Systems biology modeling of omics data: effect of cyclosporine a on the Nrf2 pathway in human renal cells
Incorporation of omic data streams for building improved systems biology models has great potential for improving their predictions of biological outcomes. We have recently shown that cyclosporine A (CsA) strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal prox...
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| Veröffentlicht in: | BMC systems biology 2014-06, Vol.8 (1), p.76-76, Article 76 |
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| creator | Hamon, Jérémy Jennings, Paul Bois, Frederic Y |
| description | Incorporation of omic data streams for building improved systems biology models has great potential for improving their predictions of biological outcomes. We have recently shown that cyclosporine A (CsA) strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells (RPTECs) exposed in vitro. We present here a quantitative calibration of a differential equation model of the Nrf2 pathway with a subset of the omics data we collected.
In vitro pharmacokinetic data on CsA exchange between cells, culture medium and vial walls, and data on the time course of omics markers in response to CsA exposure were reasonably well fitted with a coupled PK-systems biology model. Posterior statistical distributions of the model parameter values were obtained by Markov chain Monte Carlo sampling in a Bayesian framework. A complex cyclic pattern of ROS production and control emerged at 5 μM CsA repeated exposure. Plateau responses were found at 15 μM exposures. Shortly above those exposure levels, the model predicts a disproportionate increase in cellular ROS quantity which is consistent with an in vitro EC50 of about 40 μM for CsA in RPTECs.
The model proposed can be used to analyze and predict cellular response to oxidative stress, provided sufficient data to set its parameters to cell-specific values. Omics data can be used to that effect in a Bayesian statistical framework which retains prior information about the likely parameter values. |
| doi_str_mv | 10.1186/1752-0509-8-76 |
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In vitro pharmacokinetic data on CsA exchange between cells, culture medium and vial walls, and data on the time course of omics markers in response to CsA exposure were reasonably well fitted with a coupled PK-systems biology model. Posterior statistical distributions of the model parameter values were obtained by Markov chain Monte Carlo sampling in a Bayesian framework. A complex cyclic pattern of ROS production and control emerged at 5 μM CsA repeated exposure. Plateau responses were found at 15 μM exposures. Shortly above those exposure levels, the model predicts a disproportionate increase in cellular ROS quantity which is consistent with an in vitro EC50 of about 40 μM for CsA in RPTECs.
The model proposed can be used to analyze and predict cellular response to oxidative stress, provided sufficient data to set its parameters to cell-specific values. Omics data can be used to that effect in a Bayesian statistical framework which retains prior information about the likely parameter values.</description><identifier>ISSN: 1752-0509</identifier><identifier>EISSN: 1752-0509</identifier><identifier>DOI: 10.1186/1752-0509-8-76</identifier><identifier>PMID: 24964791</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Analysis ; Antioxidants ; Biology ; Cyclosporine ; Cyclosporine - pharmacokinetics ; Cyclosporine - toxicity ; Differential equations ; Humans ; Information management ; Kidney - cytology ; Life Sciences ; Markov processes ; Mathematical models ; Medical research ; Models, Biological ; Monte Carlo method ; NF-E2-Related Factor 2 - genetics ; NF-E2-Related Factor 2 - metabolism ; Oxidative stress ; Proteins ; Proteomics ; RNA, Messenger - genetics ; RNA, Messenger - metabolism ; Software ; Systems Biology - methods ; Toxicology</subject><ispartof>BMC systems biology, 2014-06, Vol.8 (1), p.76-76, Article 76</ispartof><rights>COPYRIGHT 2014 BioMed Central Ltd.</rights><rights>2014 Hamon et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><rights>Copyright © 2014 Hamon et al.; licensee BioMed Central Ltd. 2014 Hamon et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b614t-62e3e931e35618fcc09a48836031ee381b3f35d1cf38bb728a9b0849994a92033</citedby><cites>FETCH-LOGICAL-b614t-62e3e931e35618fcc09a48836031ee381b3f35d1cf38bb728a9b0849994a92033</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/PMC4089556/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4089556/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,24780,27901,27902,53766,53768,75480,75481</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24964791$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://ineris.hal.science/ineris-01862454$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Hamon, Jérémy</creatorcontrib><creatorcontrib>Jennings, Paul</creatorcontrib><creatorcontrib>Bois, Frederic Y</creatorcontrib><title>Systems biology modeling of omics data: effect of cyclosporine a on the Nrf2 pathway in human renal cells</title><title>BMC systems biology</title><addtitle>BMC Syst Biol</addtitle><description>Incorporation of omic data streams for building improved systems biology models has great potential for improving their predictions of biological outcomes. We have recently shown that cyclosporine A (CsA) strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells (RPTECs) exposed in vitro. We present here a quantitative calibration of a differential equation model of the Nrf2 pathway with a subset of the omics data we collected.
In vitro pharmacokinetic data on CsA exchange between cells, culture medium and vial walls, and data on the time course of omics markers in response to CsA exposure were reasonably well fitted with a coupled PK-systems biology model. Posterior statistical distributions of the model parameter values were obtained by Markov chain Monte Carlo sampling in a Bayesian framework. A complex cyclic pattern of ROS production and control emerged at 5 μM CsA repeated exposure. Plateau responses were found at 15 μM exposures. Shortly above those exposure levels, the model predicts a disproportionate increase in cellular ROS quantity which is consistent with an in vitro EC50 of about 40 μM for CsA in RPTECs.
The model proposed can be used to analyze and predict cellular response to oxidative stress, provided sufficient data to set its parameters to cell-specific values. Omics data can be used to that effect in a Bayesian statistical framework which retains prior information about the likely parameter values.</description><subject>Analysis</subject><subject>Antioxidants</subject><subject>Biology</subject><subject>Cyclosporine</subject><subject>Cyclosporine - pharmacokinetics</subject><subject>Cyclosporine - toxicity</subject><subject>Differential equations</subject><subject>Humans</subject><subject>Information management</subject><subject>Kidney - cytology</subject><subject>Life Sciences</subject><subject>Markov processes</subject><subject>Mathematical models</subject><subject>Medical research</subject><subject>Models, Biological</subject><subject>Monte Carlo method</subject><subject>NF-E2-Related Factor 2 - genetics</subject><subject>NF-E2-Related Factor 2 - metabolism</subject><subject>Oxidative stress</subject><subject>Proteins</subject><subject>Proteomics</subject><subject>RNA, Messenger - genetics</subject><subject>RNA, Messenger - metabolism</subject><subject>Software</subject><subject>Systems Biology - 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pharmacokinetics</topic><topic>Cyclosporine - toxicity</topic><topic>Differential equations</topic><topic>Humans</topic><topic>Information management</topic><topic>Kidney - cytology</topic><topic>Life Sciences</topic><topic>Markov processes</topic><topic>Mathematical models</topic><topic>Medical research</topic><topic>Models, Biological</topic><topic>Monte Carlo method</topic><topic>NF-E2-Related Factor 2 - genetics</topic><topic>NF-E2-Related Factor 2 - metabolism</topic><topic>Oxidative stress</topic><topic>Proteins</topic><topic>Proteomics</topic><topic>RNA, Messenger - genetics</topic><topic>RNA, Messenger - metabolism</topic><topic>Software</topic><topic>Systems Biology - methods</topic><topic>Toxicology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hamon, Jérémy</creatorcontrib><creatorcontrib>Jennings, Paul</creatorcontrib><creatorcontrib>Bois, Frederic Y</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: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC systems biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hamon, Jérémy</au><au>Jennings, Paul</au><au>Bois, Frederic Y</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Systems biology modeling of omics data: effect of cyclosporine a on the Nrf2 pathway in human renal cells</atitle><jtitle>BMC systems biology</jtitle><addtitle>BMC Syst Biol</addtitle><date>2014-06-25</date><risdate>2014</risdate><volume>8</volume><issue>1</issue><spage>76</spage><epage>76</epage><pages>76-76</pages><artnum>76</artnum><issn>1752-0509</issn><eissn>1752-0509</eissn><abstract>Incorporation of omic data streams for building improved systems biology models has great potential for improving their predictions of biological outcomes. We have recently shown that cyclosporine A (CsA) strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells (RPTECs) exposed in vitro. We present here a quantitative calibration of a differential equation model of the Nrf2 pathway with a subset of the omics data we collected.
In vitro pharmacokinetic data on CsA exchange between cells, culture medium and vial walls, and data on the time course of omics markers in response to CsA exposure were reasonably well fitted with a coupled PK-systems biology model. Posterior statistical distributions of the model parameter values were obtained by Markov chain Monte Carlo sampling in a Bayesian framework. A complex cyclic pattern of ROS production and control emerged at 5 μM CsA repeated exposure. Plateau responses were found at 15 μM exposures. Shortly above those exposure levels, the model predicts a disproportionate increase in cellular ROS quantity which is consistent with an in vitro EC50 of about 40 μM for CsA in RPTECs.
The model proposed can be used to analyze and predict cellular response to oxidative stress, provided sufficient data to set its parameters to cell-specific values. Omics data can be used to that effect in a Bayesian statistical framework which retains prior information about the likely parameter values.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>24964791</pmid><doi>10.1186/1752-0509-8-76</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Antioxidants Biology Cyclosporine Cyclosporine - pharmacokinetics Cyclosporine - toxicity Differential equations Humans Information management Kidney - cytology Life Sciences Markov processes Mathematical models Medical research Models, Biological Monte Carlo method NF-E2-Related Factor 2 - genetics NF-E2-Related Factor 2 - metabolism Oxidative stress Proteins Proteomics RNA, Messenger - genetics RNA, Messenger - metabolism Software Systems Biology - methods Toxicology |
| title | Systems biology modeling of omics data: effect of cyclosporine a on the Nrf2 pathway in human renal cells |
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