Bridging the gap between mechanistic biological models and machine learning surrogates

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...

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Bibliographic Details
Published in:PLoS computational biology 2023-04, Vol.19 (4), p.e1010988-e1010988
Main Authors: Gherman, Ioana M, Abdallah, Zahraa S, Pang, Wei, Gorochowski, Thomas E, Grierson, Claire S, Marucci, Lucia
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Bioengineering
Biological activity
Biological models (mathematics)
Biological systems
Biology and Life Sciences
Complex systems
Complexity
Computer and Information Sciences
Computer applications
Computer Simulation
Dynamical systems
Industrial applications
Learning algorithms
Machine Learning
Medicine and Health Sciences
Metabolism
Models, Biological
Molecular biology
Neural networks
Ordinary differential equations
Partial differential equations
Personal computers
Physical Sciences
Research and Analysis Methods
Review
Simulation
Simulation methods
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1010988