Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic
Abstract
:“In nova fert animus mutatas dicere formas corpora... Unus erat toto naturae vultus in orbe, Quem dixere chaos: rudis indigestaque moles, Nec quidquam, nisi pondus iners, congestaque eodem non bene junctarum discordia semina rerum…”. I want to speak about bodies changed into new forms... Nature appeared the same throughout the whole world, What we call chaos: a raw confused mass, Nothing but inert matter, badly combined discordant atoms of things, confused in the one place...(Ovide, Metamorphoses, 1st Book, 10 A.D.).
1. Introduction
2. Preliminary: Notations and Definitions
2.1. Definition of an Attractor and of Its Basin
- A is a fixed set for the composed set operator LoB: A = L(B(A)),
- there is no set C ⊃ Ā, C ≠ Ā, verifying i),
- there is no D ⊂ A, D ≠ A, verifying (i) and (ii).
2.2. Degree, Connectivity and Connectedness
2.2.1. Undirected Graph
2.2.2. Regular Graph
2.2.3. Weighted and Signed Graph
2.2.4. Directed Graph
2.2.5. Indegree and Outdegree
2.2.6. Connectedness and Connectivity in Graphs
2.3. Kauffman Boolean Networks
2.4. Threshold Boolean Automata Networks
2.5. Attractors in Kauffman Boolean Networks and Threshold Boolean Automata Networks
3. Notions of Boundary, Core, Critical Node and Critical Edge of a Regulatory Interaction Network
3.1. Boundary and Core
3.2. Critical Node and Critical Edge
4. Theoretical Complements
4.1. Potential Regulatory Networks
4.2. Hamiltonian Networks
4.3. Relationships between Kauffman Boolean and Threshold Boolean Automata Networks
4.4. Relationships between Undirected and Directed Graphs
4.5. Circuits
4.6. Attractors Counting in Real Regulatory Networks
5. Robustness
- - if the nodes are sequentially visited by the updating process, the system has 6 fixed configurations, with state 1 (resp. 0) at one node and state 0 (resp. 1) at the others. Such a system having only fixed configurations is potential in the sense of the Section 4.1. [64], because the discrete velocity of the dynamics is equal to the gradient of a Lyapunov function (it is for example more generally the case in a n-switch when the interaction weights are symmetrical),
- - if the nodes are synchronously updated, we have one limit-cycle of order 2 (made of the full 0 and full 1 configurations) and 6 fixed configurations (corresponding to those of the sequential updating). Such a discrete system is Hamiltonian in the sense of Section 4.2.,
- - in the intermediary case, called block-sequential, in which we update first a node, and then synchronously the two others, we have the same attractors as in the sequential case.
- - those for which the cycles disappear when we are going down in the hierarchy from the synchronous to the sequential modes (behaviour “Down”),
- - those for which the cycles disappear when we are going up in the hierarchy from the sequential modes to the synchronous one (behaviour “Up”),
- - those not corresponding to any previous behaviour, for which the cycles occur and disappear inside the hierarchy without clear rule (behaviour “None”).
6. Examples of Robust and Non-Robust Networks
6.1. Neuron and Plant Morphogenesis
6.2. Cardio-Respiratory Physiologic Regulation
6.3. Glycolytic/Oxidative Coupling
6.3.1. The Glycolysis
6.3.2. Control Strength
6.4. Cell Cycle Control
6.5. Feather Morphogenesis
7. Perspectives and Conclusions
Acknowledgments
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Down | None | Up | Total |
---|---|---|---|
21,729 | 13,110 | 108 | 34,947 |
62.18% | 37.51% | 0.31% | 100% |
Down | None | Up | Total | |
---|---|---|---|---|
2 | 86.19% | 70.53% | 37.04% | 80.16% |
3 | 8.28% | 20.93% | 62.96% | 13.20% |
4 | 4.59% | 6.71% | 0.00% | 5.37% |
5 | 0.70% | 1.83% | 0.00% | 1.12% |
6 | 0.24% | 0.00% | 0.00% | 0.15% |
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
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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
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 StyleDemongeot, 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
APA StyleDemongeot, J., Ben Amor, H., Elena, A., Gillois, P., Noual, M., & Sené, S. (2009). Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic. International Journal of Molecular Sciences, 10(10), 4437-4473. https://doi.org/10.3390/ijms10104437