A provably convergent control closure scheme for the Method of Moments of the Chemical Master Equation
Vincent WagneriD, Robin SträsseriD, Frank AllgöweriD, Nicole Erika RaddeiD
Abstract
In this article, we introduce a novel moment closure scheme based on concepts from Model Predictive Control (MPC) to accurately describe the time evolution of the statistical moments of the solution of the Chemical Master Equation (CME). The Method of Moments, a set of ordinary differential equations frequently used to calculate the first nm moments, is generally not closed since lower-order moments depend on higher-order moments. To overcome this limitation, we interpret the moment equations as a nonlinear dynamical system, where the first nm moments serve as states and the closing moments serve as control input. We demonstrate the efficacy of our approach using three example systems and show that it outperforms existing closure schemes. For polynomial systems, which encompass all mass-action systems, we provide probability bounds for the error between true and estimated moment trajectories. We achieve this by combining convergence properties of a priori moment estimates from stochastic simulations with guarantees for nonlinear reference tracking MPC. Our proposed method offers an effective solution to accurately predict the time evolution of moments of the CME, which has wide-ranging implications for many fields, including biology, chemistry, and engineering.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 13th percentile
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation
Vincent Wagner, Benjamin Castellaz, … Nicole Erika Radde·BioinformaticsPeer-reviewed·Aug 2, 2022
- The impossible challenge of estimating non-existent moments of the Chemical Master Equation
Vincent Wagner, Nicole Erika Radde·BioinformaticsPeer-reviewed·May 25, 2023
- DeepCME: A deep learning framework for solving the Chemical Master Equation
Ankit Gupta, Christoph Schwab, Mustafa Khammash·bioRxiv·Jun 5, 2021
- Some remarks on moments for stochastic chemical kinetics
Eduardo D. Sontag, Abhyudai Singh·bioRxiv·Jul 11, 2015
- Dynamical stability and accuracy of moment closure approximations for bursty gene expression with nonlinear degradation
Mohan Zhou, Chen Jia·bioRxiv·Oct 1, 2026
- Approximating Solutions of the Chemical Master Equation using Neural Networks
Augustinas Sukys, Kaan Öcal, Ramon Grima·iSciencePeer-reviewed·Aug 27, 2022