Predicting synthetic rescues in metabolic networks.
An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme-encoding gene results in a non-viable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic network-based identification of compensatory rescue effects may open new avenues for genetic interventions.
Motter
Adilson E
AE
Department of Physics and Astronomy and Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA. motter@northwestern.edu
Gulbahce
Natali
N
Almaas
Eivind
E
Barabási
Albert-László
AL
D018533:Biomass; D002952:Citric Acid Cycle / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics / Q000254:growth & development / Q000378:metabolism; D017353:Gene Deletion; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks; D005798:Genes, Bacterial; D020043:Genes, Essential; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008954:Models, Biological; D008957:Models, Genetic; D011237:Predictive Value of Tests; D049490:Systems Biology