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, has been developed since the 1960s by Michael Savageau, Eberhard Voit and others for the systems analysis of biochemical processes.^[1] According to Cornish-Bowden (2007) they "regarded this as a general theory of metabolic control, which includes both metabolic control analysis and flux-oriented theory as special cases".^[2]

The dynamics of a species is represented by a differential equation with the structure:

${\displaystyle {\frac {dX_{i}}{dt}}=\sum _{j}\mu _{ij}\cdot \gamma _{j}\prod _{k}X_{k}^{f_{jk}}\,}$

where X_i represents one of the n_d variables of the model (metabolite concentrations, protein concentrations or levels of gene expression). j represents the n_f biochemical processes affecting the dynamics of the species. On the other hand, ${\displaystyle \mu }$_ij (stoichiometric coefficient), ${\displaystyle \gamma }$_j (rate constants) and f_jk (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 modeled. 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 analyze several kinds of biochemical systems including metabolic networks, genetic networks and recently in cell signalling.

• Dynamical systems
• Ludwig von Bertalanffy
• Systems theory