Heuristic Parameter Estimation Methods for S-System Models of Biochemical Networks

Advanced NMR and mass spectroscopy permit simultaneous measurements of time-course in-vivo metabolite concentrations within an organism. These metabolic profiles are loaded with structural and kinetic information regarding the biochemical networks from which they were drawn. Extracting these information will require systematic application of both experimental and computational techniques. S-systems are non-linear dynamical models based on the power-law formalism which provide a general framework for the simulation of integrated biochemical systems exhibiting complex dynamics such those present in genetic circuits, immune and metabolic networks. In this paper we describe complementary heuristic methods for recovering the parameters of S-systems from time-course biochemical data.

[1]  Masahiro Okamoto,et al.  Design of virtual-labo-system for metabolic engineering: Development of biochemical engineering system analyzing tool-kit (BEST KIT) , 1997 .

[2]  A Sorribas,et al.  Validation and steady-state analysis of a power-law model of purine metabolism in man. , 1997, The Biochemical journal.

[3]  Eberhard O. Voit,et al.  Power-Law Approach to Modeling Biological Systems : I. Theory , 1982 .

[4]  H. Iba,et al.  Inferring a system of differential equations for a gene regulatory network by using genetic programming , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[5]  Shigenobu Kobayashi,et al.  A robust real-coded genetic algorithm using Unimodal Normal Distribution Crossover augmented by Uniform Crossover: effects of self-adaptation of crossover probabilities , 1999 .

[6]  A Sorribas,et al.  Structure identifiability in metabolic pathways: parameter estimation in models based on the power-law formalism. , 1994, The Biochemical journal.

[7]  Satoru Miyano,et al.  Inferring qualitative relations in genetic networks and metabolic pathways , 2000, Bioinform..

[8]  Jonas S. Almeida,et al.  Decoupling dynamical systems for pathway identification from metabolic profiles , 2004, Bioinform..

[9]  N. Torres.  Application of the transition time of metabolic system as a criterion for optimization of metabolic processes , 1994, Biotechnology and bioengineering.

[10]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[11]  Masahiro Okamoto,et al.  Development of a System for the Inference of Large Scale Genetic Networks , 2000, Pacific Symposium on Biocomputing.

[12]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[13]  M. Savageau.  Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. , 1969, Journal of theoretical biology.

[14]  Rui Alves,et al.  Systemic properties of ensembles of metabolic networks: application of graphical and statistical methods to simple unbranched pathways , 2000, Bioinform..