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Multi-experiment equation learning (ME-EQL) improves how continuum models are derived from agent-based modeling (ABM) simulations. This approach enhances model generalizability and interpretability for complex biological systems.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • Agent-based modeling (ABM) is crucial for studying self-organizing biological systems but is computationally demanding and lacks analytical tractability.
  • Equation learning (EQL) derives continuum models from ABM data, yet requires extensive simulations per parameter set, limiting generalizability.

Purpose of the Study:

  • To extend EQL to Multi-experiment equation learning (ME-EQL) for improved model generalizability and interpretability.
  • To introduce and evaluate two novel ME-EQL methods: one-at-a-time ME-EQL (OAT ME-EQL) and embedded structure ME-EQL (ES ME-EQL).

Main Methods:

  • Developed OAT ME-EQL to learn individual models per parameter set, connected via interpolation.
  • Developed ES ME-EQL to construct a unified model library across parameter sets.
  • Applied ME-EQL methods to a noisy birth-death mean-field model and an on-lattice ABM of birth, death, and migration.

Main Results:

  • Both ME-EQL methods significantly reduced relative error in parameter recovery from ABM simulations.
  • OAT ME-EQL demonstrated superior generalizability across the parameter space.
  • The methods successfully learned continuum models from complex spatial agent-based models.

Conclusions:

  • ME-EQL enhances the efficiency and generalizability of deriving continuum models from ABM data.
  • These methods offer a powerful approach for analyzing complex biological systems and interpreting simulation results.
  • Future work can leverage ME-EQL for broader applications in computational biology and systems science.