nach oben

Neural Computing and Applications

Erschienen in:

01.02.2015 | Advances in Intelligent Data Processing and Analysis

Computational models for inferring biochemical networks

verfasst von: Silvia Rausanu, Crina Grosan, Zujian Wu, Ovidiu Parvu, Ramona Stoica, David Gilbert

Erschienen in: Neural Computing and Applications | Ausgabe 2/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.

Vorheriger Artikel Coevolutionary genetic watermarking for owner identification

Nächster Artikel Ant algorithm for modifying an inconsistent pairwise weighting matrix in an analytic hierarchy process

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Aarts E, Korst J, Michiels W (1989) Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing. Wiley, New York, pp 188–202MATH

Breitling R, Gilbert D, Heiner M, Orton R (2008) A structured approach for engineering of biochemical network models, illustrated for signalling pathways. Brief Bioinform 9(5):402–404CrossRef

Calder M, Gilmore S, Hillston J (2004) Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA. In: Priami C et al (eds) Transactions on computational systems biology. Springer, Berlin, pp 1–23

Elliot W, Elliot D (2002) Biochemistry and molecular biology, 2nd edn. Oxford University Press, Oxford

Fogel G, Corne D (2003) Evolutionary computation in bioinformatics. Morgan Kaufmann, Los Altos, pp 256–276

Heaton JT (2008) Introduction to neural networks with java. Heaton Research Inc., Chesterfield, pp 245–266

Heiner M, Donaldson R, Gilbert D (2010) Petri nets for systems biology, symbolic systems biology: theory and methods. Jones & Bartlett Learning, Woods Hole, pp 61–97

Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680CrossRefMATHMathSciNet

Kitagawa J, Iba H (2002) Identifying metabolic pathways and gene regulation networks with evolutionary algorithms. In: Fogel G, Corne D (eds) Evolutionary computation in bioinformatics. Elsevier, Amsterdam

10.

Klingmueller U, Bergelson S, Hsiao JG, Lodish HF (1996) Multiple tyrosine residues in the cytosolic domain of the erythropoietin receptor promote activation of STAT5. Proc Natl Acad Sci USA 93:8324–8328 (JAK-STAT)CrossRef

11.

Kwang-Hyun C et al (2003) Mathematical modeling of the influence of RKIP on the ERK signaling pathway. In: Priami C (ed) Computational methods in systems biology (CMSB). LNCS, vol 2602. Springer, Berlin, Heidelberg, pp 127–141

12.

Oltean M, Grosan C (2003) A Comparison of several linear genetic programming techniques. Complex Syst 14(4):285–313MathSciNet

13.

Oltean M, Grosan C, Diosan L, Mihaila C (2009) Genetic programming with linear representation: a survey. Int J Artif Intell Tools 18(2):197–238CrossRef

14.

Rausanu S, Grosan C, Wu Z, Parvu O, Gilbert D (2013) Evolving biochemical systems. In: IEEE congress on evolutionary computation, IEEE CS, pp 1602–1609

15.

Sakamoto E, Iba H (2000) Inferring a system of differential equations for a gene regulatory network by using genetic programming. In: Proceedings of the IEEE congress on evolutionary computation, IEEE Service Center, Piscataway, NJ

16.

Voet D, Voet J, Pratt CW (2006) Fundamentals of biochemistry: life at the molecular level. Wiley, New York

17.

Swameye I, Muller TG, Timmer J, Sandra O, Klingmuller U (2003) Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modelling. Proc Natl Acad Sci USA 100(3):1028–1033 CrossRef

18.

Yeung K, Janosch P, McFerran B, Rose DW, Mischak H, Sedivy JM, Kolch W (2000) Mechanism of suppression of the Raf/MEK/extracellular signal regulated kinase pathway by the Raf kinase inhibitor protein. Mol Cell Biol 20(9):3079–3085CrossRef

19.

Yeung K, Seitz T, Li S, Janosch P, McFerran B, Kaiser C, Fee F, Katsanakis KD, Rose DW, Mischak H, Sedivy JM, Kolch W (1999) Suppression of Raf-1 kinase activity and MAP kinase signaling by RKIP. Nature 401:173–177CrossRef

20.

Wu Z, Grosan C, Gilbert D (2013) Empirical study of computational intelligence strategies for biochemical systems modelling. In: Nature inspired cooperative strategies for optimization (NICSO). Studies in computational intelligence, vol 512. Springer International Publishing, Switzerland, pp 245–260

21.

Wu Z, Yang S, Gilbert D (2012) A hybrid approach to piece-wise modelling of biochemical systems. In: 12th international conference on parallel problem solving from nature, LNCS 7491/2012, pp 519–528

22.

http://www-dssz.informatik.tu-cottbus.de/DSSZ/Software/Snoopy

Titel: Computational models for inferring biochemical networks
verfasst von: Silvia Rausanu
Crina Grosan
Zujian Wu
Ovidiu Parvu
Ramona Stoica
David Gilbert
Publikationsdatum: 01.02.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1617-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 2/2015

Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term

Manifold proximal support vector machine with mixed-norm for semi-supervised classification

Multiple dimensioned mining of financial fluctuation through radial basis function networks

Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction

New result on convergence for HCNNs with time-varying leakage delays

Fusion trees for fast and accurate classification of hyperspectral data with ensembles of -divergence-based RBF networks

Premium Partner