NSF/PHY-BSF: Statistical Physics of Control and Evolution in Cellular Networks

Johns Hopkins University, Baltimore MD

Investigators

Abstract

Numerous cellular processes do not follow deterministic rules; i.e., genetically identical cells can display different phenotypes even in identical environments. Such processes involve cellular decision making, in which individual cells probabilistically make choices determining their fate. One view is that the probabilistic nature of cellular decision-making originates from stochastic noise present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise of these networks. Yet extrinsic or environmental noise may be much more significant, likely governing the overall dynamics. The goal of this research project is to develop a concise theoretical framework describing the combined effect of intrinsic and extrinsic noise on the stochastic dynamics of genetic switches responsible for cellular decision-making. The PI hypothesizes that extrinsic noise not only significantly lowers the escape time from a metastable decision state, but can fundamentally change the actual escape mechanism. To elucidate the role of environmental noise on genetic circuits, the project will investigate, numerically and analytically, the interplay between intrinsic and extrinsic noise in a series of increasingly complex models of cellular decision processes. Noise-driven switching between states plays a key role in various phenomena including chemical reactions, nanomagnets, Josephson junctions, genetic switches, and protein folding. The methods investigated here can be used to study a range of physical problems. All code developed during the research activities of the project will be made available as open source software. Software protocols will be developed to ensure that all software can be easily used by other research groups. Also, all simulation and experimental datasets produced by the project will be made available for download as an "Open Science" policy, to encourage reproduction and extension of results. All of the educational curriculum will also be publicly released for other educators to use and adapt for their own courses. A sophisticated model for the contribution of extrinsic noise to cellular decision-making will be critical toward developing computational approaches to advanced synthetic biology. To develop artificial genetic programs that are as sophisticated as natural developmental ones will require a concerted effort to understand the basic physical principles by which decision-making networks operate. This project will build underlying knowledge in three specific areas. 1) The fundamental building blocks of decision making networks. 2) The response of networks to external fluctuations. 3) The impact of fluctuations on the evolution of microbial organisms. The educational component of this proposal will improve quantitative training for future genetic designers. The PI teaches a biophysically oriented systems biology undergraduate course, attempting to integrate physics-based modeling with data-driven modeling. The PI also teaches a graduate quantitative biology course for biology students. New regulatory design projects will be incorporated into both courses by creating a computational version of the BioBricks library that students can use to model regulatory designs, using the research software. The PI will also institute an outreach program for underrepresented and economically disadvantaged high-school students to learn about physics, biology, and computers using a programming "game" based on the research in the proposal. This project is being jointly supported by the Physics of Living Systems program in the Division of Physics and the Systems and Synthetic Biology Cluster in the Division of Molecular and Cellular Biosciences.