Learning equations from biological data with limited time samples

作     者：Nardini, John T. Lagergren, John H. Hawkins-Daarud, Andrea Curtin, Lee Morris, Bethan Rutter, Erica M. Swanson, Kristin R. Flores, Kevin B. 

作者机构：Department of Mathematics North Carolina State University RaleighNC United States Statistical and Applied Mathematical Sciences Insitute DurhamNC United States Mathematical NeuroOncology Laboratory Precision Neurotherapeutics Innovation Program Mayo Clinic PhoenixAZ United States Centre for Mathematical Medicine and Biology University of Nottingham Nottingham United Kingdom Department of Applied Mathematics University of California Merced MercedCA United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2020年

核心收录：

主　　题：Learning systems 

摘      要：Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets, however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data is sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions. Copyright © 2020, The Authors. All rights reserved.