Cooperativity and saturation in biochemical networks: a saturable formalism using Taylor series approximations.
Cooperative and saturable systems are common in molecular biology. Nevertheless, common canonical formalisms for kinetic modeling that are theoretically well justified do not have a saturable form. Modeling and fitting data from saturable systems are widely done using Hill-like equations. In practice, there is no theoretical justification for the generalized use of these equations, other than their ability to fit experimental data. Thus it is important to find a canonical formalism that is (a) theoretically well supported, (b) has a saturable functional form, and (c) can be justifiably applicable to any biochemical network. Here we derive such a formalism using Taylor approximations in a special transformation space defined by power-inverses and logarithms of power-inverses. This formalism is generalized for processes with n-variables, leading to a useful mathematical representation for molecular biology: the Saturable and Cooperative Formalism (SC formalism). This formalism provides an appropriate representation that can be used for modeling processes with cooperativity and saturation. We also show that the Hill equation can be seen as a special case within this formalism. Parameter estimation for the SC formalism requires information that is also necessary to build Power-Law models, Metabolic Control Analysis descriptions or (log)linear and Lin-log models. In addition, the saturation fraction of the relevant processes at the operating point needs to be considered. The practical use of the SC formalism for modeling is illustrated with a few examples. Similar models are built using different formalisms and compared to emphasize advantages and limitations of the different approaches.
Sorribas
Albert
A
Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida, Montserrat Roig 2, 25008-Lleida. albert.sorribas@cmb.udl.es
Hernández-Bermejo
Benito
B
Vilaprinyo
Ester
E
Alves
Rui
R
D000465:Algorithms; D001671:Biochemistry / Q000379:methods*; D003198:Computer Simulation; D008954:Models, Biological; D009097:Multienzyme Complexes / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D009097:Multienzyme Complexes; D020543:Proteome