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BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts

Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compil...

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Autores principales: Poole, William, Pandey, Ayush, Shur, Andrey, Tuza, Zoltan A., Murray, Richard M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060376/
https://www.ncbi.nlm.nih.gov/pubmed/35442944
http://dx.doi.org/10.1371/journal.pcbi.1009987
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author Poole, William
Pandey, Ayush
Shur, Andrey
Tuza, Zoltan A.
Murray, Richard M.
author_facet Poole, William
Pandey, Ayush
Shur, Andrey
Tuza, Zoltan A.
Murray, Richard M.
author_sort Poole, William
collection PubMed
description Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions.
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spelling pubmed-90603762022-05-03 BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts Poole, William Pandey, Ayush Shur, Andrey Tuza, Zoltan A. Murray, Richard M. PLoS Comput Biol Research Article Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions. Public Library of Science 2022-04-20 /pmc/articles/PMC9060376/ /pubmed/35442944 http://dx.doi.org/10.1371/journal.pcbi.1009987 Text en © 2022 Poole 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
Poole, William
Pandey, Ayush
Shur, Andrey
Tuza, Zoltan A.
Murray, Richard M.
BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts
title BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts
title_full BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts
title_fullStr BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts
title_full_unstemmed BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts
title_short BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts
title_sort biocrnpyler: compiling chemical reaction networks from biomolecular parts in diverse contexts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060376/
https://www.ncbi.nlm.nih.gov/pubmed/35442944
http://dx.doi.org/10.1371/journal.pcbi.1009987
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