Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data
Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the com...
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| creator | Tong, Xuming Chen, Jinghang Miao, Hongyu Li, Tingting Zhang, Le |
| description | Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data. |
| doi_str_mv | 10.1371/journal.pone.0141295 |
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Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0141295</identifier><identifier>PMID: 26535589</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Agent-based models ; Algorithms ; Animals ; Biodiversity ; Computer simulation ; Differential equations ; Encyclopedias ; Experimental data ; Greedy algorithms ; Humans ; Immune system ; Infections ; Influenza ; Information science ; Loess ; Mathematical models ; Mathematical problems ; Models, Immunological ; Optimization techniques ; Ordinary differential equations ; Parameter estimation ; Partial differential equations ; Probability ; Regression models ; Researchers</subject><ispartof>PloS one, 2015-11, Vol.10 (11), p.e0141295-e0141295</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Tong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Tong et al 2015 Tong et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-49d1bfec8521eecb9c8c714c25068914d0faba4700c89c68b95673811183b9523</citedby><cites>FETCH-LOGICAL-c758t-49d1bfec8521eecb9c8c714c25068914d0faba4700c89c68b95673811183b9523</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/PMC4633145/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633145/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26535589$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tong, Xuming</creatorcontrib><creatorcontrib>Chen, Jinghang</creatorcontrib><creatorcontrib>Miao, Hongyu</creatorcontrib><creatorcontrib>Li, Tingting</creatorcontrib><creatorcontrib>Zhang, Le</creatorcontrib><title>Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. 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One</addtitle><date>2015-11-04</date><risdate>2015</risdate><volume>10</volume><issue>11</issue><spage>e0141295</spage><epage>e0141295</epage><pages>e0141295-e0141295</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26535589</pmid><doi>10.1371/journal.pone.0141295</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Agent-based models Algorithms Animals Biodiversity Computer simulation Differential equations Encyclopedias Experimental data Greedy algorithms Humans Immune system Infections Influenza Information science Loess Mathematical models Mathematical problems Models, Immunological Optimization techniques Ordinary differential equations Parameter estimation Partial differential equations Probability Regression models Researchers |
| title | Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data |
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