Mathematical models are routinely calibrated to experimental data, with goals ranging from
building predictive models to quantifying parameters that cannot be measured. Whether or …

The key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications, computational …

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

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate
individual cell interactions and microenvironmental dynamics. Unfortunately, the high …

Bayesian and non-Bayesian moment-based inference methods are commonly used to
estimate the parameters defining stochastic models of gene regulatory networks from noisy …

We present a method for estimating parameters in stochastic models of biochemical reaction
networks by fitting steady-state distributions using Wasserstein distances. We simulate a …

Ordinary differential equation models have become a standard tool for the mechanistic
description of biochemical processes. If parameters are inferred from experimental data …

Background Discrete-state stochastic models have become a well-established approach to
describe biochemical reaction networks that are influenced by the inherent randomness of …

Almost all fields of science rely upon statistical inference to estimate unknown parameters in
theoretical and computational models. While the performance of modern computer hardware …

Based on the theory of stochastic chemical kinetics, the inherent randomness of biochemical
reaction networks can be described by discrete-state continuous-time Markov chains …