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Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints

To understand how ion channels and other proteins function at the molecular and cellular levels, one must decrypt their kinetic mechanisms. Sophisticated algorithms have been developed that can be used to extract kinetic parameters from a variety of experimental data types. However, formulating mode...

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Detalles Bibliográficos
Autores principales: Salari, Autoosa, Navarro, Marco A., Milescu, Mirela, Milescu, Lorin S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Rockefeller University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5806684/
https://www.ncbi.nlm.nih.gov/pubmed/29321264
http://dx.doi.org/10.1085/jgp.201711911
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author Salari, Autoosa
Navarro, Marco A.
Milescu, Mirela
Milescu, Lorin S.
author_facet Salari, Autoosa
Navarro, Marco A.
Milescu, Mirela
Milescu, Lorin S.
author_sort Salari, Autoosa
collection PubMed
description To understand how ion channels and other proteins function at the molecular and cellular levels, one must decrypt their kinetic mechanisms. Sophisticated algorithms have been developed that can be used to extract kinetic parameters from a variety of experimental data types. However, formulating models that not only explain new data, but are also consistent with existing knowledge, remains a challenge. Here, we present a two-part study describing a mathematical and computational formalism that can be used to enforce prior knowledge into the model using constraints. In this first part, we focus on constraints that enforce explicit linear relationships involving rate constants or other model parameters. We develop a simple, linear algebra–based transformation that can be applied to enforce many types of model properties and assumptions, such as microscopic reversibility, allosteric gating, and equality and inequality parameter relationships. This transformation converts the set of linearly interdependent model parameters into a reduced set of independent parameters, which can be passed to an automated search engine for model optimization. In the companion article, we introduce a complementary method that can be used to enforce arbitrary parameter relationships and any constraints that quantify the behavior of the model under certain conditions. The procedures described in this study can, in principle, be coupled to any of the existing methods for solving molecular kinetics for ion channels or other proteins. These concepts can be used not only to enforce existing knowledge but also to formulate and test new hypotheses.
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spelling pubmed-58066842018-08-05 Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints Salari, Autoosa Navarro, Marco A. Milescu, Mirela Milescu, Lorin S. J Gen Physiol Research Articles To understand how ion channels and other proteins function at the molecular and cellular levels, one must decrypt their kinetic mechanisms. Sophisticated algorithms have been developed that can be used to extract kinetic parameters from a variety of experimental data types. However, formulating models that not only explain new data, but are also consistent with existing knowledge, remains a challenge. Here, we present a two-part study describing a mathematical and computational formalism that can be used to enforce prior knowledge into the model using constraints. In this first part, we focus on constraints that enforce explicit linear relationships involving rate constants or other model parameters. We develop a simple, linear algebra–based transformation that can be applied to enforce many types of model properties and assumptions, such as microscopic reversibility, allosteric gating, and equality and inequality parameter relationships. This transformation converts the set of linearly interdependent model parameters into a reduced set of independent parameters, which can be passed to an automated search engine for model optimization. In the companion article, we introduce a complementary method that can be used to enforce arbitrary parameter relationships and any constraints that quantify the behavior of the model under certain conditions. The procedures described in this study can, in principle, be coupled to any of the existing methods for solving molecular kinetics for ion channels or other proteins. These concepts can be used not only to enforce existing knowledge but also to formulate and test new hypotheses. The Rockefeller University Press 2018-02-05 /pmc/articles/PMC5806684/ /pubmed/29321264 http://dx.doi.org/10.1085/jgp.201711911 Text en © 2018 Salari et al. http://www.rupress.org/terms/https://creativecommons.org/licenses/by-nc-sa/4.0/This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
spellingShingle Research Articles
Salari, Autoosa
Navarro, Marco A.
Milescu, Mirela
Milescu, Lorin S.
Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints
title Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints
title_full Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints
title_fullStr Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints
title_full_unstemmed Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints
title_short Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints
title_sort estimating kinetic mechanisms with prior knowledge i: linear parameter constraints
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5806684/
https://www.ncbi.nlm.nih.gov/pubmed/29321264
http://dx.doi.org/10.1085/jgp.201711911
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