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Bioinformatics Vol. 16 no. 6 2000
Pages 534-547
© 2000 Oxford University Press

Original Paper

Systemic properties of ensembles of metabolic networks: application of graphical and statistical methods to simple unbranched pathways

Rui Alves 1,2,3 and Michael A. Savageau 1,*

1 Department of Microbiology and Immunology, University of Michigan Medical School, 5641 Medical Science Building II Ann Arbor, MI 48109-0620 USA
2 Grupo de Bioquimica e Biologia Teoricas, Instituto Rocha Cabral, Calcada Bento da Rocha Cabral 14, 1250 Lisboa, Portugal
3 Programa Gulbenkian de Doutoramentos em Biologia e Medicina, Departamento de Ensino, Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 1800 Oeiras, Portugal

Received on July 28, 1999 ; revised on November 11, 1999 ; accepted on December 3, 1999

Motivation: Mathematical models are the only realistic method for representing the integrated dynamic behavior of complex biochemical networks. However, it is difficult to obtain a consistent set of values for the parameters that characterize such a model. Even when a set of parameter values exists, the accuracy of the individual values is questionable. Therefore, we were motivated to explore statistical techniques for analyzing the properties of a given model when knowledge of the actual parameter values is lacking.

Results: The graphical and statistical methods presented in the previous paper are applied here to simple unbranched biosynthetic pathways subject to control by feedback inhibition. We represent these pathways within a canonical nonlinear formalism that provides a regular structure that is convenient for randomly sampling the parameter space. After constructing a large ensemble of randomly generated sets of parameter values, the structural and behavioral properties of the model with these parameter sets are examined statistically and classified. The results of our analysis demonstrate that certain properties of these systems are strongly correlated, thereby revealing aspects of organization that are highly probable independent of selection. Finally, we show how specification of a given behavior affects the distribution of acceptable parameter values.

Contact: Savageau{at}umich.edu

* To whom correspondence should be addressed.


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