Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

Real-Time Sensing and Adaptive Computing to Elucidate Microenvironment-Induced Cell Heterogeneities and Accelerate Scalable Bioprocesses

Authors:

Davinia Salvachúa2* (davinia.salvachua@nrel.gov), Alexander S. Beliaev1, Alissa Bleem2, Marc Day2, Adam M. Feist3, Michael T. Guarnieri2, Brandon C. Knott2, Jeffrey G. Linger2, Clifford Louime4, Julianne Mueller2, Paul D. Piehowski2, Yi Wang5, Karsten Zengler3

Institutions:

1Pacific Northwest National Laboratory; 2National Renewable Energy Laboratory; 3University of California San Diego; 4University Puerto Rico Río Piedras; 5University of California–Davis

Goals

The overarching goal of this project is to predict microbial performance in large-scale bioreactors by understanding cell population performance, metabolism, and cell-to-cell heterogeneity in simulated bioreactor microenvironments. Addressing the uncertainty gap in scaling between laboratory- and industrial-scale cultivations is key to accelerate innovation in the bioeconomy and this project will do so by developing and integrating computational and experimental tools as well acquiring fundamental knowledge in microbial systems.

Abstract

The biological conversion of renewable and waste sources to fuels and chemicals is an integral component of a sustainable bioeconomy. While biomanufacturing has been successfully demonstrated at the laboratory scale for a wide range of products, only a few have successfully been produced at industrial scales. This transition between laboratory- and industrial-scale cultivations represents a ‘valley of death’ in biological processes, where uncertainties arise regarding the lack of predictability for microbial performance across scales. Mixing becomes one of the significant challenges encountered in large-scale bioreactors. In contrast to the well-mixed cultivations at the laboratory scale, large-scale cultivations are not uniformly mixed which results in uneven distribution of nutrients, pH, gas composition, and temperature. These heterogeneities impact microbial performance in an unpredictable manner, decreasing bioconversion efficiency and ultimately increasing manufacturing costs. Unless the capability to predict microbial performance in large-scale bioreactors can be realized, many biological conversion pathways will not come to fruition in the bioeconomy.

This multidisciplinary and multi-institutional project will develop and integrate experimental and computation tools and will acquire fundamental knowledge in microbial systems to address the uncertainty gap in scaling between laboratory- and industrial-scale cultivations. The group will establish a framework to predict and address the adequacy of a microbe at the beginning of the innovation cycle to mitigate risks during the development and scale-up of new bioprocesses. The team combines the strengths of metabolic engineering, fermentation science, systems biology, genome-scale modeling, automated high-throughput DNA sequencing, computational fluid dynamics, and machine learning. The knowledge gained through this work will serve as a foundation to address conversion issues at the microbial level and will extend to other biological disciplines that seek predictive understanding of multi-cellular system behavior, for example at an ecosystem level, which are relevant goals to DOE-BER.

Funding Information

This research is supported by the U.S. DOE, Office of Science, BER program.