A hierarchical dynamical low-rank algorithm for the stochastic description of large reaction networks
<|reference_start|>A hierarchical dynamical low-rank algorithm for the stochastic description of large reaction networks: The stochastic description of chemical reaction networks with the kinetic chemical master equation (CME) is important for studying biological cells, but it suffers from the curse of dimensionality: The amount of data to be stored grows exponentially with the number of chemical species and thus exceeds the capacity of common computational devices for realistic problems. Therefore, time-dependent model order reduction techniques such as the dynamical low-rank approximation are desirable. In this paper we propose a dynamical low-rank algorithm for the kinetic CME using binary tree tensor networks. The dimensionality of the problem is reduced in this approach by hierarchically dividing the reaction network into partitions. Only reactions that cross partitions are subject to an approximation error. We demonstrate by two numerical examples (a 5-dimensional lambda phage model and a 20-dimensional reaction cascade) that the proposed method drastically reduces memory consumption and shows improved computational performance and better accuracy compared to a Monte Carlo method.<|reference_end|>
@article{einkemmer2024a,
    title={A hierarchical dynamical low-rank algorithm for the stochastic
  description of large reaction networks},
    author={Lukas Einkemmer, Julian Mangott, Martina Prugger},
    journal={arXiv preprint arXiv:2407.11792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11792},
    primaryClass={math.NA cs.NA physics.bio-ph physics.comp-ph}
}