Abstract
Recent genomic analyses on the cellular metabolic network show that reaction flux across enzymes are diverse and exhibit power-law behavior in its distribution. While intuition might suggest that the reactions with larger fluxes are more likely to be lethal under the blockade of its catalysing gene products or gene knockouts, we find, by in silico flux analysis, that the lethality rarely has correlations with the flux level owing to the widespread backup pathways innate in the genome-wide metabolism of Escherichia coli. Lethal reactions, of which the deletion generates cascading failure of following reactions up to the biomass reaction, are identified in terms of the Boolean network scheme as well as the flux balance analysis. The avalanche size of a reaction, defined as the number of subsequently blocked reactions after its removal, turns out to be a useful measure of lethality. As a means to elucidate phenotypic robustness to a single deletion, we investigate synthetic lethality in reaction level, where simultaneous deletion of a pair of nonlethal reactions leads to the failure of the biomass reaction. Synthetic lethals identified via flux balance and Boolean scheme are consistently shown to act in parallel pathways, working in such a way that the backup machinery is compromised.
Original language | English |
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Pages (from-to) | 401-411 |
Number of pages | 11 |
Journal | Journal of Theoretical Biology |
Volume | 237 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2005 Dec 21 |
Externally published | Yes |
Bibliographical note
Funding Information:The authors thank J.L. Reed and B.Ø. Pålsson for helpful comments. This work is supported by the KOSEF Grants No. R14-2002-059-01000-0 in the ABRL program and the MOST Grant No. M1 03B500000110.
Keywords
- Flux balance analysis
- Metabolic network
- Synthetic lethality
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
- General Biochemistry,Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics