Constrained Approximation of Effective Generators for Multiscale Stochastic Reaction Networks and Application to Conditioned Path Sampling
Simon Cotter

TL;DR
This paper introduces an improved constrained approach for approximating effective generators in multiscale stochastic reaction networks, enabling accurate, efficient analysis and conditioned path sampling without relying on the quasi-steady-state assumption.
Contribution
The authors develop a method to compute effective generators without QSSA, using eigenvalue problems, applicable to complex multiscale systems, and demonstrate its accuracy and efficiency.
Findings
Exact effective dynamics for monomolecular reactions.
Efficient eigenvalue-based computation for complex systems.
Successful conditioned path sampling using the effective generators.
Abstract
Efficient analysis and simulation of multiscale stochastic systems of chemical kinetics is an ongoing area for research, and is the source of many theoretical and computational challenges. In this paper, we present a significant improvement to the constrained approach, which is a method for computing effective dynamics of slowly changing quantities in these systems, but which does not rely on the quasi-steady-state assumption (QSSA). The QSSA can cause errors in the estimation of effective dynamics for systems where the difference in timescales between the "fast" and "slow" variables is not so pronounced. This new application of the constrained approach allows us to compute the effective generator of the slow variables, without the need for expensive stochastic simulations. This is achieved by finding the null space of the generator of the constrained system. For complex systems where…
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