Genome-scale metabolic model development of Methylobacter

Chemical and Biomolecular Engineering
Faculty Advisor
Rajib Saha
Contact Email
Potential UCARE Research Position?
Start Date
Paid or Volunteer
Hours Per Week

Questions regarding the ecological and functional characteristics of natural microbial consortium under dynamic environmental conditions can be addressed using comprehensive mathematical models and ‘omics’-based data analysis.

The long-term goal of the project is to use a multifaceted approach similar to the ‘biobricks’ method of standardizing biological parts [1] to identify and characterize the roles and interactions of individual strains and species in any given microbial consortium. As proof of concept, we will characterize the methane cycling consortium [2] in Lake Washington. To this end, we will fist construct a genome-scale model (GSM) for Methylobacter, one of the significant species of the methane recycling Lake Washington consortium, using a semi-automatic pipeline in the SEED database. The GSM will subsequently be curated, gap-filled, and incorporated with the estimated growth and non-growth associated ATP (GAM & NGAM) requirements. Next, we plan to characterize its metabolic capabilities on a system-level and, subsequently, apply computational strain design techniques to propose nonintuitive genetic intervention strategies to overproduce useful products, while the strain is growing on methane.

 We expect this project to provide a detailed understanding of Methylobacter metabolism to elucidate the key metabolic drivers that underpin bio-production of desired chemicals. This project will provide multidisciplinary training and education for one undergraduate student as a part of the UCARE program. The student will perform the simulations for GSM construction and subsequently apply computational strain engineering techniques for succinate overproduction.


 1. Shetty RP, Endy D, Knight TF, Jr. (2008) Engineering BioBrick vectors from BioBrick parts. J Biol Eng 2:5. doi:10.1186/1754-1611-2-5 2. Yu Z, Krause SM, Beck DA, Chistoserdova L (2016) A Synthetic Ecology

Perspective: How Well Does Behavior of Model Organisms in the Laboratory Predict Microbial Activities in Natural Habitats? Front Microbiol 7:946.


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