title: Estimating single-cell lag times via a Bayesian scheme.
abstract: Network models offer computationally efficient tools for estimating the variability of single-cell lag phases. Currently, optical methods for estimating the variability of single-cell lag phases use single-cell inocula and are technically challenging. A Bayesian network model incorporating small uncertain inocula addresses these limitations.
authors.affiliation: Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom. pradeep.malakar@bbsrc.ac.uk
authors.forename: P K
authors.initials: PK
authors.lastname: Malakar
authors.forename: G C
authors.initials: GC
authors.lastname: Barker
mesh_terms: D001419:Bacteria / Q000254:growth & development*; D001499:Bayes Theorem; D013997:Time Factors