15
An
Experimental/Computational Platform for the Analysis of Metabolite Profile
Data--Genomatica, Inc.,
5405 Morehouse Drive, Suite 210, San Diego, CA
92121; 858‑362‑8550;
Dr. Stephen
James Van Dien, Principal Investigator, svandien@genometica.com
Dr. Christophe
Heinz Schilling, Business Official, cschilling@genometica.com
DOE Grant No. DE‑FG02‑06ER84416
Amount: $747,527
Advances in
bioinformatics and genome research have generated a rapid expansion in the
availability of information at all levels of biological investigation. A computational infrastructure for systems
biology is needed to interpret this information at the whole cell level, and to
predict behavior of complex systems in response to their environment. This project will develop a combined
experimental/in silico platform to
improve the predictive capabilities of models using metabolite profile
(metabolomics) data. Specifically,
within existing metabolic modeling software, an infrastructure will be
developed to manage, visualize, and analyze metabolomics data in the context of
the genome-scale model. In Phase I,
qualitative metabolomics data was used to improve the
quality of existing genome-scale models, by finding gaps in the network and
identifying candidate genes with functions to fill these gaps. Next, quantitative concentration data was
used in conjunction with thermodynamic considerations to probe intracellular
metabolism and improve the ability to predict cell physiology. Two computational methods were implemented
and tested with available datasets to predict reaction directionalities,
identify potential bottleneck sites, and predict potential sites for
regulation. Then, the capabilities of
performing such integrated analysis will be demonstrated, using engineered E.
coli strains as a case study.
Commercial Applications and Other
Benefits as described by the awardee: A general methodology for extracting
useful biological information from metabolomics data not only should increase
modeling capabilities but also should guide rational strain engineering for the
production of chemicals and fuels from renewable feedstocks.