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:
Infobiotics Workbench: A P Systems Based Tool for Systems and Synthetic Biology Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

7. MP Modelling for Systems Biology: Two Case Studies

verfasst von : Luca Marchetti, Vincenzo Manca, Roberto Pagliarini, Aliccia Bollig-Fischer

Erschienen in: Applications of Membrane Computing in Systems and Synthetic Biology

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Metabolic P systems (MP systems), based on Păun’s P systems, were introduced for modelling metabolic systems by means of suitable multiset rewriting grammars. The initial modelling framework has been widely extended in last years and equipped with a new regression algorithm which derives MP models from the time series of observed dynamics. This has allowed us to dramatically extend the range of possible MP modelling applications from metabolic dynamics to more general kinds of dynamical systems. In this work two applications of MP systems are presented, for discovering the internal regulation logic of two phenomena relevant to systems biology. The first one is a metabolic dynamics related to glucose/insulin interactions during the Intravenous Glucose Tolerance Test. The second one deals with the definition of gene expression networks related to breast cancer under the inhibition of a growth factor.

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 Biochemical Networks Discrete Modeling Inspired by Membrane Systems
Nächstes Kapitel Modelling and Analysis of E. coli Respiratory Chain
Fußnoten
1
See http://​www.​mathworks.​it/​index.​html for details on the MATLAB software.
 
2
The approximation order ranges from \(10^{-6}\) to \(10^{-14}\), depending on the considered model.
 
3
Therefore, LGSS can be executed by other “MATLAB–like” free applications (for example, the GNU Octave project at http://​www.​octave.​org).
 
4
The Goldbeter’s mitotic oscillator represents the simplest form of mitotic mechanism found in early amphibian embryos [22].
 
5
Other models currently available in literature provide a more accurate modelling of the test by considering other factors also, such as the dynamics of C–peptide, which is secreted by the pancreas at the same rate of insulin (see [62] for details).
 
6
Since during the IVGTT the glucose level gradually returns to its basal level, here we assume \(G_b\) to be equal to the last value of the considered glucose time–series.
 
7
Since our time series have been \(log_2\) transformed, the \(log_2\) fold–change of one time series is given by the subtraction of the maximum expression value with the minimum one.
 
Literatur
1.
2.
A. Aczel, J. Sounderpandian, Complete Business Statistics (Mc Graw Hill, International Edition, 2006)
3.
J. Bailey, Mathematical modeling and analysis in biochemical engineering: past accomplishments and future opportunities. Biotechnol. Prog. 14, 8–20 (1998)CrossRef
4.
R. Bergman, D. Finegood, M. Ader, Assessment of insulin sensitivity in vivo. Endocr. Rev. 6(1), 45–86 (1985)CrossRef
5.
R. Bergman, Y. Ider, C. Bowden, C. Cobelli, Quantitative estimation of insulin sensitivity. Am. J. Physiol. Endocrinol. Metab. 236(6), 667–677 (1979)
6.
G. Bocharov, F. Rihan, Numerical modelling in biosciences using delay differential equations. J. Comput. Appl. Math. 125, 183–199 (2000)CrossRefMATHMathSciNet
7.
H. Bolouri, E. Davidson, Modeling transcriptional regulatory networks. BioEssays 24(12), 1118–1129 (2002)CrossRef
8.
B. Bolstad, R. Irizarry, M. Astrand, T. Speed, A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185–193 (2003)CrossRef
9.
A. Boutayeb, A. Chetouani, A critical review of mathematical models and data used in diabetology. Biomed. Eng. Online 5, 43 (2006)CrossRef
10.
P. Brazhnik, A. de la Fuente, P. Mendes, Gene networks: how to put the function in genomics. Trends Biotechnol. 20(11), 467–472 (2002)CrossRef
11.
H. Cao, F. Romero-Campero, S. Heeb, M. Cámara, N. Krasnogor, Evolving cell models for systems and synthetic biology. Syst. Synth. Biol. 4(1), 55–84 (2010)CrossRef
12.
S. Choi. Introduction to Systems Biology. Humana Press (2007).
13.
C. Cobelli, E. Renard, B. Kovatchev, Artificial Pancreas: Past, Present, Future. Diabetes 60(11), 2672–2682 (2011)CrossRef
14.
I. Costa, F. de A.T. de Carvalho, M. de Souto. Comparative analysis of clustering methods for gene expression time course data. Genet. Mol. Biol. 27(4), 623–631 (2004)
15.
J. Cushing, in Lecture notes in biomathematics. Integrodifferential equations and delay models in population dynamics, vol. 20. Springer-Verlag, Berlin (1977)
16.
N. Draper, H. Smith, Applied Regression Analysis, 2nd edn. (Wiley, New York, 1981)MATH
17.
J. Fisher, T. Henzinger, Executable cell biology. Nat. biotechnol 25(11), 1239–1249 (2007)CrossRef
18.
A.D. Gaetano, O. Arino, Mathematical modelling of the intravenous glucose tolerance test. J. Math. Biol. 40(2), 136–168 (2000)CrossRefMATHMathSciNet
19.
J. Gerich, Redefining the clinical management of type 2 diabetes: matching therapy to pathophysiology. Eur. J. Clin. Invest. 32, 46–53 (2002)CrossRef
20.
A. Gilman, A. Arkin, Genetic “code”: representations and dynamical models of genetic components and networks. Annu. Rev. Genomics Hum. Genet. 3, 341–369 (2002)CrossRef
21.
P. Gilon, M. Ravier, J. Jonas, J. Henquin, Control mechanisms of the oscillations of insulin secretion in vitro and in vivo. Diabetes 51(1), S144–S151 (2002)CrossRef
22.
A. Goldbeter, A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. PNAS 88(20), 9107–9111 (1991)CrossRef
23.
J. Hasty, D. McMillen, F. Isaacs, J. Collins, Computational studies of gene regulatory networks: in numero molecular biology. Nat. Rev. Genet. 2(4), 268–279 (2001)CrossRef
24.
A. Heath, L. Kavraki, Computational challenges in systems biology. Comput. sci. rev. 3(1), 1–17 (2009)CrossRef
25.
26.
S. Hoops, S. Sahle, R. Gauges, C. Lee, J. Pahle. COPASI-a COmplex PAthway SImulator. Bioinformatics 22, 3067 (26)
27.
T. Ideker, T. Galitski, L. Hood, A new approach to decoding life: Systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001)CrossRef
28.
S. Johnson, Hierarchical Clustering Schemes. Psychometrika 2, 241–254 (1967)CrossRef
29.
H. Jong, Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 69–105 (2002)
30.
H. Kitano, Computational systems biology. Nature 420, 206–210 (2002)CrossRef
31.
H. Kitano, Systems biology: a brief overview. Science 295(5560), 1662–1664 (2002)CrossRef
32.
A.D. la Fuente, P. Brazhnik, P. Mendes, Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet. 18(8), 395–398 (2002)CrossRef
33.
D. Lockhart, E. Winzeler, Genomics, gene expression and DNA microarrays. Nature 405, 827–836 (2000)CrossRef
34.
T. Maiwald, J. Timmer, Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics 24(18), 2037–2043 (2008)CrossRef
35.
A. Makroglou, J. Li, Y. Kuang, Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: an overview. Appl. Numer. Math. 56(3), 559–573 (2006)CrossRefMATHMathSciNet
36.
37.
V. Manca. Algorithmic Bioprocesses, chapter 28: Log-Gain Principles for Metabolic P Systems. Natural Computing. pp. 585–605. Springer-Verlag (2009)
38.
V. Manca. Fundamentals of Metabolic P Systems. In [55], chapter 19 (2010).
39.
V. Manca, Infobiotics: information in biotic systems (Springer, Berlin, 2013)CrossRef
40.
V. Manca, L. Bianco, Biological networks in metabolic P systems. BioSystems 91(3), 489–498 (2008)CrossRef
41.
V. Manca, L. Bianco, F. Fontana, Evolutions and oscillations of P systems: theoretical considerations and application to biological phenomena. LNCS 3365, 63–84 (2005)
42.
V. Manca, L. Marchetti, Goldbeter’s mitotic oscillator entirely modeled by MP systems. LNCS 6501, 273–284 (2010)
43.
V. Manca, L. Marchetti, Metabolic approximation of real periodical functions. J. Logic Algebraic Program. 79, 363–373 (2010)CrossRefMATHMathSciNet
44.
V. Manca, L. Marchetti, Log-gain stoichiometic stepwise regression for MP systems. Int. J. Found. Comput. Sci. 22(1), 97–106 (2011)CrossRefMATHMathSciNet
45.
V. Manca, L. Marchetti, Solving dynamical inverse problems by means of metabolic P systems. BioSystems 109, 78–86 (2012)CrossRef
46.
V. Manca, L. Marchetti, An algebraic formulation of inverse problems in MP dynamics. Int. J. Comput. Math. 90(4), 845–856 (2013)CrossRefMATHMathSciNet
47.
V. Manca, L. Marchetti, R. Pagliarini, MP modelling of glucose-insulin interactions in the intravenous glucose tolerance test. Int. J. Nat. Comput. Res. 2(3), 13–24 (2011)CrossRef
48.
L. Marchetti, V. Manca. A methodology based on MP theory for gene expression analysis. CMC 2011, LNCS, 7184, 300–313 (2012)
49.
A. Mari, Mathematical modeling in glucose metabolism and insulin secretion. Curr. Opin. Clin. Nutr. Metab. care 5(5), 495–501 (2002)CrossRef
50.
A. Mukhopadhyay, A. D. Gaetano, O. Arino. Modelling the intravenous glucose tolerance test: A global study for single-distributed-delay model. Discrete and Continuous Dynamical Systems - Series B (DCDS-B), 4, 2, (2004), 407–417.
51.
National Diabetes Data Group, Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28(28), 1039–1057 (1979)
52.
J. Orth, I. Thiele, B. Palsson, What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010)CrossRef
53.
S. Panunzi, P. Palumbo, A. De Gaetano, A discrete single-delay model for the intra-venous glucose tolerance test. Theor. Biol. Med. Modell. 4(35), 1–16 (2007)
54.
55.
G. Păun, G. Rozenberg, Oxford Handbook of Membrane Computing (Oxford University Press, Oxford, 2010)MATH
56.
K. Pearson, Notes on the history of correlation. Biometrika 13(1), 25–45 (1920)CrossRef
57.
C. Priami, Algorithmic systems biology. Commun. ACM 52, 80–88 (2009)CrossRef
58.
J. Quackenbush, Microarray data normalization and transformation. Nat. genet. suppl. 32, 496–501 (2002)CrossRef
59.
J. Schellenberger, R. Que, R. Fleming, I. Thiele, J. Orth, A. Feist, D. Zielinski, A. Bordbar, N. Lewis, S. Rahmanian, J. K. J., D. Hyduke, B. Palsson. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature protocols 6(9), 1290–1307 (2011)
60.
P. Smolen, D. Baxter, J. Byrne, Modeling transcriptional control in gene networks: Methods, recent results, and future directions. Bull. Math. Biol. 62(2), 247–292 (2000)CrossRef
61.
G. Toffolo, R. Bergman, D. Finegood, C. Bowden, C. Cobelli, Quantitative estimation of beta cell sensitivity to glucose in the intact organism: a minimal model of insulin kinetics in the dog. Diabetes 29(12), 979–990 (1980)CrossRef
62.
M. Trombetta, L. Boselli, A. Cretti, A. Calì, M. Vettore, B. Caruso, R. Dorizzi, A. Avogaro, M. Muggeo, E. Bonora, R. Bonadonna. Type 2 diabetes mellitus: A disease of the governance of the glucose-insulin system An experimental metabolic control analysis study. Nutrition, Metabolism & Cardiovascular Diseases. In press.
63.
M. von Stosch, J. Peres, S.F. de Azevedo, R. Oliveira, Modeling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach. BMC Sys. Biol. 4, 131 (2010)CrossRef
64.
B. Wilhelm, J. Landry, Rna-seq-quantitative measurement of expression through massively parallel rna-sequencing. Methods 48, 249–257 (2009)CrossRef
Metadaten
Titel
MP Modelling for Systems Biology: Two Case Studies
verfasst von
Luca Marchetti
Vincenzo Manca
Roberto Pagliarini
Aliccia Bollig-Fischer
Copyright-Jahr
2014
Buch
Applications of Membrane Computing in Systems and Synthetic Biology

Print ISBN: 978-3-319-03190-3

Electronic ISBN: 978-3-319-03191-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-03191-0_7