TY - JOUR
T1 - Optimization strategies for metabolic networks
AU - Domingues, Alexandre
AU - Vinga, Susana
AU - Lemos, João M.
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/69530/PT#
The work reported in this paper was performed within the project DynaMo - Dynamical modeling, control and optimization of metabolic networks, supported by FCT (Portugal) under contract PTDC/EEA-ACR/69530/2006.
PY - 2010/8/13
Y1 - 2010/8/13
N2 - 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.
AB - 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.
KW - systems
U2 - 10.1186/1752-0509-4-113
DO - 10.1186/1752-0509-4-113
M3 - Article
C2 - 20707903
VL - 4
JO - BMC Systems Biology
JF - BMC Systems Biology
SN - 1752-0509
IS - NA
M1 - 113
ER -