SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies
Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a...
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| Veröffentlicht in: | PLoS computational biology 2014-12, Vol.10 (12), p.e1003993-e1003993 |
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| creator | Naik, Adviti Rozman, Damjana Belič, Aleš |
| description | Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders. |
| doi_str_mv | 10.1371/journal.pcbi.1003993 |
| format | Article |
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We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1003993</identifier><identifier>PMID: 25500563</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adipose Tissue - metabolism ; Biology ; Biology and Life Sciences ; Computational Biology - methods ; Computer and Information Sciences ; Computer Simulation ; Databases, Factual ; Enzymes ; Experiments ; Fatty Liver - metabolism ; Gene expression ; Genomes ; Glucose ; Humans ; Internet ; Lipids ; Liver ; Liver - metabolism ; Liver diseases ; Mathematical models ; Medicine and Health Sciences ; Metabolic disorders ; Metabolism ; Metabolites ; Models, Biological ; Parameter estimation ; Pathogenesis ; Proteins ; Reproducibility of Results ; Rodents ; Signal transduction ; Software ; Studies</subject><ispartof>PLoS computational biology, 2014-12, Vol.10 (12), p.e1003993-e1003993</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Naik et al 2014 Naik et al</rights><rights>2014 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Naik A, Rozman D, Beli? A (2014) SteatoNet: The First Integrated Human Metabolic Model with Multi-layered Regulation to Investigate Liver-Associated Pathologies. PLoS Comput Biol 10(12): e1003993. doi:10.1371/journal.pcbi.1003993</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c633t-f2403d05abc60a1b9fb42f4a4ab32d6a47305afe78cf6fdf659dbf1b13ca0f163</citedby><cites>FETCH-LOGICAL-c633t-f2403d05abc60a1b9fb42f4a4ab32d6a47305afe78cf6fdf659dbf1b13ca0f163</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263370/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263370/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25500563$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Maranas, Costas D.</contributor><creatorcontrib>Naik, Adviti</creatorcontrib><creatorcontrib>Rozman, Damjana</creatorcontrib><creatorcontrib>Belič, Aleš</creatorcontrib><title>SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.</description><subject>Adipose Tissue - metabolism</subject><subject>Biology</subject><subject>Biology and Life Sciences</subject><subject>Computational Biology - methods</subject><subject>Computer and Information Sciences</subject><subject>Computer Simulation</subject><subject>Databases, Factual</subject><subject>Enzymes</subject><subject>Experiments</subject><subject>Fatty Liver - metabolism</subject><subject>Gene expression</subject><subject>Genomes</subject><subject>Glucose</subject><subject>Humans</subject><subject>Internet</subject><subject>Lipids</subject><subject>Liver</subject><subject>Liver - metabolism</subject><subject>Liver diseases</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Metabolic disorders</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Models, Biological</subject><subject>Parameter estimation</subject><subject>Pathogenesis</subject><subject>Proteins</subject><subject>Reproducibility of Results</subject><subject>Rodents</subject><subject>Signal transduction</subject><subject>Software</subject><subject>Studies</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqVkk1v1DAQhiMEoqXwDxBE4gKHLHYc54MDUlXxsVJVJApna-yMs1458dZ2Cj3x1_F2t1VX4oJ8iDV-5onzZrLsJSULyhr6fu1mP4FdbJQ0C0oI6zr2KDumnLOiYbx9_GB_lD0LYZ0Y3nb10-yo5JwQXrPj7M9lRIjuAuOHPK4w18aHmJsp4uAhYp-v5hGmfMQI0lmj8tH1aPNfJq7ycbbRFBZu0CfQ4zBbiMZNeXTJcI0hmiE5cmuu0RcQglPm1rmBuHLWDQbD8-yJBhvwxf55kv38_OnH2dfi_NuX5dnpeaFqxmKhy4qwnnCQqiZAZadlVeoKKpCs7GuoGpYONTat0rXudc27XmoqKVNANK3ZSfZ6591YF8Q-uyBo3fKUSlU2iVjuiN7BWmy8GcHfCAdG3BacHwT4aJRF0TZQ91KWfUVkxSmVLbRdUkjVo6Sokuvj_m2zHLFXOEUP9kB6eDKZlRjctajK9LkNSYK3e4F3V3NKUowmKLQWJnTz9t6sq9q2aXhC3-zQAdLVzKRdMqotLk5ZV_OyaRlL1OIfVFo9jka5CbVJ9YOGdwcNiYn4Ow4whyCWl9__g704ZKsdq7wLwaO-T4USsR3su58jtoMt9oOd2l49TPS-6W6S2V-HsPjE</recordid><startdate>20141201</startdate><enddate>20141201</enddate><creator>Naik, Adviti</creator><creator>Rozman, Damjana</creator><creator>Belič, Aleš</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20141201</creationdate><title>SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies</title><author>Naik, Adviti ; Rozman, Damjana ; Belič, Aleš</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c633t-f2403d05abc60a1b9fb42f4a4ab32d6a47305afe78cf6fdf659dbf1b13ca0f163</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adipose Tissue - metabolism</topic><topic>Biology</topic><topic>Biology and Life Sciences</topic><topic>Computational Biology - methods</topic><topic>Computer and Information Sciences</topic><topic>Computer Simulation</topic><topic>Databases, Factual</topic><topic>Enzymes</topic><topic>Experiments</topic><topic>Fatty Liver - metabolism</topic><topic>Gene expression</topic><topic>Genomes</topic><topic>Glucose</topic><topic>Humans</topic><topic>Internet</topic><topic>Lipids</topic><topic>Liver</topic><topic>Liver - metabolism</topic><topic>Liver diseases</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Metabolic disorders</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Models, Biological</topic><topic>Parameter estimation</topic><topic>Pathogenesis</topic><topic>Proteins</topic><topic>Reproducibility of Results</topic><topic>Rodents</topic><topic>Signal transduction</topic><topic>Software</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Naik, Adviti</creatorcontrib><creatorcontrib>Rozman, Damjana</creatorcontrib><creatorcontrib>Belič, Aleš</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Naik, Adviti</au><au>Rozman, Damjana</au><au>Belič, Aleš</au><au>Maranas, Costas D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2014-12-01</date><risdate>2014</risdate><volume>10</volume><issue>12</issue><spage>e1003993</spage><epage>e1003993</epage><pages>e1003993-e1003993</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25500563</pmid><doi>10.1371/journal.pcbi.1003993</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Adipose Tissue - metabolism Biology Biology and Life Sciences Computational Biology - methods Computer and Information Sciences Computer Simulation Databases, Factual Enzymes Experiments Fatty Liver - metabolism Gene expression Genomes Glucose Humans Internet Lipids Liver Liver - metabolism Liver diseases Mathematical models Medicine and Health Sciences Metabolic disorders Metabolism Metabolites Models, Biological Parameter estimation Pathogenesis Proteins Reproducibility of Results Rodents Signal transduction Software Studies |
| title | SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies |
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