Determination of causal connectivities of species in reaction networks

We present an approach to the determination of causal connectivities and part of the kinetics of complex reaction systems. Our approach is based on analytical and computational methods for studying the effects of a pulse change of concentration of a chemical species in a reaction network, either at equilibrium or in a nonequilibrium stationary state. Such disturbances generally propagate through a few species, depending on the values of the kinetic coefficients, before being broadened and dissipated. This short range gives a local probe of the kinetics and connectivity of the reaction network. The range of propagation also indicates species to perturb in further experiments. From piecing together these local connectivities, the global structure of the network can be constructed. The experimental design allows deduction of both reaction orders and rate constants in many cases. An example of the usefulness of the approach is illustrated on a model of a part of glycolysis.

[1]  Michel Boudart,et al.  Kinetics of chemical processes , 1968 .

[2]  J. Lunsford Advances in catalysis , 1980 .

[3]  J. Ross,et al.  Oscillations and control features in glycolysis: numerical analysis of a comprehensive model. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[4]  D. Irvine,et al.  Efficient solution of nonlinear ordinary differential equations expressed in S-system canonical form , 1990 .

[5]  J. Ross,et al.  Operational procedure toward the classification of chemical oscillators , 1991 .

[6]  Adam P. Arkin,et al.  Statistical Construction of Chemical Reaction Mechanisms from Measured Time-Series , 1995 .

[7]  J. Ross,et al.  Genetic-algorithm selection of a regulatory structure that directs flux in a simple metabolic model. , 1995, Biophysical journal.

[8]  J. Ross,et al.  A Test Case of Correlation Metric Construction of a Reaction Pathway from Measurements , 1997 .

[9]  J. Ross,et al.  Nonlinear kinetics and new approaches to complex reaction mechanisms. , 1999, Annual review of physical chemistry.

[10]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

[11]  Federico Morán,et al.  Response theory for random channel kinetics in complex systems. Application to lifetime distributions of active intermediates , 2000 .

[12]  J. Ross,et al.  Application of Genetic Algorithm to Chemical Kinetics: Systematic Determination of Reaction Mechanism and Rate Coefficients for a Complex Reaction Network , 2001 .