Stochasticity is a key characteristic of intracellular processes such as gene regulation and
chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is …

Background Stochastic modeling and simulation provide powerful predictive methods for the
intrinsic understanding of fundamental mechanisms in complex biochemical networks …

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

Stochastic kinetic methods are currently considered to be the most realistic and elegant
means of representing and simulating the dynamics of biochemical and biological networks …

Motivation: Over the last years it has become evident that stochastic effects play an important
role in biological processes leading to an increase in stochastic modelling attempts. Despite …

The discrete and random occurrence of chemical reactions far from thermodynamic
equilibrium, and low copy numbers of chemical species, in systems biology necessitate …

Continuous-time Markov chain models are often used to describe the stochastic dynamics of
networks of reacting chemical species, especially in the growing field of systems biology …

Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can
have large effects on the response of biochemical networks. This is particularly true for …

Background Stochastic simulation can be used to illustrate the development of biological
systems over time and the stochastic nature of these processes. Currently available …

The dynamics of stochastic reaction networks within cells are inevitably modulated by factors
considered extrinsic to the network such as, for instance, the fluctuations in ribosome copy …