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The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis.<\/jats:p>\n                  <jats:p>Contextual parameter space was examined using four parameters external to the model\u2019s rule set. The model\u2019s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization.<\/jats:p>\n                  <jats:p>We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.<\/jats:p>","DOI":"10.1177\/0037549720975075","type":"journal-article","created":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T05:02:34Z","timestamp":1607922154000},"page":"287-296","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models"],"prefix":"10.1177","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3224-7617","authenticated-orcid":false,"given":"Chase","family":"Cockrell","sequence":"first","affiliation":[{"name":"Department of Surgery, University of Vermont, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Ozik","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nick","family":"Collier","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gary","family":"An","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Vermont, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,12,14]]},"reference":[{"key":"bibr1-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aao3612"},{"key":"bibr2-0037549720975075","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2018.00241"},{"key":"bibr3-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2017.07.016"},{"key":"bibr4-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1097\/01.CCM.0000139707.13729.7D"},{"key":"bibr5-0037549720975075","doi-asserted-by":"publisher","DOI":"10.2165\/00019053-200422140-00001"},{"key":"bibr6-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005876"},{"key":"bibr7-0037549720975075","first-page":"1","volume":"2","author":"An G","year":"2012","journal-title":"Int J Burn Trauma"},{"key":"bibr8-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1152\/ajpheart.00889.2003"},{"key":"bibr9-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e31829a6eb4"},{"key":"bibr10-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1098\/rsfs.2014.0004"},{"key":"bibr11-0037549720975075","first-page":"368","volume-title":"II international conference on antimicrobial research (ICAR2012)","volume":"1","author":"Siqueira-Batista R","year":"2012"},{"key":"bibr12-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-012-0565-9"},{"key":"bibr13-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1002\/net.1975.5.1.45"},{"key":"bibr14-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1546"},{"key":"bibr15-0037549720975075","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/BF00339943","volume":"52","author":"Hopfield JJ","year":"1985","journal-title":"Biol Cybern"},{"key":"bibr16-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1063\/1.1286997"},{"key":"bibr17-0037549720975075","first-page":"567","volume-title":"proceedings of the 15th annual conference companion on genetic and evolutionary computation","author":"Neumann F"},{"key":"bibr18-0037549720975075","author":"Cockrell C","journal-title":"bioRxiv. 2019:790394"},{"key":"bibr19-0037549720975075","author":"Saltelli A","year":"2008","journal-title":"Global sensitivity analysis: the primer"},{"key":"bibr20-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1016\/0021-9991(78)90097-9"},{"key":"bibr21-0037549720975075","author":"Saltelli A","year":"2004","journal-title":"Sensitivity analysis in practice: a guide to assessing scientific models"},{"key":"bibr22-0037549720975075","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2012.11.011"},{"key":"bibr23-0037549720975075","volume-title":"Workshop on threat anticipation: social science methods and models","author":"Macal CM"},{"key":"bibr24-0037549720975075","doi-asserted-by":"crossref","unstructured":"Calvez B, Hutzler G. 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