Multi-state modeling of biomolecules.

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that ca...

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Autores principales:	Melanie I Stefan, Thomas M Bartol, Terrence J Sejnowski, Mary B Kennedy
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/5c3a25481f2a4012bf875ce54faecd4f
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spelling	oai:doaj.org-article:5c3a25481f2a4012bf875ce54faecd4f2021-11-25T05:40:44ZMulti-state modeling of biomolecules.1553-734X1553-735810.1371/journal.pcbi.1003844https://doaj.org/article/5c3a25481f2a4012bf875ce54faecd4f2014-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1003844https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the "specification problem") and the problem of how to use a computer to simulate the progress of the system over time (the "computation problem"). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus, BioNetGen, the Allosteric Network Compiler, and others. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim, DYNSTOC, RuleMonkey, and the Network-Free Stochastic Simulator (NFSim), and spatial simulators, including Meredys, SRSim, and MCell. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.Melanie I StefanThomas M BartolTerrence J SejnowskiMary B KennedyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 9, p e1003844 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Melanie I Stefan Thomas M Bartol Terrence J Sejnowski Mary B Kennedy Multi-state modeling of biomolecules.
description	Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the "specification problem") and the problem of how to use a computer to simulate the progress of the system over time (the "computation problem"). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus, BioNetGen, the Allosteric Network Compiler, and others. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim, DYNSTOC, RuleMonkey, and the Network-Free Stochastic Simulator (NFSim), and spatial simulators, including Meredys, SRSim, and MCell. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.
format	article
author	Melanie I Stefan Thomas M Bartol Terrence J Sejnowski Mary B Kennedy
author_facet	Melanie I Stefan Thomas M Bartol Terrence J Sejnowski Mary B Kennedy
author_sort	Melanie I Stefan
title	Multi-state modeling of biomolecules.
title_short	Multi-state modeling of biomolecules.
title_full	Multi-state modeling of biomolecules.
title_fullStr	Multi-state modeling of biomolecules.
title_full_unstemmed	Multi-state modeling of biomolecules.
title_sort	multi-state modeling of biomolecules.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/5c3a25481f2a4012bf875ce54faecd4f
work_keys_str_mv	AT melanieistefan multistatemodelingofbiomolecules AT thomasmbartol multistatemodelingofbiomolecules AT terrencejsejnowski multistatemodelingofbiomolecules AT marybkennedy multistatemodelingofbiomolecules
_version_	1718414518631006208

Multi-state modeling of biomolecules.

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