Improving Metabolic Flux Estimation of Metabolic Networks by QPSO with Penalty Function

Metabolic flux estimation through 13C trace experiment is crucial for metabolic system to quantify the intracellular metabolic fluxes. In essence, it corresponds to a constrained optimization problem, objective function of which is non-linear and non-differentiable and exist multiple local minima making this problem a special difficulty. In this paper, we propose Quantum-behaved particle swarm optimization (QPSO) with penalty function to solve 13C-based metabolic flux estimation problem. The stoichiometric constraints are transformed to an unconstrained one, by penalizing the constraints and building a single objective function, which in turn is minimized using QPSO algorithm for flux quantification. The proposed algorithm is applied to estimate the central metabolic fluxes of Corynebacterium glutamicum and compared with conventional optimization technique. Experimental results illustrated that our algorithm is capable of achieving fast convergence to good near-optima.