Inverse problem of inferring adaptive strategies from the statistics of rare events

Abstract: Biology has a diverse range of adaptation strategies to deal with changing environments that range from Darwinian multi-generational processes which play out over millions of years to within-a-lifetime learning. The underlying mechanistic basis of these strategies is highly varied and context dependent. The traditional time-consuming approach has been to distinguish these strategies with mechanistic experimental approaches. Here we propose building a mathematical framework to guide high throughput experiments that will use rare event sampling to reveal learning and adaptation strategies. We will apply our mathematical framework to experiments on drug resistance in cancer cells and in microbes. The proposed work here will (a) solve the inverse problem of inferring a broad class of adaptation strategies with finite heritability from the shape of rare-event distributions; (b) tailor proposed mathematics to specific regimes accessible in current high-throughput experiments, (c) develop novel experimental workflows for studying drug resistance in cancer cells and microbes.