2015/08/03 by Yen Ting Lin, Tobias Galla · 53 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Neuroscience · Physics and Astronomy · #Artificial intelligence #Bacterial Genetics and Biotechnology #Biological system #Biology #Bursting #Computer science #Diffusion #Embedding #Evolution and Genetic Dynamics #Gene Regulatory Network Analysis #Machine learning #Markov chain #Markov process #Mathematics #Mesoscopic physics #Neuroscience #Noise (video) #Physics #Piecewise #Stationary distribution #Statistical physics #Stochastic dynamics #cond-mat.stat-mech #q-bio.MN #q-bio.QM
paper · pdf · doi:10.1098/rsif.2015.0772
published in Journal of The Royal Society Interface 13(114), 20150772 (Royal Society) · Manuscript: 11 pages, 9 figures, 1 table. Supplementary Information: 24 pages, 11 figures
arxiv created 2015/08/03 · openalex publication_date 2016/01/13 · arxiv updated 2016/01/14 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
The dynamics of short-lived mRNA results in bursts of protein production in gene regulatory networks. We investigate the propagation of bursting noise between different levels of mathematical modelling and demonstrate that conventional approaches based on diffusion approximations can fail to capture bursting noise. An alternative coarse-grained model, the so-called piecewise deterministic Markov process (PDMP), is seen to outperform the diffusion approximation in biologically relevant parameter regimes. We provide a systematic embedding of the PDMP model into the landscape of existing approaches, and we present analytical methods to calculate its stationary distribution and switching frequencies.