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

This book, as with others in the series, is intended to provide supplementary material for
courses in probability or stochastic processes. The mathematical focus is on counting …

Epidemic models have become a routinely used tool to inform policy on infectious disease.
A particular interest at the moment is the use of computationally intensive inference to …

Modeling and Inverse Problems in the Presence of Uncertainty collects recent research—
including the authors' own substantial projects—on uncertainty propagation and …

We consider stochastically modeled chemical reaction systems with mass-action kinetics
and prove that a product-form stationary distribution exists for each closed, irreducible …

We show how to extend a recently proposed multilevel Monte Carlo approach to the
continuous time Markov chain setting, thereby greatly lowering the computational complexity …

We present an efficient finite difference method for the computation of parameter sensitivities
that is applicable to a wide class of continuous time Markov chain models. The estimator for …

The chemostat is classically represented, at large population scale, as a system of ordinary
differential equations. Our goal is to establish a set of stochastic models that are valid at …

This is a textbook for advanced undergraduate students and beginning graduate students in
applied mathematics. It presents the basic mathematical foundations of stochastic analysis …

In this paper, we present a mathematical description for excitable biological membranes, in
particular neuronal membranes. We aim to model the (spatio-) temporal dynamics, eg, the …