An estimate is worth about a thousand experiments: using order-of-magnitude estimates to identify cellular engineering targets

Abstract

Biotechnological processes use microbes to convert abundant molecules, such as glucose, into high-value products, such as pharmaceuticals, commodity and fine chemicals, and energy. However, from the outset of the development of a new bioprocess, it is difficult to determine the feasibility, expected yields, and targets for engineering. In this review, we describe a methodology that uses rough estimates to assess the feasibility of a process, approximate the expected product titer of a biological system, and identify variables to manipulate in order to achieve the desired performance. This methodology uses estimates from literature and biological intuition, and can be applied in the early stages of a project to help plan future engineering. We highlight recent literature examples, as well as two case studies from our own work, to demonstrate the use and power of rough estimates. Describing and predicting biological function using estimates guides the research and development phase of new bioprocesses and is a useful first step to understand and build a new microbial factory.

Computational Modeling and Evolutionary Implications of Biochemical Reactions in Bacterial Microcompartments

“…This suggests that the O2 exclusion property of carboxysomes may be a more general property of BMCs than previously considered. Through computational modeling of BMCs, we can relate results to experimental data, probe theoretical evolutionary pressures, and gain deeper understanding of these systems [21,77]. Given the rapidly advancing power of computation and AI in addition to new experimental techniques, we anticipate many new breakthroughs in the coming decades harnessing the capabilities of BMCs that could have major benefits for society and the environment.…”

“…With these models we can explore the parameter space that BMCs may functionally occupy, test evolutionary pressures leading to formation, and help focus the direction of future experimental work. Ultimately, these quantitative understandings could enable the de novo design and engineering of novel BMCs for diverse functions [21,22].…”