Open Access

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

Genome Biology20056:P15

DOI: 10.1186/gb-2005-6-13-p15

Received: 23 December 2005

Published: 28 December 2005



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


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


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

Additional data files

Additional data files 1, 2 and 3.


Authors’ Affiliations

Biophysics Program, Harvard University
Harvard-MIT Division of Health Sciences & Technology, Massachusetts Institute of Technology


© BioMed Central Ltd 2005