Living organisms rely on adaptive mechanisms to respond to environmental fluctuations, particularly changes in nutrient availability. In bacteria such as Escherichia coli, these adaptations involve complex regulatory processes that enable transitions between different carbon sources. While such mechanisms are well characterized at the population level, recent studies have highlighted that individual cells within the same population and genotype can display strikingly heterogeneous behaviors (Barthe et al., 2020). This phenotypic variability manifests most clearly during substrate transitions, where lag phases and growth rates may differ substantially between cells.

Understanding the origins and functional roles of this heterogeneity is essential both for advancing our fundamental knowledge of microbial physiology and for improving the predictive accuracy of biotechnological applications that rely on bacterial growth on mixed substrates. The MoHME project addresses this challenge by developing a stochastic model of E. coli 's glucose to xylose adaptation. Unlike deterministic approaches, a stochastic framework makes it possible to capture the diversity of cellular responses and to identify the regulatory factors most responsible for variability. Our goal is to build a model that not only reproduces observed heterologous behaviors during substrate shifts but also provides a mechanistic understanding of the processes that govern them.

This project is supported by INSA's grants for societal concerns. It primarily addresses Global Health and Bioengineering through the study of cellular heterogeneity and its impact on growth, stress tolerance, and antibiotic resistance, while also contributing to Sustainable Energy by informing bioprocess optimization.