CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.04 - July-Aug.
Issue No.04 - July-Aug. (2013 vol.10)
Mouli Das , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
C. A. Murthy , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
Rajat K. De , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.67
In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method, being independent of the learning parameter, exhibits improved results.
Newton-Raphson method, Learning parameters,learning parameter, Local minima, Newton-Raphson method, underdetermined problem, metabolic pathways
Mouli Das, C. A. Murthy, Rajat K. De, "An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 4, pp. 914-926, July-Aug. 2013, doi:10.1109/TCBB.2013.67