Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important
examples include gene expression and enzymatic processes in living cells. Such systems …

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

The presence of protein–DNA binding reactions often leads to analytically intractable
models of stochastic gene expression. Here we present the linear-mapping approximation …

Gene-gene interactions are crucial to the control of sub-cellular processes but our
understanding of their stochastic dynamics is hindered by the lack of simulation methods …

In recent years, moment-closure approximations (MAs) of the chemical master equation
have become a popular method for the study of stochastic effects in chemical reaction …

Genetic feedback loops in cells break detailed balance and involve bimolecular reactions;
hence, exact solutions revealing the nature of the stochastic fluctuations in these loops are …

Reaction networks are systems in which the populations of a finite number of species evolve
through predefined interactions. Such networks are found as modeling tools in many …

Quantitative mechanistic models are valuable tools for disentangling biochemical pathways
and for achieving a comprehensive understanding of biological systems. However, to be …

In the stochastic description of biochemical reaction systems, the time evolution of statistical
moments for species population counts is described by a linear dynamical system. However …

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 …