Although engineered microbial production of natural compounds provides a promising alternative method to plant production and extraction, the process can be inefficient and ineffective in terms of time and cost. To render microbial systems profitable and viable, the process must be optimized to produce as much product as possible. To this end, this work illustrates the construction of a new probabilistic computational model to simulate the microbial production of a well-known cardioprotective molecule, resveratrol, and the implementation of the model to enhance the yield of the product in Escherichia coli. This model identified stilbene synthase as the limiting enzyme and informed the effects on changes in concentration and source of this enzyme. These parameters, when employed in a laboratory system, were able to improve the titer from 62.472 mg/L to 172.799 mg/L, demonstrating the model's ability to produce a useful simulation of a dynamic microbial resveratrol production system.

Physiological processes rely on control of cell proliferation in time and space and dysregulation of cell growth underlies pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-lab studies may be integrated with different mathematical modeling approaches to aid interpretation of the results and to enable prediction of cell behaviors, specifically in the context of cancer.