Exploring the Landscape of Medium Chain Fatty Acid Production of a Synthetic Anaerobic Bacterial Community Using a Bottom-up Systems Biology Approach | AIChE

Exploring the Landscape of Medium Chain Fatty Acid Production of a Synthetic Anaerobic Bacterial Community Using a Bottom-up Systems Biology Approach

Authors 

Venturelli, O., University of Wisconsin-Madison
Thompson, J., University of Wisconsin-Madison
Microbes exist in complex communities in every environment on earth, leveraging their flexible metabolic capabilities to perform a wide variety of chemical transformations. Anaerobic fermentative communities perform functions of interest collectively, but the species-species interactions which govern the flow of carbon and energy within complex communities are often unknown. The use of computational models informed by experimental data is an emerging technique with applications in understanding microbiome function and designing communities to produce compounds of interest. Medium chain fatty acids (MCFA) are high-value chemicals used in many industries, from pharmaceuticals to textiles, but are currently sourced from fossil fuels. Transforming bioenergy waste streams (biofuel stillage waste) into MCFA’s using microbial bioprocessing holds tremendous potential for both reducing dependency on fossil fuels and extracting additional value-added chemicals from existing bioenergy processes. However, the ecological and molecular mechanisms driving MCFA production remain unresolved. By combining high-throughput experiments and data-driven computational modeling, we investigate the MCFA production landscape of a diverse synthetic anaerobic bacterial community. We utilize an iterative design-test-learn approach to systematically explore the relationship between community composition and MCFA production. Our data-driven approach enables the efficient discovery of high hexanoate producing communities in a landscape of >65,000 possibilities. We confirm Clostridium kluyveri as a key MCFA producer, and identify interactions shaping community dynamics. Our models reveal pairwise interactions and environmental parameters governing the favorability of MCFA production, including pH. Our systems biology approach maps the production landscape of industrially relevant compounds and elucidates key control knobs for manipulating MCFA production.