Data-driven modeling of molecular mechanisms underlying cellular quiescence

About This Grant

Project Abstract In order to survive nutrient deprivation and fluctuating environments, microbes adopt quiescent (non-growing) physiological states. Some quiescent states, such as bacterial spores, are remarkably resilient and can re- initiate growth after surviving centuries as a dormant spore. Compared to the exponential growth phase of bacteria, relatively little is known about quiescent states. For example, quantitative relations between growth rates and cellular physiology have been established for the exponential growth phase, but no equivalent relations have been formulated for non-growing states. To that end, the central goal of the proposed work is to uncover such relations for non-growing states through the development of data-driven, predictive biophysical models of molecular mechanisms which characterize cellular quiescence. These models will incorporate data from a variety of experimental studies, which will improve our current understanding of the interplay between certain physiological traits and molecular processes that have been observed in non-growing states. This proposal builds on our previous works on rate-limiting molecular processes in exponentially growing unicellular organisms, in which data-informed biophysical models allowed us to deduce previously unrecognized growth laws and invariant quantities. In a similar spirit, here we aim to deduce quantitative relations characterizing cellular quiescence, to provide a new framework for understanding non-growing states. Over the next five years, we will focus on three main research thrusts, all motivated by experimental observations: (i) regulation of protein synthesis and ribosome abundance in quiescent states; (ii) the influence of pH on critical metabolic reactions; and (iii) the effect of protein composition on germination rates of bacterial spores and its implications for population growth. Our biophysical models and computational analyses will be validated against data provided by experimental collaborators. This five-year plan will enable us to lay the foundations for our long- term vision to uncover principles of dormancy and deliver predictive models that can be applied in the laboratory. The proposed work will provide molecular-scale insights into the physical processes that support cellular quiescence, and will contribute to the discovery of new methods to target non-growing cells. In particular, antibiotic-tolerant persister cells present one such application.