Monte Carlo methods are heuristic algorithms that use probabilities to select an outcome
among several possible events in a given process. Monte Carlo methods are useful in …

Abstract Models of chemical kinetics that incorporate both stochasticity and diffusion are an
increasingly common tool for studying biology. The variety of competing models is vast, but …

We outline our perspective on stochastic chemical kinetics, paying particular attention to
numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose …

Background Experiments in silico using stochastic reaction-diffusion models have emerged
as an important tool in molecular systems biology. Designing computational software for …

Like engineering systems, biological systems must also operate effectively in the presence
of internal and external uncertainty—such as genetic mutations or temperature changes, for …

Spatial stochastic simulation is a valuable technique for studying reactions in biological
systems. With the availability of high‐performance computing (HPC), the method is poised to …

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an
inherent feature of biological systems. However, for many biological systems, some kinetic …

We present StochSS: Stochastic Simulation as a Service, an integrated development
environment for modeling and simulation of both deterministic and discrete stochastic …

Many systems of equations fit naturally in the form du/dt=A(u)+B(u). We may separate
convection from diffusion, x-derivatives from y-derivatives, and (especially) linear from …

We develop numerical methods for stochastic reaction-diffusion systems based on
approaches used for fluctuating hydrodynamics (FHD). For hydrodynamic systems, the FHD …