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The Lost Art of Mathematical Modelling

arXiv 2301.08559 ↗First posted 19 Jan 2023q-bio.OT cs.LG nlin.AOcited by 0

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Abstract (arXiv)

We provide a critique of mathematical biology in light of rapid developments in modern machine learning. We argue that out of the three modelling activities -- (1) formulating models; (2) analysing models; and (3) fitting or comparing models to data -- inherent to mathematical biology, researchers currently focus too much on activity (2) at the cost of (1). This trend, we propose, can be reversed by realising that any given biological phenomena can be modelled in an infinite number of different ways, through the adoption of an open/pluralistic approach. We explain the open approach using fish locomotion as a case study and illustrate some of the pitfalls -- universalism, creating models of models, etc. -- that hinder mathematical biology. We then ask how we might rediscover a lost art: that of creative mathematical modelling. This article is dedicated to the memory of Edmund Crampin.

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References · 8

  1. 2020Universal Differential Equations for Scientific Machine Learningcited by 353
  2. 2016Extrapolation and learning equationscited by 110
  3. 2018Scale-free networks are rarecited by 40
  4. 2022Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networkscited by 23
  5. 2013Mapping the stereotyped behaviour of freely-moving fruit fliescited by 21
  6. 2022Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approachcited by 15
  7. 2015Evolutionary games on multilayer networks: A colloquiumcited by 6
  8. 2022The games we play: critical complexity improves machine learningcited by 1

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