Parametric Sensitivity Analysis for Models of Reaction Networks within Interacting Compartments
<|reference_start|>Parametric Sensitivity Analysis for Models of Reaction Networks within Interacting Compartments: Models of reaction networks within interacting compartments (RNIC) are a generalization of stochastic reaction networks. It is most natural to think of the interacting compartments as ``cells'' that can appear, degrade, split, and even merge, with each cell containing an evolving copy of the underlying stochastic reaction network. Such models have a number of parameters, including those associated with the internal chemical model and those associated with the compartment interactions, and it is natural to want efficient computational methods for the numerical estimation of sensitivities of model statistics with respect to these parameters. Motivated by the extensive work on computational methods for parametric sensitivity analysis in the context of stochastic reaction networks over the past few decades, we provide a number of methods in the RNIC setting. Provided methods include the (unbiased) Girsanov transformation method (also called the Likelihood Ratio method) and a number of coupling methods for the implementation of finite differences. We provide several numerical examples and conclude that the method associated with the ``Split Coupling'' provides the most efficient algorithm. This finding is in line with the conclusions from the work related to sensitivity analysis of standard stochastic reaction networks. We have made all of the Matlab code used to implement the various methods freely available for download.<|reference_end|>
@article{anderson2024parametric,
    title={Parametric Sensitivity Analysis for Models of Reaction Networks within
  Interacting Compartments},
    author={David F. Anderson and Aidan S. Howells},
    journal={arXiv preprint arXiv:2408.09208},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09208},
    primaryClass={q-bio.MN cs.NA math.NA math.PR q-bio.QM}
}