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

Accelerating Carbon-Negative Biomanufacturing Through Systems-Level Biology and Genome Optimization

Authors:

Fungmin Eric Liew1* (eric.liew@lanzatech.com), Sruti Dammalapati1, Christine Brown1, Hannah Ranft1, Nicholas Fackler1, Heidi Schindel1, Shilpa Nagaraju1, Ching Leang1, Michael Köpke1, Michael C. Jewett2,3

Institutions:

1LanzaTech; 2Northwestern University–Evanston; 3Stanford University–Palo Alto

Goals

To develop high-throughput biosystems design tools that are applied to multiple testbeds for carbon-negative biomanufacturing.

Abstract

In the face of escalating climate change, there is a pressing need for innovative strategies to mitigate carbon (C) emissions. LanzaTech stands at the forefront of this initiative, employing chemoautotrophic gas fermenting microorganisms to convert carbon dioxide (CO2) into valuable C-based materials. This multidisciplinary project aims to enhance the metabolic efficiency of CO2-utilizing biosystems through genome optimization and the integration of machine learning (ML) techniques. This approach employs cutting-edge genomic tools to iteratively knockout gene clusters towards engineering strains streamlined for the rigorous conditions of industrial fermentation. Given the abundant possible permutations, researchers leverage ML to inform the experimental strategies and strategically guide the knockout efforts. To this end, researchers have trained ML models using transcriptomic datasets to discern intricate patterns and relationships between genes and their functions. These models will be leveraged towards identifying contiguous genetic regions for targeted reduction. Strains generated from this process will not only enhance the understanding of gene functionality, they also enable the construction of genotype-phenotype associations through downstream screening (Sastry et al. 2019; Sastry et al. 2021). These efforts will significantly advance in silico models and streamline the development of microbial strains for industrial-scale applications.

References

Sastry, A.V., et al. 2019. “The Escherichia Coli Transcriptome Mostly Consists of Independently Regulated Modules,” Nature Communications 10, 5536. DOI:10.1038/s41467-019-13483-w.

Sastry, A.V., et al. 2021. I”ndependent Component Analysis Recovers Consistent Regulatory Signals from Disparate Datasets,” PLOS Computational Biology 17(2), e1008647. DOI:10.1371/journal.pcbi.1008647.

Funding Information

This material is based upon work supported by the DOE, Office of Science, BER program, Genomic Science program under award no. DE-SC0023278.