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Using the Topology of Metabolic Networks to Predict Viability of Mutant Strains (F1000 Evaluation)

By: Rafael Najmanovich  
March 27 2013


Genome-wide metabolic reconstructions have numerous uses. One of which is predicting what would be the effect of knocking out one or more genes or inhibiting the function of the corresponding protein(s). Once a gene is deleted or the protein inhibited, matter flows around other available metabolic routes. Such rearrangements may drastically disrupt the production of biomass or altogether prevent it, signifying that the gene/protein is fundamental or essential. Such proteins represent potential therapeutic targets.

Flux Balance Analysis (FBA) is widely used to perform predictions of gene essentiality [1]. Wunderlich and Mirny introduced Synthetic Accessibility (SA) in 2006 as an alternative method [2] that is based solely on the topology of the metabolic network. The idea is derived from synthetic chemistry labs where the difficulty in creating a new molecule is measured as the number of synthetic steps necessary to produce the molecule starting from available starting materials. In the case of metabolic networks, the idea is to calculate the number of steps necessary to produce biomass compounds from input metabolites.

The validity of the SA approach in predicting essential genes was verified in E. coli and S. cerevisiae [2]. When a gene is knocked out or its protein inhibited in silico, the SA will necessarily either remain unchanged or increase (even infinitely) reflecting the longer path (or the absence thereof) necessary to reach output compounds using alternate metabolic routes.

SA and FBA are equivalent in terms of accuracy, around 60% and 80% respectively for E. coli and S. cereviseae [2]. We implemented both SA and FBA in our lab and independently verified these results. Furthermore we also tested B. subtilis where a metabolic network exists [3] and the full list of essential genes is known [4], obtaining a success rate of 92% with SA (equivalent to the 94% obtained by Oh et al. [3] with FBA). Wunderlich and Mirny point that the equivalent success rates between FBA and SA suggests the success of the former should be attributed mainly to network topology.

Some advantages of SA over FBA involve the simplicity of the approach (in terms of implementation and execution), not requiring any knowledge of the stoichiometry of reactions (or initial ranges for reaction rates). The latter in my opinion is a very interesting aspect of SA that allows its application to mixed networks that integrate gene regulatory networks, metabolic networks and other cellular processes that are more difficult to define in terms of stoichiometry and reaction rates.

1. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat Biotechnol 28, 245–248 (2010).

2. Wunderlich, Z. & Mirny, L. A. Using the topology of metabolic networks to predict viability of mutant strains.
Biophys J 91, 2304–2311 (2006).

3. Oh, Y.-K., Palsson, B. Ø., Park, S. M., Schilling, C. H. & Mahadevan, R. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data.
J Biol Chem 282, 28791–28799 (2007).

4. Kobayashi, K. & Kobayashi, K. Essential Bacillus subtilis genes.
Proceedings of the National Academy of Sciences 100, 4678–4683 (2003).



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