Biochemical systems theory: Difference between revisions
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where X<sub>i</sub> represents one of the n<sub>d</sub> variables of the model (metabolite concentrations, protein concentrations or levels of gene expression). j represents the n<sub>f</sub> biochemical processes affecting the dynamics of the specie. On the other hand, <math>\mu</math><sub>ij</sub> (stoichiometric coefficient), <math>\gamma</math><sub>j</sub> (rate constants) and f<sub>ik</sub> (kinetic orders) are two different kinds of parameters defining the dynamics of the system. | where X<sub>i</sub> represents one of the n<sub>d</sub> variables of the model (metabolite concentrations, protein concentrations or levels of gene expression). j represents the n<sub>f</sub> biochemical processes affecting the dynamics of the specie. On the other hand, <math>\mu</math><sub>ij</sub> (stoichiometric coefficient), <math>\gamma</math><sub>j</sub> (rate constants) and f<sub>ik</sub> (kinetic orders) are two different kinds of parameters defining the dynamics of the system. | ||
The principal difference of | The principal difference of power-law [[model]]s with respect to other ODE models used in biochemical systems is that the kinetic orders can be non-integer numbers. A kinetic order can have even negative value when inhibition is modelled. In this way, power-law models have a higher flexibility to reproduce the non-linearity of biochemical systems. | ||
Models using power-law expansions have been used during the last 35 years to model and analyse several kinds of biochemical systems including metabolic networks, genetic networks and recently in cell signalling. | Models using power-law expansions have been used during the last 35 years to model and analyse several kinds of biochemical systems including metabolic networks, genetic networks and recently in cell signalling. | ||
== See also == | == See also == | ||
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Biochemical systems theory is a mathematical modelling framework for biochemical systems, based on ordinary differential equations (ODE), in which biochemical processes are represented using power-law expansions in the variables of the system. This framework, which became known as Biochemical Systems Theory, is developed since the 1960s by Michael Savageau and other groups for systems analysis of biochemical processes.[1] They regard this as a general theory of metabolic control, which includes both metabolic control analysis and flux-oriented theory as special cases.[2]
Representation
The dynamics of a specie is represented by a differential equation with the structure:
where Xi represents one of the nd variables of the model (metabolite concentrations, protein concentrations or levels of gene expression). j represents the nf biochemical processes affecting the dynamics of the specie. On the other hand, <math>\mu</math>ij (stoichiometric coefficient), <math>\gamma</math>j (rate constants) and fik (kinetic orders) are two different kinds of parameters defining the dynamics of the system.
The principal difference of power-law models with respect to other ODE models used in biochemical systems is that the kinetic orders can be non-integer numbers. A kinetic order can have even negative value when inhibition is modelled. In this way, power-law models have a higher flexibility to reproduce the non-linearity of biochemical systems.
Models using power-law expansions have been used during the last 35 years to model and analyse several kinds of biochemical systems including metabolic networks, genetic networks and recently in cell signalling.
See also
References
- ↑ Biochemical Systems Theory, an introduction.
- ↑ Athel Cornish-Bowden, Metabolic control analysis FAQ, website 18 April 2007.
Literature
Books:
- M.A. Savageau, Biochemical systems analysis: a study of function and design in molecular biology, Reading, MA, Addison–Wesley, 1976.
- E.O. Voit (ed), Canonical Nonlinear Modeling. S-System Approach to Understanding Complexity, Van Nostrand Reinhold, NY, 1991.
- E.O. Voit, Computational Analysis of Biochemical Systems. A Practical Guide for Biochemists and Molecular Biologists, Cambridge University Press, Cambridge, U.K., 2000.
- N.V. Torres and E.O. Voit, Pathway Analysis and Optimization in Metabolic Engineering, Cambridge University Press, Cambridge, U.K., 2002.
Scientific articles:
- M.A. Savageau, Biochemical systems analysis: I. Some mathematical properties of the rate law for the component enzymatic reactions in: J. Theor. Biol. 25, pp. 365-369, 1969.
- M.A. Savageau, Development of fractal kinetic theory for enzyme-catalysed reactions and implications for the design of biochemical pathways in: Biosystems 47(1-2), pp. 9-36, 1998.
- M.R. Atkinson et al, Design of gene circuits using power-law models, in: Cell 113, pp. 597–607, 2003.
- F. Alvarez-Vasquez et al, Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae, Nature 27, pp. 433(7024), pp. 425-30, 2005.
- J. Vera et al, Power-Law models of signal transduction pathways in: Cellular Signalling doi:10.1016/j.cellsig.2007.01.029), 2007.
- Eberhart O. Voit, Applications of Biochemical Systems Theory, 2006.