Matt DeJongh, Department of Computer Science
Aaron Best, Department of Biology

Research Area: Computational Biology - Modeling Microbial Metabolism

Research Description:

Computer models of microbial metabolism have proved useful for understanding the biochemical processes by which organisms transform substrates into biomass components and energy. These computer models consist in part of a network representing the biochemical reactions catalyzed by the enzymes encoded in an organism’s genome. The challenge of metabolic reconstruction is to create a complete reaction network suitable for systems level analysis directly from an annotated genome. Metabolic reconstruction is still largely a manual process, and to date metabolic reconstructions have been published for only a handful of organisms. Given the accelerating pace of microbial genome sequencing projects, there is an urgent need for more substantial automation of this process.

We have developed technology for automating metabolic reconstruction
that capitalizes on common aspects of metabolism shared across the diversity of microbial life. We have developed a database that contains discrete, interconnected reaction network components representing these common aspects of metabolism, and software for automatically assembling appropriate components for a particular organism based on its genome annotation. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. As the database of reaction network components grows, so does the quality of all metabolic reconstructions ssembled from the database.

We propose to take a breadth-first approach to generating substantially complete metabolic reconstructions for all sequenced microbial genomes. This approach involves an iterative process of identifying areas of metabolism encoded in microbial genomes that are not yet represented in the database, creating new reaction network components for them, and generating updated metabolic reconstructions. Each of these metabolic reconstructions will require some degree of manual refinement to push it to completion. We will develop
software tools to assist in this process and validate these tools by producing complete metabolic reconstructions for a small set of selected organisms. We will create a web site to make all of the metabolic reconstructions and the tools for refining them available to the scientific community. The database of metabolic components and software will be fully integrated with the SEED, a widely used comparative genome annotation and analysis environment (www.theseed.org).

Aaron Best,
Associate Professor of Biology

Hope College, Department of Biology
http://www.hope.edu/academic/biology/ourdepartment/profiles/aaronbest/


Matt DeJongh,
Associate Professor of Computer Science

Hope College, Department of Computer Science
http://www.hope.edu/cs/dejongh/