Robustness in biological regulatory networks IV: Application to genetic networks controlling the cell cycle

Demongeot, Jacques; Waku, Jules

doi:10.1016/j.crma.2012.02.005

Dynamical Systems

Robustness in biological regulatory networks IV: Application to genetic networks controlling the cell cycle
[Robustesse dans les réseaux de régulation biologique IV : Applications biologiques aux réseaux génétiques contrôlant le cycle cellulaire]

Présenté par : the Editorial Board

Demongeot, Jacques ¹ ; Waku, Jules ^1,²

¹ AGIM CNRS/UJF 3405, Université J. Fourier Grenoble I, Faculté de Médecine, 38700 La Tronche, France
² LIRIMA-UMMISCO, Université de Yaoundé, Faculté des Sciences, BP 812, Yaoundé, Cameroon

Comptes Rendus. Mathématique, Tome 350 (2012) no. 5-6, pp. 293-298.

Résumé
Abstract

De nombres indices ont été proposés pour quantifier la complexité des réseaux biologiques de régulation, comme le nombre de leurs composants, leur connectivité, ou le nombre des composantes fortement connexes de leur graphe dʼinteraction. Quant à la stabilité de ces réseaux biologiques, elle correspond à leur capacité à absorber les changements dynamiques ou paramétriques. La complexité est ici mesurée par lʼentropie évolutionnaire, qui décrit la manière dont la probabilité de présence asymptotique du système dynamique correspondant est distribuée dans lʼespace dʼétat, et la stabilité est caractérisée par la vitesse de retour à lʼéquilibre de cette distribution, après perturbation. Cet article utilise ces notions dans le cadre de réseaux génétiques ayant une structure aléatoire booléenne à seuil, et plus particulièrement ceux contrôlant le cycle cellulaire.

Various indices of complexity are used in biological regulatory networks like the number n of their components and I of the interactions between these components, their connectance (or connectivity) equal to the ratio $I / n$ , or the number of the strong connected components of their interaction graph. The stability of a biological network corresponds to its ability to recover from dynamical or parametric disturbance. Complexity is here quantified by the evolutionary entropy, which describes the way the asymptotic presence distribution or equilibrium distribution of the corresponding dynamical system is spread over the state space and the stability (or robustness) is characterized by the rate at which the system returns to its equilibrium distribution after a perturbation. This article applies these notions in the case of genetic networks having a getBren structure (i.e., being threshold Boolean random networks) and notably those controlling the cell cycle.

Reçu le : 2011-11-18
Accepté le : 2012-01-04
Publié le : 2012-03-01

DOI : 10.1016/j.crma.2012.02.005

Affiliations des auteurs :

Demongeot, Jacques ¹ ; Waku, Jules ^{1, 2}

¹ AGIM CNRS/UJF 3405, Université J. Fourier Grenoble I, Faculté de Médecine, 38700 La Tronche, France
² LIRIMA-UMMISCO, Université de Yaoundé, Faculté des Sciences, BP 812, Yaoundé, Cameroon

@article{CRMATH_2012__350_5-6_293_0,
     author = {Demongeot, Jacques and Waku, Jules},
     title = {Robustness in biological regulatory networks {IV:} {Application} to genetic networks controlling the cell cycle},
     journal = {Comptes Rendus. Math\'ematique},
     pages = {293--298},
     publisher = {Elsevier},
     volume = {350},
     number = {5-6},
     year = {2012},
     doi = {10.1016/j.crma.2012.02.005},
     language = {en},
     url = {http://www.numdam.org/articles/10.1016/j.crma.2012.02.005/}
}

TY  - JOUR
AU  - Demongeot, Jacques
AU  - Waku, Jules
TI  - Robustness in biological regulatory networks IV: Application to genetic networks controlling the cell cycle
JO  - Comptes Rendus. Mathématique
PY  - 2012
SP  - 293
EP  - 298
VL  - 350
IS  - 5-6
PB  - Elsevier
UR  - http://www.numdam.org/articles/10.1016/j.crma.2012.02.005/
DO  - 10.1016/j.crma.2012.02.005
LA  - en
ID  - CRMATH_2012__350_5-6_293_0
ER  -

%0 Journal Article
%A Demongeot, Jacques
%A Waku, Jules
%T Robustness in biological regulatory networks IV: Application to genetic networks controlling the cell cycle
%J Comptes Rendus. Mathématique
%D 2012
%P 293-298
%V 350
%N 5-6
%I Elsevier
%U http://www.numdam.org/articles/10.1016/j.crma.2012.02.005/
%R 10.1016/j.crma.2012.02.005
%G en
%F CRMATH_2012__350_5-6_293_0

Demongeot, Jacques; Waku, Jules. Robustness in biological regulatory networks IV: Application to genetic networks controlling the cell cycle. Comptes Rendus. Mathématique, Tome 350 (2012) no. 5-6, pp. 293-298. doi : 10.1016/j.crma.2012.02.005. http://www.numdam.org/articles/10.1016/j.crma.2012.02.005/

Bibliographie
Cité par

[1] Adhikary, S.; Eilers, M. Transcriptional regulation and transformation by Myc proteins, Nat. Rev., Mol. Cell Biol., Volume 6 (2005), pp. 635-645

[2] Almanza, G.; Fernandez, A.; Volinia, S.; Cortez-Gonzalez, X.; Croce, C.M.; Zanetti, M. Selected MicroRNAs define cell fate determination of murine central memory CD8 T cells, PLoS ONE, Volume 5 (2010), p. e11243

[3] Baum, T.P.; Pasqual, N.; Thuderoz, F.; Hierle, V.; Chaume, D.; Lefranc, M.P.; Jouvin-Marche, E.; Marche, P.; Demongeot, J. IMGT/GeneInfo: enhancing $V (D) J$ recombination database accessibility, Nucleic Acids Res., Volume 32 (2004), pp. 51-54

[4] Amor, H. Ben; Demongeot, J.; Elena, A.; Sené, S. Structural sensitivity of neural and genetic networks, Lecture Notes in Comput. Sci., Volume 5317 (2008), pp. 973-986

[5] Amor, H. Ben; Cadau, S.; Elena, A.; Dhouailly, D.; Demongeot, J. Regulatory networks analysis: robustness in biological regulatory networks, Proceedings AINAʼ09 & BLSMCʼ09, IEEE Press, Piscataway, 2009, pp. 224-229

[6] Demongeot, J. Biological boundaries and biological age, Acta Biotheor., Volume 57 (2009), pp. 397-419

[7] Demongeot, J.; Drouet, E.; Moreira, A.; Rechoum, Y.; Sené, S. Micro-RNAs: viral genome and robustness of the genes expression in host, Philos. Trans. R. Soc. A, Volume 367 (2009), pp. 4941-4965

[8] Demongeot, J.; Henrion-Caude, A.; Lontos, A.; Promayon, E. General architecture of a genetic regulation network. Applications to embryologic and immunologic control (Lenaerts, T.; Giacobini, M.; Bersini, H.; Bourgine, P.; Dorigo, M.; Doursat, R., eds.), ECALʼ11, Advances in Artificial Life, Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems, MIT Press, Cambridge, MA, 2011, pp. 1-8

[9] Demongeot, J.; Goles, E.; Morvan, M.; Noual, M.; Sené, S. Attraction basins as gauges of environmental robustness in biological complex systems, PloS ONE, Volume 5 (2010), p. e11793

[10] Demongeot, J.; Elena, A.; Noual, M.; Sené, S.; Thuderoz, F. “Immunetworks”, attractors & intersecting circuits, J. Theoret. Biol., Volume 280 (2011), pp. 19-33

[11] Demongeot, J.; Waku, J. Robustness in biological regulatory networks I: Mathematical approach, C. R. Acad. Sci. Paris, Ser. I, Volume 350 (2012), pp. 221-224 | DOI

[12] Demongeot, J.; Waku, J. Robustness in biological regulatory networks II: Application to genetic threshold Boolean random regulatory networks (getBren), C. R. Acad. Sci. Paris, Ser. I, Volume 350 (2012), pp. 225-228 | DOI

[13] Demongeot, J.; Waku, J. Robustness in biological regulatory networks III: Applications to genetic networks controlling the morphogenesis, C. R. Acad. Sci. Paris, Ser. I, Volume 350 (2012), pp. 288-292 | DOI

[14] Demongeot, J.; Noual, M.; Sené, S. Combinatorics of Boolean automata circuits dynamics, Discrete Appl. Math., Volume 160 (2012), pp. 398-415

[15] Dimova, D.K.; Dyson, N.J. The E2F transcriptional network: old acquaintances with new faces, Oncogene, Volume 24 (2005), pp. 2810-2826

[16] N. Glade, A. Elena, E. Fanchon, J. Demongeot, H. Ben Amor, Determination, optimization and taxonomy of regulatory networks. The example of Arabidopsis thaliana flower morphogenesis, in: Proceedings IEEE AINAʼ11 & BLSMCʼ11, IEEE Proceedings, Piscataway, 2011, pp. 488–494.

[17] He, L.; He, X.; Lim, L.P.; De Stanchina, E.; Xuan, Z.; Liang, Y.; Xue, W.; Zender, L.; Magnus, J.; Ridzon, D.; Jackson, A.L.; Linsley, P.S.; Chen, C.; Lowe, S.W.; Cleary, M.A.; Hannon, G.J. A microRNA component of the p53 tumour suppressor network, Nature, Volume 447 (2007), pp. 1130-1134

[18] Hermeking, H.; Eick, D. Mediation of c-Myc-induced apoptosis by p53, Science, Volume 265 (1994), pp. 2091-2093

[19] Ivanov, V.N.; Hei, T.K. Combined treatment with EGFR inhibitors and arsenite upregulated apoptosis in human EGFR-positive melanomas: a role of suppression of the PI3K-AKT pathway, Oncogene, Volume 24 (2005), pp. 616-626

[20] Kohn, K.W. Molecular interaction map of the mammalian cell cycle control and DNA repair systems, Mol. Biol. Cell, Volume 10 (1999), pp. 2703-2734

[21] Moriya, H.; Shimizu-Yoshida, Y.; Kitano, H. In vivo robustness analysis of cell division cycle genes in saccharomyces cerevisiae, PLoS Genet., Volume 2 (2006), pp. 1034-1045

[22] OʼConnell, R.M.; Rao, D.S.; Chaudhuri, A.A.; Baltimore, D. Physiological and pathological roles for microRNAs in the immune system, Nat. Rev., Immunol., Volume 10 (2010), pp. 111-122

[23] Pelengaris, S.; Khan, M.; Evan, G. c-MYC: more than just a matter of life and death, Nat. Rev., Cancer, Volume 2 (2002), pp. 764-776

[24] Sheaff, R.J.; Groudine, M.; Gordon, M.; Roberts, J.M.; Clurman, B.E. Cyclin E-CDK2 is a regulator of p27Kip1, Genes Dev., Volume 11 (1997), pp. 1464-1478

[25] Suzuki, H.I.; Miyazono, K. Dynamics of microRNA biogenesis: crosstalk between p53 network and microRNA processing pathway, J. Mol. Med., Volume 88 (2010), pp. 1085-1094

[26] Tonnelier, A.; Meignen, S.; Bosch, H.; Demongeot, J. Synchronization and desynchronization of neural oscillators: comparison of two models, Neural Netw., Volume 12 (1999), pp. 1213-1228

[27] Xiao, C.; Calado, D.P.; Galler, G.; Thai, T.H.; Patterson, H.C.; Wang, J.; Rajewsky, N.; Bender, T.P.; Rajewsky, K. MiR-150 controls B cell differentiation by targeting the transcription factor c-Myb, Cell, Volume 131 (2007), pp. 146-159

Cité par Sources :