SysBio-SR: Benchmarking Symbolic Regression for Trajectory Fidelity and Mechanism Recovery in Systems Biology

Symbolic regression seeks to discover interpretable governing equations for dynamical systems from data. In systems biology, however, accurately reproducing system trajectories does not necessarily imply recovery of the underlying reaction mechanisms. Existing benchmarks evaluate numerical accuracy, expression complexity, or equation-level similarity, but not agreement between recovered reactions and reference mechanisms in biological systems. We introduce SysBio-SR, a benchmark of 198 literature-based biological ODE systems with reaction annotations spanning mass action, Michaelis–Menten, Hill, and complex kinetics. Trajectory fidelity is evaluated across all systems under in-distribution reconstruction and out-of-distribution (OOD) extrapolation, while mechanism recovery is assessed on the subset of 161 systems. Of 11 state-of-the-art symbolic-discovery methods, only 3 support mechanism recovery, as most output unconstrained expressions that cannot map to biological mechanisms. Joint analysis across systems highlights how severely trajectory-based evaluation overestimates true mechanism recovery. Motivated by these findings, we develop KineSelect, a framework that searches candidate reaction topologies under biologically grounded kinetic laws to select optimal mechanisms that balance trajectory fidelity with structural parsimony. Compared with the strongest baseline, KineSelect improves aggregate OOD extrapolation $R^2$ by 19.2% and overall reaction F1 score by 14.3%.