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  • Investigation of Metabolic Phenomena Using Information Theory

  • Posted in Research Teams :


    An organism preforms a range of metabolic reactions with the goal of survival, but the control goal(s) driving metabolism are not fully understood in this context. Our team brings together a chemical engineer, an algorithms and random structures student, and a machine learning student with an information theory professor to address the team's primary question of "does the sum of metabolic regulation converge upon the control goal of carbon uptake at maximum rates?" We will attempt to look at dynamic control goals that guide metabolic function as represented in complex sets of bioinformatics data. Our team will first address how to best analyze gene expression data as a big data problem. This project has been active from September 1, 2012 - December 31, 2016.

    A presentation of this project is avaliable here.

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