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Distilling identifiable and interpretable dynamic models from biological data

Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open pr...

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Autores principales: Massonis, Gemma, Villaverde, Alejandro F., Banga, Julio R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615316/
https://www.ncbi.nlm.nih.gov/pubmed/37851682
http://dx.doi.org/10.1371/journal.pcbi.1011014
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author Massonis, Gemma
Villaverde, Alejandro F.
Banga, Julio R.
author_facet Massonis, Gemma
Villaverde, Alejandro F.
Banga, Julio R.
author_sort Massonis, Gemma
collection PubMed
description Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occam’s razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable.
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spelling pubmed-106153162023-10-31 Distilling identifiable and interpretable dynamic models from biological data Massonis, Gemma Villaverde, Alejandro F. Banga, Julio R. PLoS Comput Biol Research Article Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occam’s razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable. Public Library of Science 2023-10-18 /pmc/articles/PMC10615316/ /pubmed/37851682 http://dx.doi.org/10.1371/journal.pcbi.1011014 Text en © 2023 Massonis 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
Massonis, Gemma
Villaverde, Alejandro F.
Banga, Julio R.
Distilling identifiable and interpretable dynamic models from biological data
title Distilling identifiable and interpretable dynamic models from biological data
title_full Distilling identifiable and interpretable dynamic models from biological data
title_fullStr Distilling identifiable and interpretable dynamic models from biological data
title_full_unstemmed Distilling identifiable and interpretable dynamic models from biological data
title_short Distilling identifiable and interpretable dynamic models from biological data
title_sort distilling identifiable and interpretable dynamic models from biological data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615316/
https://www.ncbi.nlm.nih.gov/pubmed/37851682
http://dx.doi.org/10.1371/journal.pcbi.1011014
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