APPLICATION OF THE MP THEORY TO SYSTEMS BIOLOGY

Vincenzo Manca, Luca Marchetti

2012

Abstract

The main framework analysis for the most part of biological dynamics remains the theory of ordinary differential equations (ODEs). However, ODEs present some intrinsic limitations in the evaluation of the kinetic reaction rates. In contrast to ODEs, Metabolic P systems (MP systems), based on P˘aun’s P systems, were introduced for modelling metabolic systems by means of suitable multiset rewriting grammars. In this work three applications of MP systems are presented, for discovering the internal regulation logic of three phenomena relevant in systems biology: i) the Goldbeter’s mitotic oscillator; ii) the glucose/insulin dynamics in the Intravenous Glucose Tolerance Test; iii) the HER-2 oncogene-regulated transcriptome in human SUM-225 cells. Despite the differences between the considered phenomena, in all the cases a model was found that exhibits good approximation of the observed time series and highlights results which are new or that have been only theorized before.

References

  1. Bailey, J. (1998). Mathematical modeling and analysis in biochemical engineering: past accomplishments and future opportunities. Biotechnology Progress, 14:8- 20.
  2. Bolouri, H. and Davidson, E. (2002). Modeling transcriptional regulatory networks. BioEssays, 24(12):1118- 1129.
  3. Cao, H., Romero-Campero, F., Heeb, S., Cámara, M., and Krasnogor, N. (2010). Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology, 4(1):55-84.
  4. Decraene, J. and Hinze, T. (2010). A Multidisciplinary Survey of Computational Techniques for the Modelling, Simulation and Analysis of Biochemical Networks. Journal of Universal Computer Science, 16(9):1152- 1175.
  5. Draper, N. and Smith, H. (1981). Applied Regression Analysis, 2nd Edition. John Wiley & Sons, New York.
  6. Gilman, A. and Arkin, A. (2002). Genetic “code”: Representations and dynamical models of genetic components and networks. Annual Review of Genomics and Human Genetics, 3:341-369.
  7. Goldbeter, A. (1991). A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. PNAS, 88(20):9107-9111.
  8. Goldbeter, A. (2002). Computational approaches to cellular rhythms. Nature, 420:238-245.
  9. Hasty, J., McMillen, D., Isaacs, F., and Collins, J. (2001). Computational studies of gene regulatory networks: In numero molecular biology. Nature Review Genetics, 2(4):268-279.
  10. Hinze, T., Hayat, S., Lenser, T., Matsumaru, N., and Dittrich, P. (2007). Hill Kinetics Meets P Systems: A Case Study on Gene Regulatory Networks as Computing Agents in silico and in vivo. In Eleftherakis, G., Kefalas, P., and Paun, G., editors, Proceedings of the Eight Workshop on Membrane Computing (WMC8), pages 363-381. SEERC Publishers.
  11. Hocking, R. (1976). The Analysis and Selection of Variables in Linear Regression. Biometrics, 32.
  12. Manca, V. (2008). The metabolic algorithm for P systems: Principles and applications. Theoretical Computer Science, 404:142-155.
  13. Manca, V. (2009). Algorithmic Bioprocesses, chapter 28: Log-Gain Principles for Metabolic P Systems, pages 585-605. Natural Computing. Springer-Verlag.
  14. Manca, V., Bianco, L., and Fontana, F. (2005). Evolutions and Oscillations of P systems: Theoretical Considerations and Application to biological phenomena. LNCS, (3365):63-84.
  15. Manca, V. and Marchetti, L. (2010a). Goldbeter's Mitotic Oscillator Entirely Modeled by MP Systems. LNCS, 6501:273-284.
  16. Manca, V. and Marchetti, L. (2010b). Metabolic approximation of real periodical functions. The Journal of Logic and Algebraic Programming, 79:363-373.
  17. Manca, V. and Marchetti, L. (2011). Log-Gain Stoichiometic Stepwise regression for MP systems. International Journal of Foundations of Computer Science, 22(1):97-106.
  18. Manca, V., Marchetti, L., and Pagliarini, R. (2011). MP modelling of glucose-insulin interactions in the Intravenous Glucose Tolerance Test. International Journal of Natural Computing Research, 2(3):13-24.
  19. Marchetti, L. and Manca, V. (2011). A methodology based on MP theory for gene expression analysis. LNCS. In print.
  20. Pa?un, G. (2002). Membrane Computing. An Introduction. Springer.
  21. Smolen, P., Baxter, D., and Byrne, J. (2000). Modeling transcriptional control in gene networks: Methods, recent results, and future directions. Bulletin of Mathematical Biology, 62(2):247-292.
Download


Paper Citation


in Harvard Style

Manca V. and Marchetti L. (2012). APPLICATION OF THE MP THEORY TO SYSTEMS BIOLOGY . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 303-308. DOI: 10.5220/0003852003030308


in Bibtex Style

@conference{biosignals12,
author={Vincenzo Manca and Luca Marchetti},
title={APPLICATION OF THE MP THEORY TO SYSTEMS BIOLOGY},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={303-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003852003030308},
isbn={978-989-8425-89-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - APPLICATION OF THE MP THEORY TO SYSTEMS BIOLOGY
SN - 978-989-8425-89-8
AU - Manca V.
AU - Marchetti L.
PY - 2012
SP - 303
EP - 308
DO - 10.5220/0003852003030308