A low-rank complexity reduction algorithm for the high-dimensional kinetic chemical master equation

• Published in: Journal of computational physics Vol. 503; p. 112827
• Main Authors: Einkemmer, Lukas, Mangott, Julian, Prugger, Martina
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.04.2024
• Subjects: Chemical master equation; Complexity reduction; Dynamical low-rank approximation; High-dimensional problems; Reaction networks; Dynamical low-rank approximation; Complexity reduction; Reaction networks; Chemical master equation; High-dimensional problems
• ISSN: 0021-9991; 1090-2716; 1090-2716
• DOI: 10.1016/j.jcp.2024.112827

It is increasingly realized that taking stochastic effects into account is important in order to study biological cells. However, the corresponding mathematical formulation, the chemical master equation (CME), suffers from the curse of dimensionality and thus solving it directly is not feasible for most realistic problems. In this paper we propose a dynamical low-rank algorithm for the CME that reduces the dimensionality of the problem by dividing the reaction network into partitions. Only reactions that cross partitions are subject to an approximation error (everything else is computed exactly). This approach, compared to the commonly used stochastic simulation algorithm (SSA, a Monte Carlo method), has the advantage that it is completely noise-free. This is particularly important if one is interested in resolving the tails of the probability distribution. We show that in some cases (e.g. for the lambda phage) the proposed method can drastically reduce memory consumption and run time and provide better accuracy than SSA. •We propose a low-rank algorithm that does not rely on single orbital basis functions.•The proposed approach is better suited for biological/chemical reaction networks.•We demonstrate better accuracy and improved efficiency compared to SSA for some problems.•The proposed complexity reduction algorithm is noise free (in contrast to SSA).•We demonstrate that our algorithm accurately resolves the tails of the distribution (in contrast to SSA).