One of the biggest scientific challenges in understanding living cells at a systems level is the disconnect between simple behaviors at the cellular level (e.g., growth and movement) and the complex interactions at the molecular level. Despite enormous efforts invested into characterizing molecular interactions, their complexities make it very difficult to predict and manipulate cellular responses to environmental and genetic perturbations. This project aims to elucidate the strategies by which cells achieve "dimensional reduction", i.e., how cells themselves manage to reduce the multiplicity of molecular information and direct simple coherent behaviors. The specific topic will be on bacteria growth, with focus on how a bacterium cell determines how fast it grows and how it uses this information to coordinate metabolism and gene expression. Student and post-doctoral researchers supported by this grant will learn to make molecular and behavioral characterization, develop mathematical models to relate these measurements, and test specific hypotheses on the mechanism of dimensional reduction. The lessons learned will be integrated into the graduate-level curriculum in Quantitative Biology being developed by the PI, both for students of biological sciences to learn the mathematics of dimensional reduction and for students of physical sciences to learn different types of experiments to characterize molecular interactions and behaviors.
This research program will focus on the ppGpp signaling system underlying growth control of E. coli as a specific molecular system implementing dimensional reduction. From known qualitative features of ppGpp-based control in E. coli, a quantitative model of growth control is proposed based on both positive and negative regulation of protein synthesis via ppGpp signaling. This research program will test and elucidate the details of this ppGpp-based mode of growth control, probe its dependences on key components of the signaling and control circuit which set its few parameters, and determine its consequences on the kinetics growth transitions by E. coli, from one exponential growth state to another through periods of growth arrest. The final output of this research is a predictive, mechanistic model of bacterial growth dynamics in response to changes in the environment, including nutrient availability and antibiotics. This work is cofounded by MCB's Synthetic and Systems Biology and Physics of Living Systems in Physics in MPS.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
