Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Principal process analysis of biological models

Stefano Casagranda 1, 2 Suzanne Touzeau 3, 1, 2 Delphine Ropers 4 Jean-Luc Gouzé 1, 2
1 BIOCORE - Biological control of artificial ecosystems
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, LOV - Laboratoire d'océanographie de Villefranche
4 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
Abstract : Background: Understanding the dynamical behaviour of biological systems is challenged by their large number of components and interactions. While efforts have been made in this direction to reduce model complexity, they often prove insufficient to grasp which and when model processes play a crucial role. Answering these questions is fundamental to unravel the functioning of living organisms. Results: We design a method for dealing with model complexity, based on the analysis of dynamical models by means of Principal Process Analysis. We apply the method to a well-known model of circadian rhythms in mammals. The knowledge of the system trajectories allows us to decompose the system dynamics into processes that are active or inactive with respect to a certain threshold value. Process activities are graphically represented by Boolean and Dynamical Process Maps. We detect model processes that are always inactive, or inactive on some time interval. Eliminating these processes reduces the complex dynamics of the original model to the much simpler dynamics of the core processes, in a succession of sub-models that are easier to analyse. We quantify by means of global relative errors the extent to which the simplified models reproduce the main features of the original system dynamics and apply global sensitivity analysis to test the influence of model parameters on the errors. Conclusion: The results obtained prove the robustness of the method. The analysis of the sub-model dynamics allows us to identify the source of circadian oscillations. We find that the negative feedback loop involving proteins PER, CRY, CLOCK-BMAL1 is the main oscillator, in agreement with previous modelling and experimental studies. In conclusion, Principal Process Analysis is a simple-to-use method, which constitutes an additional and useful tool for analysing the complex dynamical behaviour of biological systems.
Type de document :
Article dans une revue
Liste complète des métadonnées

Littérature citée [29 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01818033
Déposant : Jean-Luc Gouzé <>
Soumis le : mardi 26 mai 2020 - 03:28:25
Dernière modification le : mardi 8 décembre 2020 - 03:45:06

Fichier

2018_Casagranda_BMC Systems Bi...
Fichiers éditeurs autorisés sur une archive ouverte

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Stefano Casagranda, Suzanne Touzeau, Delphine Ropers, Jean-Luc Gouzé. Principal process analysis of biological models. BMC Systems Biology, BioMed Central, 2018, 12, pp.68. ⟨10.1186/s12918-018-0586-6⟩. ⟨hal-01818033⟩

Partager

Métriques

Consultations de la notice

412

Téléchargements de fichiers

431