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Deposited research article

Using Topology of the Metabolic Network to Predict Viability of Mutant Strains

Zeba Wunderlich¹ and Leonid Mirny²

¹Biophysics Program, Harvard University, 77 Massachusetts Avenue, 16-361, Cambridge, MA 02139, USA.

²Harvard-MIT Division of Health Sciences & Technology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 16-343, Cambridge, MA 02139, USA.

author email corresponding author email

Genome Biology 2005, 6:P15doi:10.1186/gb-2005-6-13-p15

Published:	28 December 2005

This was the first version of this article to be made available publicly.

Subject areas: Bioinformatics, Microbiology and parasitology, Biochemistry and structural biology

Abstract

Background

Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly-disputed hypothesis. While the structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g. flux balance analysis (FBA), are more successful in predicting phenotypes of knock-out strains.

Results

We reconcile these seemingly conflicting results by showing that the topology of E. coli's metabolic network is, in fact, sufficient to predict the viability of knock-out strains with accuracy comparable to FBA on a large, unbiased dataset of mutants. This surprising result is obtained by introducing a novel topology-based measure of network transport: synthetic accessibility. We also show that other popular topology-based characteristics like node degree, graph diameter, and node usage (betweenness) fail to predict the viability of mutant strains. The success of synthetic accessibility demonstrates its ability to capture the essential properties of the metabolic network, such as the branching of chemical reactions and the directed transport of material from inputs to outputs.

Conclusions

Our results (1) strongly support a link between the topology and function of biological networks; (2) in agreement with recent genetic studies, emphasize the minimal role of flux re-routing in providing robustness of mutant strains.