(353e) Origins and Consequences of Chromosomal Heterogeneity during Bacterial Growth – Implications for the Design and Analysis of Engineered Cellular Functions

A grand challenge in synthetic biology is the development of engineered biological functions that operate robustly under a variety of conditions. Both the design and analysis of such systems is complicated by the fact that individual cells within a population often exhibit distinct phenotypes, particularly under the conditions of rapid growth under which such systems are typically evaluated. Although variations in plasmid copy number are known to contribute to cell-to-cell variability in plasmid-encoded functions, variations in chromosome copy number may also be significant, yet we lack a quantitative understanding and framework for incorporating these effects into the design and analysis of engineered cellular functions. To begin addressing this need, we present a quantitative framework for characterizing and predicting chromosome copy number dynamics and heterogeneity over a range of  growth conditions. Strikingly, we observed experimentally that chromosome copy number magnitude and heterogeneity vary substantially over growth regimes typically used to evaluate synthetic biology “programs” and biosynthetic pathways. Moreover, chromosome copy number variation was not always reflected in readily apparent changes in microbial growth. These observations informed our development of a mechanistic agent-based computational simulation that facilitates both understanding and predicting these ensemble effects. This core model is readily extended to incorporate novel phenomena that are of fundamental biological interest or that impact microbial engineering, which we investigated by considering the influence of plasmid/gene load on chromosomal dynamics. Our analysis enables one to estimate the magnitude of such effects using simple growth-curve data as inputs to the simulation. This analysis and computational tool will enable synthetic biology researchers to better interpret the results of their experiments and could ultimate enable the design of engineered systems that function more robustly across various cells within a population.