Steady-state optimization of biochemical systems through geometric programming
This paper presents an iterative strategy to address the steady-state optimization of biochemical systems. In the method we take advantage of a special class of nonlinear kinetic models known as Generalized Mass Action (GMA) models. These systems are interesting in that they allow direct merging of stoichiometric and S-system models. In most cases nonconvex steady-state optimization problems with GMA models cannot be transformed into tractable convex formulations, but an iterative strategy can be used to compute the optimal solution by solving a series of geometric programming. The presented framework is applied to several case studies and shown to the tractability and effectiveness of the method. The simulation is also studied to investigate the convergence properties of the algorithm and to give a performance comparison of our proposed and other approaches.
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