Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

John H. Lagergren; John T. Nardini; Ruth E. Baker; Matthew J. Simpson; Kevin B. Flores

doi:10.1371/journal.pcbi.1008462

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

John H. Lagergren, John T. Nardini, Ruth E. Baker, Matthew J. Simpson, Kevin B. Flores

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62 Scopus citations

Abstract

Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). By allowing the diffusion and reaction terms to be multilayer perceptrons (MLPs), the nonlinear forms of these terms can be learned while simultaneously converging to the solution of the governing PDE. Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the biological and physical mechanisms that govern the dynamics of the observed system. The method is evaluated on sparse real-world data from wound healing assays with varying initial cell densities [2].

Original language	English
Article number	e1008462
Journal	PLoS Computational Biology
Volume	16
Issue number	11
DOIs	https://doi.org/10.1371/journal.pcbi.1008462
State	Published - Dec 1 2020
Externally published	Yes

Funding

This material was based upon work partially supported by the National Science Foundation Directorate for Mathematical and Physical Sciences under grant DMS-1638521 to the Statistical and Applied Mathematical Sciences Institute and the Division of Integrative Organismal Systems under grant IOS-1838314 to KBF, and in part by National Institute of Aging grant R21AG059099 to KBF. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. REB is a Royal Society Wolfson Research Merit Award holder and also acknowledges the Biotechnology and Biological Sciences Research Council for funding via grant no. BB/R000816/1. MJS acknowledges the Australian Research Council (DP200100177). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Funders	Funder number
National Science Foundation
National Institute on Aging	R21AG059099
Directorate for Mathematical and Physical Sciences	DMS-1638521
Division of Integrative Organismal Systems	IOS-1838314
Biotechnology and Biological Sciences Research Council	BB/R000816/1
Royal Society
Australian Research Council	DP200100177

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Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

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