The Genome-Scale Metabolic Extreme Pathway Structure in Haemophilus influenzae Shows Significant Network Redundancy
✍ Scribed by JASON A. PAPIN; NATHAN D. PRICE; JEREMY S. EDWARDS; BERNHARD Ø. PALSSON
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 778 KB
- Volume
- 215
- Category
- Article
- ISSN
- 0022-5193
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✦ Synopsis
Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state #ux distributions achievable by the de"ned metabolic network. Genome-scale extreme pathways associated with the production of nonessential amino acids in Haemophilus in-uenzae were computed. They o!er valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal #ux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange #ux vector in H. in-uenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1 : 1 and 4 : 1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the signi"cance of metabolic extreme pathway analysis at the genome-scale.