Papers for

biochemical modelers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Inferring reaction rates from lagged molecular data without full state enumeration

Koopman Operator Inference for the Chemical Master Equation

Abstract: We infer kinetic rates of stochastic reaction networks from molecular-state pairs separated by a fixed time lag, without constructing the large state space of the Chemical Master Equation. Extended dynamic mode decomposition estimates the finite-time evolution of selected functions of the molecular state. A matrix logarithm then gives a projected generator whose species rows determine the rates through the known reaction stoichiometry. A finite set of functions introduces a systematic bias. We construct additional functions from the reaction channels and prove that each level of closure moves the first bias term to higher order. Spectral formulas identify the omitted modes responsible for this bias and show when the logarithm amplifies sampling error. Exact calculations and experiments on five reaction networks confirm the predicted error behavior and show when the added functions improve finite-sample rate recovery.

Sun 11 OctComputational Engineering, Finance, and Science
The gist
Understanding how molecules change in chemical reactions often requires tracking many possible states, which can be overwhelming. This work shows a way to estimate reaction speeds using data pairs from molecules observed at two times, without needing to handle all possible molecular states. They use a mathematical technique called extended dynamic mode decomposition to simplify the problem and improve accuracy by adding smart functions related to the reactions. Tests on simulated reaction networks show their method helps recover reaction rates more reliably from limited data.
Open → 2610.15937v1