Metabolic Predictions Move into the Fast Lane

A collaborative effort among researchers in computer science, mathematics and biology at Hope College has produced a new way to model microbial metabolism. Their software takes raw DNA data from microbes and uses this information to reconstruct metabolic pathways, the routes organisms use to transform nutrients into energy.

The availability of these software resources has already sped up the efforts to create metabolic reconstructions that can lead to new applications in industry, medicine and environmental science. The research also provides valuable opportunities and learning experiences for undergraduates in interdisciplinary research in computer science, biology and mathematics.

The way organisms take in nutrients and transform them to energy and growth is an extremely complex process involving a large number of components and pathways. To make predictions on growth of an organism in a given environment, computer models based on genomic data are used to simulate what goes on in a living cell. But reconstructing metabolic pathways from information in the DNA is work intensive and there is a great need to automate this process. Though we now have the complete genomic blueprints of a large number of organisms, it is still difficult to use that data to make predictions about how these organisms grow and react to different environments.

The software package is part of the RAST (Rapid Annotation using Subsystems Technology) genome analysis service available to all researchers which includes the Model SEED, a web-based resource for high-throughput metabolic reconstruction and analysis.