Stochastic Simulation of Biomolecular Networks in Dynamic Environments

TL;DR

This paper introduces the Extrande method for fast, exact stochastic simulation of biomolecular networks in dynamic environments, enabling better analysis of cellular decision-making and synthetic circuit design.

Contribution

The novel Extrande algorithm allows efficient, conditionally exact simulation of networks with time-varying inputs, overcoming limitations of existing methods.

Findings

Extrande achieves several orders-of-magnitude faster simulations.

Robustness to upstream fluctuations constrains network design in quorum sensing bacteria.

The method advances understanding of molecular systems biology and synthetic circuit engineering.

Abstract

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate, using decision-making by a large population of quorum sensing bacteria, that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.