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Open Access Highly Accessed Research article

Optimization strategies for metabolic networks

Alexandre Domingues13, Susana Vinga12 and João M Lemos13*

Author Affiliations

1 INESC-ID - R. Alves Redol 9, 1000-029 Lisboa, Portugal

2 FCM-UNL - C Mártires Pátria 130, 1169-056 Lisboa, Portugal

3 IST-UTL - Avenida Rovisco Pais, 1000 Lisboa, Portugal

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BMC Systems Biology 2010, 4:113  doi:10.1186/1752-0509-4-113

Published: 13 August 2010

Abstract

Background

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. 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.

Results

The 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.

Conclusions

It 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.