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Interestingly, system (2) is similar to a previously presented "minimal reaction network" [ 12] modelling activation of the epidermal growth factor receptor (EGFR).
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The proposed recursive MILP approach for identifying all the minimal reaction sets in metabolic network is shown in Figure 3.
The proposed recursive MILP method is then employed to identify all the minimal reaction sets from this network.
Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive.
We have previously proposed a graph theory based approach for identifying minimal reaction set in metabolic networks [ 18].
There are a total of 256 minimal reaction sets for this metabolic network.
In this paper, we proposed a graph theory augmented recursive MILP approach to identify all the minimal reaction sets in a metabolic network.
In this paper, we propose a graph theory based recursive math programming approach to identify all the minimal reaction sets in the metabolic network.
Multiple gap-filling is an extension of gap-filling where an FBA model is generated by simultaneously computing minimal completions of the reactions in the reaction network, the metabolites for the biomass reaction, the nutrients and the secretions.
Here, we identify all the minimal reaction sets from the E. coli metabolic network so as to meet cellular objective ν max biomass ≥ 0.7 g/gDW∙h for a glucose uptake rate of 10 mmol /gDW∙h.
The three minimal reaction sets identified in the E. coli metabolic network are shown in Figure 5.
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