Published September 14, 2016
| Version v1
Conference paper
Model simplification and process analysis of biological models
Contributors
Others:
- Biological control of artificial ecosystems (BIOCORE) ; Laboratoire d'océanographie de Villefranche (LOV) ; Observatoire océanologique de Villefranche-sur-mer (OOVM) ; Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM) ; Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)
- Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS) ; Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM) ; Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget
Description
Understanding the dynamical behavior of biological networks is complicated due to their large number of components and interactions. We present a simple method to analyse key processes for the system behavior, based on the a priori knowledge of the system trajectory and the simplification of mathematical models of these networks. The method consists of the model decomposition into biologically meaningful processes (positive or negative), whose activity or inactivity is evaluated during the time evolution of the system. The structure of the model is reduced to the core mechanisms involving active processes only. We assess the quality of the reduction by means of global relative errors and apply our method to two models of the circa-dian rhythm in Drosophila and the influence of RKIP on the ERK signaling pathway. We give some recent generalizations for a more robust approach.
Abstract
International audienceAdditional details
Identifiers
- URL
- https://hal.inria.fr/hal-01412049
- URN
- urn:oai:HAL:hal-01412049v1
Origin repository
- Origin repository
- UNICA