Year: 2023
Type: article
Abstract: Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.
Source: PLoS Computational Biology
Institutions: University of Bristol, Heriot-Watt University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,655
Awards: G1309768829, G1393776984, G1633672591, G2734201728, G3314637109, G3855435662, G4019402016, BrisEngBio: From Synthetic to Engineering Biology at Bristol, G444630160, The Alan Turing Institute, G4631897923, 22ROMITIGATIONFUNDBristol, G5349518980, 21ENGBIO Reprogramming bacterial cells using whole-cell models, G6285227557, G6775709800, COMBO: CONTROL-BASED BIODESIGN OF MAMMALIAN CELL DYNAMICS, G777199097, 21EBTA: EB-AI Consortium for Bioengineered Cells & Systems (AI-4-EB), G9000132329
OpenAlex ID: W4366463408