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John H Lagergren1,2, John T Nardini1,3, Ruth E Baker4

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Biologically-informed neural networks (BINNs) discover biological system dynamics from sparse data. This approach learns complex reaction-diffusion equations, revealing new mechanistic insights into cell behavior.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Machine Learning

Background:

  • Physics-informed neural networks (PINNs) integrate physical laws into neural networks.
  • Discovering underlying dynamics in biological systems from limited experimental data remains challenging.

Purpose of the Study:

  • Introduce Biologically-informed neural networks (BINNs) as an extension of PINNs.
  • Develop a method to learn governing partial differential equations (PDEs) from sparse biological data.
  • Uncover biologically interpretable mechanistic forms of PDE terms.

Main Methods:

  • Trained BINNs in a supervised learning framework to approximate in vitro cell biology assay data.
  • Treated diffusion and reaction terms as multilayer perceptrons (MLPs) to learn nonlinear dynamics.
  • Utilized trained MLPs to guide the selection of mechanistic PDE forms.

Main Results:

  • Successfully approximated experimental data from wound healing assays.
  • Learned nonlinear diffusion and reaction terms within a reaction-diffusion PDE framework.
  • Identified biologically interpretable mechanistic insights into system dynamics.

Conclusions:

  • BINNs effectively discover complex biological dynamics from sparse data.
  • The method provides a powerful tool for mechanistic inference in cell biology.
  • BINNs offer new insights into biological and physical mechanisms governing observed systems.