Abstract

Originating from the Luria–Delbrück experiment in 1943, fluctuation analysis (FA) has been well developed to demonstrate random mutagenesis in microbial cell populations and infer mutation rates. Despite the remarkable progress in its theory and applications, FA often faces difficulties in the computation perspective, due to the lack of appropriate simulators. Existing simulation algorithms are usually designed specifically for particular scenarios, thus their applications may be largely restricted. There is a pressing need for more flexible simulators that rely on minimum model assumptions and are highly adaptable to produce data for a wide range of scenarios. In this study, we propose SimuBP, a simulator of population dynamics and mutations based on branching processes. SimuBP generates data based on a general two-type branching process, which is able to mimic the real cell proliferation and mutation process. Through simulations under traditional FA assumptions, we demonstrate that the data generated by SimuBP follow expected distributions, and exhibit high consistency with those generated by two alternative simulators. The most impressive feature of SimuBP lies in its flexibility, which enables the simulation of data analogous to real fluctuation experiments. We demonstrate the application of SimuBP through examples of estimating mutation rates.