["1"],"repository":["Cell Collective"],"version_url":["https://cellcollective.org/#2171:1/t-cell-receptor-signaling"],"version_description":[""],"pubmed_abstract":["Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications."],"pubmed_title":["A logical model provides insights into T cell receptor signaling."],"pubmed_authors":["Saez-Rodriguez Julio J, Simeoni Luca L, Lindquist Jonathan A JA, Hemenway Rebecca R, Bommhardt Ursula U, Arndt Boerge B, Haus Utz-Uwe UU, Weismantel Robert R, Gilles Ernst D ED, Klamt Steffen S, Schraven Burkhart B"],"description_synonyms":["MGC130048, scale tissue, T cell activation, T-cell surface antigen T4/Leu-3, Product, determination, acetylglucosaminyltransferase-like protein, T-lymphocyte activation, Mbp1, Receptors, A4, B-cell receptor complex, ZDBF1, c-fyn, element, L3T4, Pharmaceutical Product, T-cell activation, Ly-4, Kinase, myd, Tp44, IKKg, INSL3R, KEY, Key, gamma sarcoglycan, increased, like-acetylglucosaminyltransferase, cfyn-a, membrane bound, plant peltate hair, T Cell Antigen Receptor, Mbp-1, procedures, Ask, ASK, CHIF, genetic, GREAT, medicine, Pharmaceutical, Great, gamma-sarcoglycan, Mekk5, CD28, B-lymphocyte receptor complex, ATP Phosphotransferases, ATP, single organism signaling, single-organism behavior, wide/broad, T-cell surface antigen T4|Leu-3, Process, functional failure, gyltl1b-b, T-Cell Receptor, T-Cell Antigen, SG-gamma, familial, CD28RNA, antibodies, Acceptance Processes, predicted, Acceptance Process, DmIKKgamma, Antigen Receptor, AI448320, T Cell Receptor, SYN, T-Cell, dIKK, MDDGA6, mKIAA0609, Kenny, sarcoglycan, atomo, Pharmaceutic, failure, KIAA0609, atome, acetylglucosaminyltransferase-like 1A, activation, fg, LGR8, Transphosphorylases, Epistemology, Lgr8, AA545217, gyltl1b, Element, T lymphocyte activation, T-cell surface glycoprotein CD4, p32, atoms, mdc1d, IKK-gamma, CD4mut, gamma (35kDa dystrophin-associated glycoprotein), LARGE_HUMAN, MDC1D, wide, immunoglobulin, DMDA, ASK1, B cell receptor accessory molecule complex, DmelCG16910, enr, 35kD dystrophin-associated glycoprotein, B lymphocyte receptor complex, microarray, T Cell, inherited genetic, Feedbacks, accessory, SGCG_HUMAN, conformation, Processes, peltate hair, number, SLK, broad, disfunctional, LARGE1, presence, supernumerary, TYPE, dIKK-gamma, froggy, Gyltl1a, AW552119, DAGA4, antibody, BCR complex, DmIKK-gamma, 35DAG, dmIKKgamma, MAM, gamma-SG, IKK[[gamma]], MAL, SCG3, immunoglobulin complex, Phosphotransferase, Drugs, Gpr106, MDDGB6, MAPKKK5, LARGE, RXFPR2, Transphosphorylase, T-Cell Receptors, BPFD#36, drugs, IKK, Pharmaceuticals., TSULF, Preparation, elements, Behaviors, constitutitional genetic, GPR106, atom, Antigen Receptors, Products, T-Cell Antigen Receptor, 35 kDa dystrophin-associated glycoprotein, Leu2, defective, Medications, Cell, Phosphotransferases, SGCG, LGMD2C, T Cell Antigen, IKKgamma, count in organism, T-cell differentiation antigen L3T4, chemical analysis, atomus, techniques, scales, UNQ630/PRO1246, opsonin activity, p59-FYN, 7420452D20Rik, Acceptance, DBF4A, scale, DMDA1, Pharmaceutic Preparations, Kinases, Dmikkgamma, increased number, LGR8.1, CG16910, TP44, Drug, present in greater numbers in organism, Preparations, like-glycosyltransferase, signalling process, T Cell Receptors, SCARMD2, T-Cell Antigen Receptors, B cell receptor activity, CD4, assay, Receptor, CD8, Pharmaceutical Products, hereditary, glycosyltransferase-like protein LARGE1, methodology, Pharmaceutical Preparation"],"pubmed_title_synonyms":["T Cells, T-cell, T cell, Thymus-Dependent Lymphocytes, signalling process, T Lymphocyte, T-Cell, mature T cell, T-Cells, Cells, Thymus-Dependent, T, T Cell, Lymphocytes, T-Lymphocyte, immature T cell, T Lymphocytes, T lymphocyte, Thymus Dependent Lymphocytes, Cell, Lymphocyte, single organism signaling., Thymus-Dependent Lymphocyte"],"name_synonyms":["T Cell Antigen, Antigen Receptor, signalling process, T-Cell Antigen Receptor, T Cell Receptor, T-Cell, T-Cell Receptor, T-Cell Antigen, T Cell Receptors, T Cell Antigen Receptor, T-Cell Antigen Receptors, Receptors, T Cell, Receptor, Antigen Receptors, single organism signaling., T-Cell Receptors"],"pubmed_abstract_synonyms":["author summary, MGC130048, scale tissue, T cell activation, T-cell surface antigen T4/Leu-3, Product, determination, acetylglucosaminyltransferase-like protein, Feature, T-lymphocyte activation, Mbp1, A4, T-Lymphocyte, B-cell receptor complex, ZDBF1, c-fyn, element, L3T4, T Lymphocyte, Pharmaceutical Product, T-cell activation, Ly-4, myd, immature T cell, Tp44, IKKg, INSL3R, KEY, Key, gamma sarcoglycan, increased, like-acetylglucosaminyltransferase, cfyn-a, membrane bound, T-Cells, plant peltate hair, Mbp-1, T, procedures, Ask, ASK, CHIF, genetic, GREAT, T Cells, medicine, Pharmaceutical, Great, gamma-sarcoglycan, Mekk5, CD28, B-lymphocyte receptor complex, Thymus Dependent Lymphocytes, single organism signaling, single-organism behavior, wide/broad, T-cell surface antigen T4|Leu-3, Process, functional failure, gyltl1b-b, SG-gamma, familial, CD28RNA, antibodies, Acceptance Processes, predicted, Acceptance Process, DmIKKgamma, AI448320, SYN, T-Cell, dIKK, MDDGA6, mKIAA0609, Kenny, sarcoglycan, atomo, Pharmaceutic, failure, KIAA0609, atome, acetylglucosaminyltransferase-like 1A, activation, fg, LGR8, T-cell, Epistemology, Lgr8, AA545217, gyltl1b, Element, T lymphocyte activation, T-cell surface glycoprotein CD4, p32, atoms, Thymus-Dependent, mdc1d, IKK-gamma, CD4mut, Features, gamma (35kDa dystrophin-associated glycoprotein), LARGE_HUMAN, Thymus-Dependent Lymphocyte, summary, MDC1D, wide, immunoglobulin, T cell, DMDA, ASK1, B cell receptor accessory molecule complex, DmelCG16910, enr, 35kD dystrophin-associated glycoprotein, Cells, B lymphocyte receptor complex, microarray, T Cell, inherited genetic, Feedbacks, accessory, Thymus-Dependent Lymphocytes, SGCG_HUMAN, conformation, Processes, peltate hair, number, SLK, broad, disfunctional, LARGE1, presence, supernumerary, TYPE, dIKK-gamma, froggy, Gyltl1a, AW552119, DAGA4, antibody, BCR complex, DmIKK-gamma, 35DAG, dmIKKgamma, MAM, gamma-SG, IKK[[gamma]], MAL, SCG3, immunoglobulin complex, Drugs, Gpr106, MDDGB6, MAPKKK5, LARGE, RXFPR2, BPFD#36, drugs, IKK, Pharmaceuticals., synopsis, mature T cell, Characteristics, TSULF, Preparation, elements, Behaviors, constitutitional genetic, T Lymphocytes, GPR106, atom, Lymphocyte, Products, 35 kDa dystrophin-associated glycoprotein, Leu2, defective, Medications, Cell, SGCG, LGMD2C, IKKgamma, count in organism, Characteristic, T-cell differentiation antigen L3T4, chemical analysis, atomus, techniques, scales, T lymphocyte, UNQ630/PRO1246, opsonin activity, p59-FYN, 7420452D20Rik, Acceptance, DBF4A, scale, DMDA1, Pharmaceutic Preparations, Dmikkgamma, increased number, Lymphocytes, LGR8.1, CG16910, TP44, Drug, present in greater numbers in organism, Preparations, like-glycosyltransferase, signalling process, SCARMD2, B cell receptor activity, CD4, assay, CD8, Pharmaceutical Products, hereditary, glycosyltransferase-like protein LARGE1, methodology, Pharmaceutical Preparation"],"citation_count":["47617"],"additional_accession":[]},"is_claimable":false,"name":"T Cell Receptor Signaling","description":"Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.","dates":{"created":"2013-06-24","publication":"","submission":"2019-08-09","last_modified":"2019-08-09"},"accession":"2171","cross_references":{"pubmed":["17722974"]}}