2018/03/31 by Christopher D. Bayliss, C. D. Bayliss, Christopher J. Fallaize +3
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Mathematics · #Algorithm #Approximate Bayesian computation #Artificial intelligence #Bacteria #Bayesian probability #Biology #Campylobacter jejuni #Computation #Computer science #Econometrics #Evolution and Genetic Dynamics #Gene #Genetics #Mathematics #Mutation #Mutation rate #Population #Salmonella and Campylobacter epidemiology #Selection (genetic algorithm) #Unobservable #Vibrio bacteria research studies #math.PR #msc:60J10 #msc:62F15 #msc:92D25 #q-bio.PE #q-bio.QM
paper · pdf · doi:10.1007/s11538-018-0529-9
published as Bulletin of Mathematical BiologyBulletin of Mathematical Biology, March 2019, Volume 81, Issue 3, pp 639-675 · 35 pages. The accepted version by The Bulletin of Mathematical Biology
arxiv created 2018/10/28 · openalex publication_date 2018/11/14 · arxiv updated 2019/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection-mutation model has unobservable fitness parameters, and, to estimate them, we use an Approximate Bayesian Computation algorithm. The algorithms are illustrated using in vitro data for phase variable genes of Campylobacter jejuni.