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A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference

Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive li...

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Autores principales: Tegnér, Jesper, Zenil, Hector, Kiani, Narsis A., Ball, Gordon, Gomez-Cabrero, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5052728/
https://www.ncbi.nlm.nih.gov/pubmed/27698038
http://dx.doi.org/10.1098/rsta.2016.0144
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author Tegnér, Jesper
Zenil, Hector
Kiani, Narsis A.
Ball, Gordon
Gomez-Cabrero, David
author_facet Tegnér, Jesper
Zenil, Hector
Kiani, Narsis A.
Ball, Gordon
Gomez-Cabrero, David
author_sort Tegnér, Jesper
collection PubMed
description Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.
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spelling pubmed-50527282016-11-13 A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference Tegnér, Jesper Zenil, Hector Kiani, Narsis A. Ball, Gordon Gomez-Cabrero, David Philos Trans A Math Phys Eng Sci Articles Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’. The Royal Society 2016-11-13 /pmc/articles/PMC5052728/ /pubmed/27698038 http://dx.doi.org/10.1098/rsta.2016.0144 Text en © 2016 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Tegnér, Jesper
Zenil, Hector
Kiani, Narsis A.
Ball, Gordon
Gomez-Cabrero, David
A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
title A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
title_full A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
title_fullStr A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
title_full_unstemmed A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
title_short A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
title_sort perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5052728/
https://www.ncbi.nlm.nih.gov/pubmed/27698038
http://dx.doi.org/10.1098/rsta.2016.0144
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