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Rational design of complex phenotype via network models

We 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...

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Detalles Bibliográficos
Autores principales: Gameiro, Marcio, Gedeon, Tomáš, Kepley, Shane, Mischaikow, Konstantin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354484/
https://www.ncbi.nlm.nih.gov/pubmed/34324484
http://dx.doi.org/10.1371/journal.pcbi.1009189
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author Gameiro, Marcio
Gedeon, Tomáš
Kepley, Shane
Mischaikow, Konstantin
author_facet Gameiro, Marcio
Gedeon, Tomáš
Kepley, Shane
Mischaikow, Konstantin
author_sort Gameiro, Marcio
collection PubMed
description We 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.
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spelling pubmed-83544842021-08-11 Rational design of complex phenotype via network models Gameiro, Marcio Gedeon, Tomáš Kepley, Shane Mischaikow, Konstantin PLoS Comput Biol Research Article We 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. Public Library of Science 2021-07-29 /pmc/articles/PMC8354484/ /pubmed/34324484 http://dx.doi.org/10.1371/journal.pcbi.1009189 Text en © 2021 Gameiro et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gameiro, Marcio
Gedeon, Tomáš
Kepley, Shane
Mischaikow, Konstantin
Rational design of complex phenotype via network models
title Rational design of complex phenotype via network models
title_full Rational design of complex phenotype via network models
title_fullStr Rational design of complex phenotype via network models
title_full_unstemmed Rational design of complex phenotype via network models
title_short Rational design of complex phenotype via network models
title_sort rational design of complex phenotype via network models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354484/
https://www.ncbi.nlm.nih.gov/pubmed/34324484
http://dx.doi.org/10.1371/journal.pcbi.1009189
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