Abstract
BackgroundThe increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In this paper three control optimization methods, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network.ResultsThe results obtained show that Bi-Level optimization lead to a good approximation of the optimum attainable with the full information on the original network. Furthermore, using Pontryagin's Maximum Principle it is shown that the optimal control for the network in question, can only assume values on the extremes of the interval of its possible values.ConclusionsIt is shown that, for a class of networks in which the product that favors cell growth competes with the desired product yield, the optimal control that explores this trade-off assumes only extreme values. The proposed Bi-Level optimization led to a good approximation of the original network, allowing to overcome the limitation on the available information, often present in metabolic network models. Although the prototype network considered, it is stressed that the results obtained concern methods, and provide guidelines that are valid in a wider context.
Highlights
The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems
Current metabolic engineering processes allow to manipulate metabolic networks to improve the desired characteristics of biochemical systems [1]. These manipulations may lead to the maximization of the normal product yield or redirect the production to a flux that was residual or non-significant in the original network
For a class of networks in which the yield of the product that favors cell population growth competes with the desired product yield, with the manipulated variable affecting linearly the fluxes, it has been shown that the optimal control assumes only extreme values
Summary
The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. Current metabolic engineering processes allow to manipulate metabolic networks to improve the desired characteristics of biochemical systems [1]. These manipulations may lead to the maximization of the normal product yield or redirect the production to a flux that was residual or non-significant in the original network. The high level of uncertainty in metabolic network models knowledge makes it extremely difficult to determine what are the required manipulations needed to attain a given objective. The creation of reliable kinetic models involves the estimation of parameters, the complexity of this task increasing with the size of the network considered
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