Towards a quantitative understanding of dynamic metabolic systems

Cells – from simple bacteria to mammalian cells – are constantly exposed to fluctuating environments in terms of, for example, nutrient availability. Such changes typically require cellular adaptations, which are commonly administered by complex molecular sensing and regulatory machineries. Although many of the players and mechanisms involved are known, we are still far from an actual quantitative understanding of the complex orchestration of these processes. As model systems, we are currently studying the dynamic regulation processes controlling central carbon metabolism in E. coli and S. cerevisiae upon change in nutrient availability.

Here, we follow a systems biology approach combining experimental analyses and mathematical modeling efforts. On the experimental side, we use two different but complementary approaches: As a top-down approach, we analyze and extract mechanistic insight from global omics data. In a bottom-up approach, we study the properties of single cells through microscopic analyses and flow cytometry. By integrating both population averaged and single cell data into computational models, we seek to derive a quantitative understanding of the regulatory processes which explicitly account for phenotypic heterogeneity in clonal populations.