Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Reconstructing Statistics of Promoter Switching from Reporter Protein Population Snapshot Data Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Integrating Time-Series Data in Large-Scale Discrete Cell-Based Models

verfasst von : Louis Fippo Fitime, Christian Schuster, Peter Angel, Olivier Roux, Carito Guziolowski

Erschienen in: Hybrid Systems Biology

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

In this work we propose an automatic way of generating and verifying formal hybrid models of signaling and transcriptional events, gathered in large-scale regulatory networks.This is done by integrating temporal and stochastic aspects of the expression of some biological components. The hybrid approach lies in the fact that measurements take into account both times of lengthening phases and discrete switches between them. The model proposed is based on a real case study of keratinocytes differentiation, in which gene time-series data was generated upon Calcium stimulation.
To achieve this we rely on the Process Hitting (PH) formalism that was designed to consider large-scale system analysis. We first propose an automatic way of detecting and translating biological motifs from the Pathway Interaction Database to the PH formalism. Then, we propose a way of estimating temporal and stochastic parameters from time-series expression data of action on the PH. Simulations emphasize the interest of synchronizing concurrent events.

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

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

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"

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
Vorheriges Kapitel High-Performance Discrete Bifurcation Analysis for Piecewise-Affine Dynamical Systems
Nächstes Kapitel Approximate Probabilistic Verification of Hybrid Systems
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
Literatur
1.
Ahmad, J., Roux, O., Bernot, G., Comet, J.-P., Richard, A.: Analysing formal models of genetic regulatory networks with delays. Int. J. Bioinform. Res. Appl. (IJBRA) 4(3), 240–262 (2008)CrossRef
2.
Andreychenko, A., Mikeev, L., Spieler, D., Wolf, V.: Approximate maximum likelihood estimation for stochastic chemical kinetics. EURASIP J. Bioinform. Syst. Biol. 2012(1), 9 (2012)CrossRef
3.
Batt, G., Page, M., Cantone, I., Goessler, G., Monteiro, P., de Jong, H.: Efficient parameter search for qualitative models of regulatory networks using symbolic model checking. Bioinformatics 26(18), i603–i610 (2010)CrossRef
4.
Batt, G., Ben Salah, R., Maler, O.: On timed models of gene networks. In: Raskin, J.-F., Thiagarajan, P.S. (eds.) FORMATS 2007. LNCS, vol. 4763, pp. 38–52. Springer, Heidelberg (2007)CrossRef
5.
Busch, H., Camacho-Trullio, D., Rogon, Z., Breuhahn, K., Angel, P., Eils, R., Szabowski, A.: Gene network dynamics controlling keratinocyte migration. Mol. Syst. Biol. 4(1), 199 (2008)
6.
Chaouiya, C., Remy, E., Mossé, B., Thieffry, D.: Qualitative analysis of regulatory graphs: a computational tool based on a discrete formal framework. In: Benvenuti, L., De Santis, A., Farina, L. (eds.) Positive Systems. LNCIS, vol. 294, pp. 119–126. Springer, Heidelberg (2003)CrossRef
7.
De Jong, H., Geiselmann, J., Hernandez, C., Page, M.: Genetic network analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 19(3), 336–344 (2003)CrossRef
8.
Gardner, T.S., Di Bernardo, D., Lorenz, D., Collins, J.J.: Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629), 102–105 (2003)CrossRef
9.
Guziolowski, C., Kittas, A., Dittmann, F., Grabe, N.: Automatic generation of causal networks linking growth factor stimuli to functional cell state changes. FEBS J. 279(18), 3462–3474 (2012)CrossRef
10.
Guziolowski, C., Videla, S., Eduati, F., Thiele, S., Cokelaer, T., Siegel, A., Saez-Rodriguez, J.: Exhaustively characterizing feasible logic models of a signaling network using answer set programming. Bioinformatics 29(18), 2320–2326 (2013)CrossRef
11.
Heiner, Monika, Gilbert, David, Donaldson, Robin: Petri nets for systems and synthetic biology. In: Bernardo, Marco, Degano, Pierpaolo, Zavattaro, Gianluigi (eds.) SFM 2008. LNCS, vol. 5016, pp. 215–264. Springer, Heidelberg (2008)CrossRef
12.
Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)CrossRef
13.
Kolly, C., Suter, M.M., Muller, E.J.: Proliferation, cell cycle exit, and onset of terminal differentiation in cultured keratinocytes: pre-programmed pathways in control of C-Myc and Notch1 prevail over extracellular calcium signals. J. Invest. Dermatol. 124(5), 1014–1025 (2005)CrossRef
14.
MacNamara, A., Terfve, C., Henriques, D., Bernabé, B.P., Saez-Rodriguez, J.: State-time spectrum of signal transduction logic models. Phys. Biol. 9(4), 045003 (2012)CrossRef
15.
Maurin, M., Magnin, M., Roux, O.: Modeling of genetic regulatory network in stochastic \(\pi \)-calculus. In: Rajasekaran, S. (ed.) BICoB 2009. LNCS, vol. 5462, pp. 282–294. Springer, Heidelberg (2009)CrossRef
16.
Mitsos, A., Melas, I.N., Siminelakis, P., Chairakaki, A.D., Saez-Rodriguez, J., Alexopoulos, L.G.: Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput. Biol. 5(12), e1000591 (2009)CrossRef
17.
Molloy, M.K.: Performance analysis using stochastic petri nets. IEEE Trans. Comput. 100(9), 913–917 (1982)CrossRef
18.
Paulevé, L., Magnin, M., Roux, O.: Refining dynamics of gene regulatory networks in a stochastic \(\pi \)-calculus Framework. In: Priami, C., Back, R.-J., Petre, I., de Vink, E. (eds.) Transactions on Computational Systems Biology XIII. LNCS, vol. 6575, pp. 171–191. Springer, Heidelberg (2011)CrossRef
19.
Pinna, A., Soranzo, N., de la Fuente, A.: From knockouts to networks: Establishing direct cause-effect relationships through graph analysis. PLoS ONE 5(10), e12912 (2010)CrossRef
20.
Porreca, R., Cinquemani, E., Lygeros, J., Ferrari-Trecate, G.: Identification of genetic network dynamics with unate structure. Bioinformatics 26(9), 1239–1245 (2010)CrossRefMATH
21.
Priami, C.: Stochastic \(\pi \)-calculus. Comput. J. 38(7), 578–589 (1995)CrossRef
22.
Altman, R., Reinker, S., Timmer, J.: Parameter estimation in stochastic biochemical reactions. IEE Pro. Syst. Biol. 153, 168–178 (2006)CrossRef
23.
Schaefer, C.F., Anthony, K., Krupa, S., Buchoff, J., Day, M., Hannay, T., Buetow, K.H.: Pid: the pathway interaction database. Nucleic Acids Res. 37(suppl 1), D674–D679 (2009)CrossRef
24.
Siebert, H., Bockmayr, A.: Incorporating time delays into the logical analysis of gene regulatory networks. In: Priami, C. (ed.) CMSB 2006. LNCS (LNBI), vol. 4210, pp. 169–183. Springer, Heidelberg (2006)CrossRef
25.
Snoussi, E.H.: Qualitative dynamics of piecewise-linear differential equations: a discrete mapping approach. Dyn. Stab. Syst. 4(3–4), 565–583 (1989)MathSciNetCrossRefMATH
26.
Thieffry, D., Thomas, R.: Dynamical behaviour of biological regulatory networks immunity control in bacteriophage lambda. Bull. Math. Biol. 57(2), 277–297 (1995)MATH
27.
Thomas, R.: Boolean formalization of genetic control circuits. J. Theor. Biol. 42(3), 563–585 (1973)CrossRef
28.
Tu, C.L., Chang, W., Bikle, D.D.: The calcium-sensing receptor-dependent regulation of cell-cell adhesion and keratinocyte differentiation requires Rho and filamin A. J. Invest. Dermatol. 131(5), 1119–1128 (2011)CrossRef
29.
Van Goethem, S., Jacquet, J.-M., Brim, L., Šafránek, D.: Timed modelling of gene networks with arbitrarily precise expression discretization. Electron. Notes Theoret. Comput. Sci. 293, 67–81 (2013)CrossRef
30.
Namhee, Y., Seo, J., Rho, K., Jang, Y., Park, J., Kim, W.K., Lee, S.: hipathdb: a human-integrated pathway database with facile visualization. Nucleic Acids Res. 40(D1), D797–D802 (2012)CrossRef
Metadaten
Titel
Integrating Time-Series Data in Large-Scale Discrete Cell-Based Models
verfasst von
Louis Fippo Fitime
Christian Schuster
Peter Angel
Olivier Roux
Carito Guziolowski
Copyright-Jahr
2015
Buch
Hybrid Systems Biology

Print ISBN: 978-3-319-26915-3

Electronic ISBN: 978-3-319-26916-0

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-26916-0_5

Premium Partner

    Bildnachweise