title: Power-law modeling based on least-squares minimization criteria.
abstract: The power-law formalism has been successfully used as a modeling tool in many applications. The resulting models, either as Generalized Mass Action or as S-systems models, allow one to characterize the target system and to simulate its dynamical behavior in response to external perturbations and parameter changes. The power-law formalism was first derived as a Taylor series approximation in logarithmic space for kinetic rate-laws. The especial characteristics of this approximation produce an extremely useful systemic representation that allows a complete system characterization. Furthermore, their parameters have a precise interpretation as local sensitivities of each of the individual processes and as rate-constants. This facilitates a qualitative discussion and a quantitative estimation of their possible values in relation to the kinetic properties. Following this interpretation, parameter estimation is also possible by relating the systemic behavior to the underlying processes. Without leaving the general formalism, in this paper we suggest deriving the power-law representation in an alternative way that uses least-squares minimization. The resulting power-law mimics the target rate-law in a wider range of concentration values than the classical power-law. Although the implications of this alternative approach remain to be established, our results show that the predicted steady-state using the least-squares power-law is closest to the actual steady-state of the target system.
authors.affiliation: Departamento de Física Fundamental, Universidad Nacional de Educación a Distancia, Madrid, Spain. bhernand@apphys.uned.es
authors.forename: B
authors.initials: B
authors.lastname: Hernández-Bermejo
authors.forename: V
authors.initials: V
authors.lastname: Fairén
authors.forename: A
authors.initials: A
authors.lastname: Sorribas
mesh_terms: D004798:Enzymes / Q000378:metabolism*; D006801:Humans; D007700:Kinetics; D016018:Least-Squares Analysis; D008954:Models, Biological
chemical_list: D004798:Enzymes