Stochastic chemical reaction networks for robustly approximating arbitrary probability distributions
Open access
Date
2020-01-01Type
- Journal Article
Abstract
We show that discrete distributions on the d-dimensional non-negative integer lattice can be approximated arbitrarily well via the marginals of stationary distributions for various classes of stochastic chemical reaction networks. We begin by providing a class of detailed balanced networks and prove that they can approximate any discrete distribution to any desired accuracy. However, these detailed balanced constructions rely on the ability to initialize a system precisely, and are therefore susceptible to perturbations in the initial conditions. We therefore provide another construction based on the ability to approximate point mass distributions and prove that this construction is capable of approximating arbitrary discrete distributions for any choice of initial condition. In particular, the developed models are ergodic, so their limit distributions are robust to a finite number of perturbations over time in the counts of molecules. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000380034Publication status
publishedExternal links
Journal / series
Theoretical Computer ScienceVolume
Pages / Article No.
Publisher
ElsevierSubject
Stochastic chemical reaction networks; Approximation; Arbitrary distributions; Detailed balance; Robustness; Molecular computingOrganisational unit
03921 - Khammash, Mustafa / Khammash, Mustafa
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