Metabolic Flux Analysis

Metabolic Flux Analysis


Building and employing models for learning form integrated systems biology data is an important capability within the ABF. Pathway genome databases are developed for host strains and used in the building of metabolic models for quantitative analysis of in vivo carbon fluxes in metabolic networks, i.e. intracellular activities of enzymes and pathways.

13C Metabolic Flux Analysis (MFA) involves culturing strains on carbon sources labeled at specific positions with 13C followed by GC-MS metabolomics to determine the labeling pattern of metabolites. The labeling data is compared to a computational simulation using a flux model that combines isotopomer and metabolite balancing representing the investigated metabolic network. The flux estimation is based on minimizing the deviation between the measured and the simulated labeling data. In combination with experimentally determined extracellular fluxes, 13C MFA determines the crucial fluxes of substrates through intermediates to target molecules in the metabolic network. This is valuable for understanding bottlenecks and side reactions to maximize efficiency of carbon utilization and hence maximize TRY (titer rate and yield) in the conversion of substrates to target products in our ABF host strains.

Genome-scale metabolic models (GEMs) summarize the known metabolic information in a mathematically defined reaction network. A key objective of utilizing GEMs for metabolic engineering is to improve the production of target products in our ABF host strains by providing genetic targets to be deleted, down-regulated or over-expressed. One method employed in the ABF is OptForce, which identifies potential gene targets by classifying metabolic reactions with regard to necessary changes in flux (i.e., increase, decrease, or zero) to meet a pre-specified overproduction target. The efficiency and quality of engineering interventions predicted by OptForce and related methods depends on quality multi-omics, cell mass and other meta-data for the wild-type strain.

The modeling capabilities are a tremendous benefit to the ABF with regard to incorporating and interpreting the multi-omics and meta data to provide actionable genetic engineering targets for the next round of DBTL.

Development and use of models requires the time of domain experts. The types of isotopically labeled substrates are limited and expensive. Biological replicates within very well designed experiments are crucial to getting statistically robust predictions.

References and Additional Information: 

Dreyfuss, J. M., Zucker, J. D., Hood, H. M., Ocasio, L. R., Sachs, M. S., & Galagan, J. E. (2013). Reconstruction and validation of a genome-scale metabolic model for the filamentous fungus Neurospora crassa using FARM. PLoS computational biology9(7), e1003126.

Henry, C. S., Bernstein, H. C., Weisenhorn, P., Taylor, R. C., Lee, J. Y., Zucker, J., & Song, H. S. (2016). Microbial community metabolic modeling: a community data‐driven network reconstruction. Journal of cellular physiology, 231(11), 2339-2345.

Reilly, M. C., Kim, J., Lynn, J., Simmons, B. A., Gladden, J. M., Magnuson, J. K., & Baker, S. E. (2018). Forward genetics screen coupled with whole-genome resequencing identifies novel gene targets for improving heterologous enzyme production in Aspergillus niger. Applied microbiology and biotechnology, 1-11.


Pacific Northwest National Laboratory

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