Multiscale Stochastic Modeling To Meet the Needs Of Modern Microbial Biology

PROJECT SUMMARY Cell proliferation is fundamental to life. Yet, proliferation is noisy at every scale. While molecular noise is unavoidable, one might think that it gets averaged out and that proliferation becomes deterministic at the cell and population scales. However, technologies like microfluidic culturing and DNA barcoding are revealing that noise at the cellular and population scales is pronounced. For example, cell size fluctuations can last for many generations, and microbial population sizes can fluctuate over many orders of magnitude. These striking and fundamental statistical features are poorly understood, and new theoretical tools are needed to determine their origins. Moreover, to disentangle whether noise is inherited from smaller scales or arises newly at each scale, these tools must be multiscale. The goal of my group for the next five years is to develop predictive stochastic descriptions of microbial proliferation that bridge the molecular, cellular, and population scales. Specifically, we will develop stochastic descriptions that predict (i) the effects of molecular noise on microbial cell size control, (ii) the effects of cell division noise on microbial population dynamics, and (iii) the statistics of microbial population dynamics in fluctuating, clinically relevant environments. We will test these predictions against new data from two experimental collaborators: one using microfluidic culturing to investigate cell size homeostasis in bacteria, and one using DNA barcoding to investigate bacterial population dynamics in liquid culture and in realistic environments such as tumors. To achieve this goal, I will draw upon my theoretical physics background, my record of successful experimental collaborations, and my demonstrated expertise in multiscale stochastic modeling. The outcome will be to transform my group into leaders in theoretical multiscale population biology. This work will answer fundamental questions about microbial population dynamics and have important implications for our understanding of the tumor microbiome and antibiotic resistance.