Bayesian Parameter Estimation for Stochastic Reaction Networks from Steady-State Observations

摘要

Stochasticity is a fundamental feature of biology at the single cell level. Quantitative experimental data ranging from microscopy to single-cell transcriptomic is continually expanding our understanding of the role of stochasticity in gene expression and other cellular processes. Computational modelling has played a fundamental role in elucidating the potential function of stochasticity in biological dynamics, creating a fertile field of interaction between the computational and life sciences (see e.g. [7]).