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Article

Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

by
Jacques Demongeot
1,*,
Hedi Ben Amor
1,
Adrien Elena
1,
Pierre Gillois
1,
Mathilde Noual
2,4 and
Sylvain Sené
3,4
1
Université J. Fourier de Grenoble, TIMC-IMAG, CNRS UMR 5525, Faculté de Médecine, 38700 La Tronche, France
2
Université de Lyon, École Normale Supérieure Lyon, LIP, CNRS UMR 5668, 69007 Lyon, France
3
Université d'Evry Val d'Essonne, IBISC, CNRS FRE 3190, 91000 Evry, France
4
IXXI, Institut rhône-alpin des systèmes complexes, 69007 Lyon, France
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2009, 10(10), 4437-4473; https://doi.org/10.3390/ijms10104437
Received: 15 September 2009 / Revised: 2 October 2009 / Accepted: 14 October 2009 / Published: 19 October 2009
(This article belongs to the Special Issue Quantitative Modelling in Molecular System Bioenergetics)
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Abstract

Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control. View Full-Text
Keywords: robustness in regulatory interaction networks; attractors; interaction graph boundary; interaction graph core; critical node; critical edge; updating mode; microRNAs robustness in regulatory interaction networks; attractors; interaction graph boundary; interaction graph core; critical node; critical edge; updating mode; microRNAs
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MDPI and ACS Style

Demongeot, J.; Ben Amor, H.; Elena, A.; Gillois, P.; Noual, M.; Sené, S. Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic. Int. J. Mol. Sci. 2009, 10, 4437-4473. https://doi.org/10.3390/ijms10104437

AMA Style

Demongeot J, Ben Amor H, Elena A, Gillois P, Noual M, Sené S. Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic. International Journal of Molecular Sciences. 2009; 10(10):4437-4473. https://doi.org/10.3390/ijms10104437

Chicago/Turabian Style

Demongeot, Jacques, Hedi Ben Amor, Adrien Elena, Pierre Gillois, Mathilde Noual, and Sylvain Sené. 2009. "Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic" International Journal of Molecular Sciences 10, no. 10: 4437-4473. https://doi.org/10.3390/ijms10104437

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