SBIR-STTR Award

Advances in bioinformatics and genome research have generated a rapid expansion in the availability of information at all levels of biological investigation. One of the goals of the of the DOE’s Genomes-to-Life Program is the development of a computational systems-biology infrastructure, in order to interpret this information at the whole cell level and to predict the behavior of these complex systems in response to their environment. This project will develop a combined experimental/in-silico simulation platform to improve the predictive capabilities of models that use metabolite profile (metabolomics) data. The approach will include a methodology for interpreting metabolomics data within the context of genome-scale metabolic models. Phase I will create a database containing intracellular metabolite concentrations of three organisms under various conditions. The database will be used first to improve existing in silico models by elucidating new metabolic pathways, and second to characterize, compare, and contrast metabolic phenotypes under the different conditions studied.

Commercial Applications and Other Benefits as described by the awardee:
The technology should leverage metabolite concentration data to expand in silico models of metabolism and improve the predictive capability of these models. In addition, a general methodology for extracting useful biological information from metabolomics data would be established. The technology would advance basic biological research, drive metabolic engineering efforts for the production of chemicals from renewable feedstocks, and assist in developing strains to sequester greenhouse gases

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