Emergence of mutants in bacterial populations: a simulation-based approach for parameter inference in extended Luria & Delbru&x0308;ck scenarios

Abstract

0Bacterial mutation rates are traditionally inferred from phenotypic data using fluctuation assays. Mutation rate computation from these assays relies on a mathematical model describing the emergence of mutants during population growth. However, this standard model relies on restrictive assumptions that are often transgressed in real biological setups. Key assumptions include equal growth rate of the wild-type and the mutant population, no cell death during growth phase, and no post-growth sampling. Although several refined mathematical models have been proposed over the last decades to circumvent some of these assumptions, none can fully account for the complex ecological scenarios frequently observed in the lab or in the wild, such as combination of cell death and unknown fitness effect of the mutation, or non-constant death rates. In this work, we propose to replace these standard mathematical models by a stochastic simulation framework, which can circumvent all these assumptions. We provide a fast and accurate implementation of this stochastic model, and use it to perform simulation-based, likelihood-free parameter inference through the Metropolis-Hastings algorithm. Beyond mutation rate estimation, our approach can simultaneously infer additional biological parameters, provided enough experimental replicates are available. We benchmarked our proposed method against the most recent tools (rSalvador, Flan, and mlemur) in a broad range of biological scenarios with randomly sampled parameter sets. We found that that our method has similar accuracy than existing tools in simple conditions where these tools work, but also reliably estimates mutation rate and some other parameters in more complex conditions where other tools do not work. Overall, our proposed method can be extended to arbitrary biological complications, as long as these can be efficiently simulated.