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Research Abstracts Online
January 2010 - March 2011

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University of Minnesota Twin Cities
College of Biological Sciences
College of Science and Engineering
BioTechnology Institute

PI: Friedrich Srienc, Associate Fellow

Elementary Mode Analysis of Biochemical Networks

Evolution has supplied biological organisms with a highly coupled network of hundreds of enzyme-catalyzed reactions. Recent advances in molecular biology techniques such as the polymerase chain reaction (PCR) have provided means of altering the topography of these reaction networks. For instance, enzymatic activities can be blocked by disrupting certain genes or new reactions can be introduced through expression of recombinant genes. Analysis of native and recombinant networks has been simplified by a number of theoretical tools. One such method is elementary mode analysis. An elementary mode is the simplest, balanced combination of substrates, products, and reactions operating at steady state. Computational bioinformatics tools are currently available to determine all possible balanced elementary modes for reaction networks of limited size and complexity. Such results can give information on product formation pathways and can determine basic parameters like maximum theoretical yield. Knowledge of all elementary modes provides a rigorous basis for rational design of efficient strains. Large, highly branched networks with multiple products and substrates often have numerous elementary modes. The determination of all possible modes for these complex metabolic networks faces significant computational difficulties. Current analysis of our experimental systems is limited by computational time and memory requirements. Therefore, these researchers, in collaboration with computational experts, are developing more efficient algorithms with the application of parallel programming to address this challenging problem.

Group Members

Johnathan Gorke, Graduate Student
Pedro Pena, Research Associate
Tyler P. Price, Graduate Student
Daniel P. Rouse, Graduate Student
Pornkamol (Apple) Unrean, Graduate Student