Rational design of complex phenotype via network models

dc.contributor.authorGameiro, Marcio
dc.contributor.authorGedeon, Tomáš
dc.contributor.authorKepley, Shane
dc.contributor.authorMischaikow, Konstantin
dc.date.accessioned2022-09-15T20:29:16Z
dc.date.available2022-09-15T20:29:16Z
dc.date.issued2021-07
dc.description.abstractWe demonstrate a modeling and computational framework that allows for rapid screening of thousands of potential network designs for particular dynamic behavior. To illustrate this capability we consider the problem of hysteresis, a prerequisite for construction of robust bistable switches and hence a cornerstone for construction of more complex synthetic circuits. We evaluate and rank most three node networks according to their ability to robustly exhibit hysteresis where robustness is measured with respect to parameters over multiple dynamic phenotypes. Focusing on the highest ranked networks, we demonstrate how additional robustness and design constraints can be applied. We compare our results to more traditional methods based on specific parameterization of ordinary differential equation models and demonstrate a strong qualitative match at a small fraction of the computational cost.en_US
dc.identifier.citationGameiro M, Gedeon T, Kepley S, Mischaikow K (2021) Rational design of complex phenotype via network models. PLoS Comput Biol 17(7): e1009189. https://doi.org/10.1371/journal. pcbi.1009189en_US
dc.identifier.issn1553-7358
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/17157
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.rightscc-byen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectrational design complex phenotype network modelsen_US
dc.titleRational design of complex phenotype via network modelsen_US
dc.typeArticleen_US
mus.citation.extentfirstpage1en_US
mus.citation.extentlastpage25en_US
mus.citation.issue7en_US
mus.citation.journaltitlePLOS Computational Biologyen_US
mus.citation.volume17en_US
mus.data.thumbpage6en_US
mus.identifier.doi10.1371/journal.pcbi.1009189en_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentMathematical Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US

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