Research Aims

Our research seeks to extend existing computational modeling techniques to more flexible and informative environments. This centers on extensions of traditional metabolic modeling methods of cyanobacteria to:

  • Improve strain design through optimization and interrogation of collateral metabolic effects,
  • Extend genome-based metabolic reconstructions to account for physiological constraints,
  • Develop empirically grounded in silico optimization techniques for multi-objective systems, and
  • Create frameworks for uniquely interacting cell types in realistic environments.

Our intent is to take existing well-vetted metabolic modeling techniques and introduce flexibility to more complex environments and biological platforms while encouraging more comprehensive cross-study data usage. We believe these in silico approaches can be paramount to more effective and better designed in vivo experimentation, data analysis, and design.

Current Outcomes

Metabolic Modeling of T. erythraeum

We have created a genome-scale reconstruction of Trichodesmium erythraeum, a crucial organism in the pelagic nitrogen and carbon cycles. Its unique mechanism of oxygen sequestration from irreversible inhibition of nitrogenase through differentiation into two cell types is also a model for microbial consortia. We have used the genome-scale reconstruction to create two unique metabolic models for each cell type in T. erythraeum and have used Flux Balance Analysis related methods to interrogate the metabolic controls on colony development.

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 Optimizing Synechococcus sp. PCC 7002

In addition to T. erythraeum, we are investigating the utility of Synechococcus sp. PCC 7002 for carbon neutral chemical manufacture. Unfortunately, insertion of genes into a strain does not immediately predict productivity. Instead, collateral effects of gene insertion, like activation of alternative pathways, low substrate availability, material imbalances, and redox imbalances, need to be mitigated in order to create an industrial strain. We are using computational and experimental techniques to quantify these ulterior effects for correction.

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