Nowadays, mathematical modeling is playing a key role in many different research fields. In
the context of system biology, mathematical models and their associated computer …

The dynamics of molecular systems is an essential tool of systems biology. It helps figuring
out what is the effect of the perturbation of a system, or what is the best dose for a drug or …

In this study, we present an adaptive multilevel Monte Carlo (AMLMC) algorithm for
approximating deterministic, real-valued, bounded linear functionals that depend on the …

Discrete-state, continuous-time Markov models are widely used in the modeling of
biochemical reaction networks. Their complexity often precludes analytic solution, and we …

In this work we present new scalable, information theory-based variational methods for the
efficient model reduction of high-dimensional deterministic and stochastic reaction networks …

In this work, we extend the hybrid Chernoff tau-leap method to the multilevel Monte Carlo
(MLMC) setting. Inspired by the work of Anderson and Higham on the tau-leap MLMC …

In biochemically reactive systems with small copy numbers of one or more reactant
molecules, the dynamics is dominated by stochastic effects. To approximate those systems …

Stochastic models of chemical systems are often subjected to uncertainties in kinetic
parameters in addition to the inherent random nature of their dynamics. Uncertainty …

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of
stochastic reaction networks characterized by having simultaneously fast and slow reaction …

In this work, we present an extension of the forward–reverse representation introduced by
Bayer and Schoenmakers (Annals of Applied Probability, 24 (5): 1994–2032, 2014) to the …