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

Recent experimental developments in genome-wide RNA quantification hold considerable
promise for systems biology. However, rigorously probing the biology of living cells requires …

The presence of protein–DNA binding reactions often leads to analytically intractable
models of stochastic gene expression. Here we present the linear-map** approximation …

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 …

Complex molecular details of transcriptional regulation can be coarse-grained by assuming
that reaction waiting times for promoter-state transitions, the mRNA synthesis, and the …

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 …

We study individual-based dynamics in finite populations, subject to randomly switching
environmental conditions. These are inspired by models in which genes transition between …

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

Inside individual cells, stochastic expression drives random fluctuations in gene product
copy numbers, which corrupts functioning of both natural and synthetic genetic circuits …