Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to the lack of experimental data. This can sometimes lead to
inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1,155 in
silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. Capacity constraints identified using experimental fitness profiles with OCCI generated novel hypotheses, while integrating thermodynamics-based metabolic flux analysis allowed prediction of metabolite concentrations.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/10443 |
| Date | 25 July 2008 |
| Creators | Yang, Laurence |
| Contributors | Cluett, William R., Mahadevan, Radhakrishnan |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
| Format | 1399984 bytes, application/pdf |
Page generated in 0.0019 seconds