Copeland, W.B., Bartley, B.A., Chandran, D., Galdzicki, M., Kim, K.H., Sleight, S.C., Maranas, C.D. and Sauro, H.M. (2012) Computational Tools for Metabolic Engineering. Metabolic Engineering, 14, 270-280.
http://www.sciencedirect.com/science/article/pii/S1096717612000250
http://dx.doi.org/10.1016/j.ymben.2012.03.001

has been cited by the following article:

• TITLE: Large-Scale Kinetic Parameter Identification of Metabolic Network Model of E. coli Using PSO

  AUTHORS: Mohammed Adam Kunna, Tuty Asmawaty Abdul Kadir, Aqeel S. Jaber, Julius B. Odili

  KEYWORDS: Metabolic Engineering, Metabolic Network, Dynamic Model, Sensitivity Analysis, Optimization and Estimation

  JOURNAL NAME: Advances in Bioscience and Biotechnology, Vol.6 No.2, February 15, 2015

  ABSTRACT: In metabolic network modelling, the accuracy of kinetic parameters has become more important over the last two decades. Even a small perturbation in kinetic parameters may cause major changes in a model’s response. The focus of this study is to identify the kinetic parameters, using two distinct approaches: firstly, a One-at-a-Time Sensitivity Measure, performed on 185 kinetic parameters, which represent glycolysis, pentose phosphate, TCA cycle, gluconeogenesis, glycoxylate pathways, and acetate formation. Time profiles for sensitivity indices were calculated for each parameter. Seven kinetic parameters were found to be highly affected in the model response; secondly, particle swarm optimization was applied for kinetic parameter identification of a metabolic network model. The simulation results proved the effectiveness of the proposed method.