Determination of optimal metabolic pathways through a new learning algorithm

In the present article, we introduce a new method for identification of metabolic pathways in constraint based models that consider enzyme and substrate concentrations. It generates data on reaction fluxes based on biomass conservation constraint and then a set of constraints is formulated incorporating weighting coefficients corresponding to concentration of enzymes catalyzing reactions in the pathway. Finally, the rate of yield of the target metabolite, starting with a given substrate, is maximized in order to identify an optimal pathway through these weighting coefficients. In an attempt to solve this problem, we have developed a learning technique that optimizes a given objective function to find the optimal pathways. Finally, we propose a modification of the Newton Raphson method and incorporate it to our proposed methodology, which yields more relevant results from the perspective of biology.