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

This book aims to provide an introduction to dynamical systems with multiple time scales. As
in any overview book, several topics are covered only quite briefly. My aim was to focus on …

Gene expression in individual cells is highly variable and sporadic, often resulting in the
synthesis of mRNAs and proteins in bursts. Such bursting has important consequences for …

Intracellular reaction rates depend on concentrations and hence their levels are often
regulated. However classical models of stochastic gene expression lack a cell size …

The stochasticity of gene expression is manifested in the fluctuations of messenger
ribonucleic acid and protein copy numbers within a cell lineage over time. While data of this …

Stochastic gene expression dynamics can be modelled either discretely or continuously.
Previous studies have shown that the mRNA or protein number distributions of some simple …

Classical gene expression models assume exponential switching time distributions between
the active and inactive promoter states. However, recent experiments have shown that many …

While the steady-state behavior of stochastic gene expression with auto-regulation has been
extensively studied, its time-dependent behavior has received much less attention. Here …

Gene-expression bimodality, as a potential mechanism generating phenotypic cell diversity,
can enhance the survival of cells in a fluctuating environment. Previous studies have shown …

Transcription factors (TFs) interact with a multitude of binding sites on DNA and partner
proteins inside cells. We investigate how nonspecific binding/unbinding to such decoy …