Computational Approaches in Metabolic Flux Analysis (MFA)

Speaker:
Naama Tepper, Ph.D. Thesis Seminar
Date:
Wednesday, 22.10.2014, 12:30
Place:
Taub 601
Advisor:
Prof. T. Shlomi

Cellular metabolism represents fundamental biochemical activities that enable cells to break down food nutrients, generate energy, and produce molecular building blocks required for cell replication. Metabolic processes in living cells involve thousands of enzymes, whose joint activity can be represented via metabolic networks. In these networks, nodes represent small molecules called metabolites, and edges represent biochemical reactions that transform substrate metabolites to products. A major challenge in Systems Biology and Bioinformatics is to develop methods for inferring the rate through reactions in a metabolic network (also referred to as metabolic flux) - essentially, assigning values to edges in the network. A common approach that addresses this challenge is isotope tracing. It works by feeding cells with nutrients (i.e. metabolites) that are labeled with (heavy) stable isotopes, measuring the incorporation of these isotopes within various metabolites in the network though time, and employing computational methods to interpret these metabolite labeling patterns to infer metabolic flux. Intuitively, if metabolites in the network are envisioned as a set of water pools and edges as rivers connecting the pools, isotope tracing is analogous to pouring colored water to one pool and inferring river water flows by tracking the coloring of various water pools. Considering a key observation that metabolic flux through all reactions in the network uniquely determine the labeling pattern of all metabolites, these computational methods typically search for metabolic fluxes that would give rise to metabolite labeling that optimally match experimental measurements. A major limitation of these methods is that computing metabolit labeling given a candidate flux vector is a computationally intensive task. Here, we describe a new computational method called tandemers that enables rapid simulation of metabolite labeling patterns given candidate fluxes through reactions in the network. The method is shown to provide a two-order of magnitude improvement of running time compared to state-of-the-art methods in computing special types of metabolite labeling patterns measured via a technology called tandem-mass spectrometry.The talk will provide an overview of metabolic network analysis and isotope tracing, and will assume no prior biological background.

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