Complex networks, comprised of individual elements that interact with each other through
reaction channels, are ubiquitous across many scientific and engineering disciplines …

Single-cell expression profiling reveals the molecular states of individual cells with
unprecedented detail. Because these methods destroy cells in the process of analysis, they …

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

Autoregulatory feedback loops are one of the most common network motifs. A wide variety of
stochastic models have been constructed to understand how the fluctuations in protein …

Moment-closure approximations have in recent years become a popular means to estimate
the mean concentrations and the variances and covariances of the concentration …

Cell-to-cell heterogeneity is driven by stochasticity in intracellular reactions and the
population dynamics. While these sources are usually studied separately, we develop an …

We study a stochastic model of protein dynamics that explicitly includes delay in the
degradation. We rigorously derive the master equation for the processes and solve it exactly …

This book is, of course, about stochastic chemical kinetics. Chemical kinetics is a prototype
of nonlinear science, since the rate of reaction is generally a nonlinear function of the …

The linear noise approximation (LNA) is a way of approximating the stochastic time
evolution of a well-stirred chemically reacting system. It can be obtained either as the lowest …

Mean field approximation is a powerful technique to study the performance of large
stochastic systems represented as n interacting objects. Applications include load balancing …