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Life sciences · Preprint
arXiv · September 4, 2026
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FluxDisco is a proposed computational method for discovering stoichiometric differential equations from data by constraining symbolic regression to physical laws. The work is methodological and developmental, demonstrating conceptual feasibility across unspecified physical and biological systems, but lacks empirical comparison to existing methods or quantified performance metrics.
Methodological development with computational validation. Intervention: FluxDisco: a physics-informed symbolic regression framework adapted from Monte Carlo Graph Search, constrained by known stoichiometry for flux-based ODE systems.
Framework recovers governing dynamics through interpretable equations across physical and biological systems Approach ensures physical adherence by leveraging known stoichiometry to reduce search space
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This is a methodological development paper presenting a novel algorithmic framework (FluxDisco) with evaluation across multiple systems but no clinical endpoints, patient data, or head-to-head comparison against established methods.
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Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing interpretability and predictive accuracy. However, standard methods often produce expressions that violate known physical laws. To address this, we propose FluxDisco, a physics-informed framework tailored for flux-based, stoichiometric ODE systems. By leveraging a known stoichiometry, we reduce the expression search space and ensure physical adherence. Our framework adapts the Monte Carlo Graph Search algorithm for the unique challenges associated with joint flux discovery of stoichiometric systems. We evaluate our method across a range of physical and biological systems, demonstrating its ability to accurately recover governing dynamics through interpretable equations.
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