Titer, rate and yield improvement
Genome-scale metabolic models of microbial metabolism can be used to predict which strain manipulations can improve the production of bioproducts of interest. Potential strain manipulations include reaction deletion, reaction addition, gene deletion or overexpression, and nutrient supplementation. Growth-coupled strain design algorithms search for a set of manipulations that must produce some amount of bioproduct in order to grow, thus ensuring a strain that is evolutionary stable.
Metabolic control analysis
Kinetic models of microbial metabolism are needed to predict the results of changes in enzyme expression, which shift metabolism from one pseudo-steady state distribution of metabolic fluxes to another. Inferring the parameters of these kinetic models from observable data is challenging, and often the available experimental data is not sufficient to determine individual enzymatic parameters with high confidence.
Our method uses techniques from modern Bayesian inference, combined with an approximate kinetic rate rule, to make efficient inference of confidence intervals in enzymatic parameters feasible.
Comprehensive understanding of cell metabolism
13C Metabolic Flux Analysis (MFA) is the gold standard for measuring intracellular metabolic fluxes. 13C MFA is based on the use of 13C labeling experiments, in which labeled carbon sources are fed to the organism under study. The ensuing metabolite labeling is combined with experimentally measured extracellular fluxes to provide a “radiography” of internal metabolic fluxes.
If coupled with genome-scale metabolic models, this method can provide a rigorous and comprehensive understanding of cell metabolism. The knowledge of these internal metabolic fluxes is valuable for understanding bottlenecks and side reactions to maximize efficiency of carbon utilization and maximizing titer, rate and yield in the conversion of substrates to target products in host strains.
Comparison and harnessing of unique metabolic capabilities
Tens of thousands of complete microbial genomic sequences have become available, with more being added to publicly available resources every day.
Our pangenomic analysis capabilities allow us to search this evolutionary space for metabolic and regulatory mechanisms that can serve as solutions to biodesign problems.
Our ability to search and model thousands of sequenced microbial genomes helps speed up bioprocess design by identifying potential solutions to complex questions that we may not be able to find using conventional design approaches. Using pan genomic analysis, we can potentially uncover new processes for integrated biomanufacturing.