Towards a Large Integrated Model of Signal Transduction and Gene Regulation Events in Mammalian Cells

Liam G. Fearnley, Mark A. Ragan, Lars K. Nielsen

Abstract

Recent work has generated whole-cell and whole-process models capable of predicting phenotype in simple organisms. The approaches used are hindered in higher organisms and more-complex cells by a lack of kinetic parameters for reactions and events, and the difficulty of measuring and estimating these. Here, we outline a large, two-process model capable of predicting the effects of gene expression on a signal transduction network. Our method models signal transduction and the processes involved in gene expression as two separate systems, solved iteratively. We show that this approach is sufficient to capture functionally significant behaviour resulting from common network motifs. We further demonstrate that our method is scalable and efficient to the size of the largest signal transduction databases currently available. This approach enables analysis and prediction in the absence of kinetic data, but is itself held back by the lack of detailed large-scale gene expression models. However, research consortia such as ENCODE and FANTOM are rapidly adding to the knowledge of transcriptional regulation, and we anticipate that incorporating this data into our regulatory model could allow the modelling of complex cellular phenomena such as the structured progression seen in cellular differentiation.

References

  1. BioPAX Consortium (2006). BioPAX: Biological pathways exchange. Available online at http://www.biopax.org. Retrieved June 2011.
  2. Chen, W. W., Schoeberl, B., Jasper, P. J., Niepel, M., Nielsen, U. B., Lauffenburger, D. A., and Sorger, P. K. (2009). Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol. Syst. Biol., 5:239.
  3. de Jong, H. (2002). Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol., 9(1):67-103.
  4. Fearnley, L. G. and Nielsen, L. K. (2012). PATHLOGIC-S: a scalable Boolean framework for modelling cellular signalling. PLoS One, 7(8):e41977.
  5. Handorf, T. and Klipp, E. (2012). Modeling mechanistic biological networks: an advanced boolean approach. Bioinformatics, 28(4):557-563.
  6. Haus, U.-U., Niermann, K., Truemper, K., and Weismantel, R. (2009). Logic integer programming models for signaling networks. J. Comput. Biol., 16(5):725-743.
  7. Heinrich, R., Neel, B. G., and Rapoport, T. A. (2002). Mathematical models of protein kinase signal transduction. Mol. Cell, 9(5):957-970.
  8. Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nat. Protoc., 4(1):44-57.
  9. Karr, J., Sanghvi, J., Macklin, D., Gutschow, M., Jacobs, J., Bolival, B., Assad-Garcia, N., Glass, J., and Covert, M. (2012). A whole-cell computational model predicts phenotype from genotype.
  10. Keen, J. C. and Davidson, N. E. (2003). The biology of breast carcinoma. Cancer, 97(3 Suppl):825-833.
  11. Kofahl, B. and Wolf, J. (2010). Mathematical modelling of Wnt/ß-catenin signalling. Biochem. Soc. Trans., 38(5):1281-1285.
  12. Levy, D. E. and Darnell, J. E. (2002). STATs: transcriptional control and biological impact. Nat. Rev. Mol. Cell Biol., 3(9):651-662.
  13. Matthews, L., Gopinath, G., Gillespie, M., Caudy, M., Croft, D., de Bono, B., Garapati, P., Hemish, J., Hermjakob, H., Jassal, B., Kanapin, A., Lewis, S., Mahajan, S., May, B., Schmidt, E., Vastrik, I., Wu, G., Birney, E., Stein, L., and D'Eustachio, P. (2008). Reactome knowledgebase of biological pathways and processes.
  14. Nucleic Acids Res., 37:D619-22. PMID: 18981052.
  15. Naka, T., Narazaki, M., Hirata, M., Matsumoto, T., Minamoto, S., Aono, A., Nishimoto, N., Kajita, T., Taga, T., Yoshizaki, K., Akira, S., and Kishimoto, T. (1997). Structure and function of a new STAT-induced STAT inhibitor. Nature, 387(6636):924-929.
  16. NCI-Nature Pathway Interaction Database (2012). Validated estrogen receptor alpha network.
  17. Panther Pathways (2012). Interleukin signaling pathway. Retrieved December, 2012.
  18. Saez-Rodriguez, J., Simeoni, L., Lindquist, J. A., Hemenway, R., Bommhardt, U., Arndt, B., Haus, U.- U., Weismantel, R., Gilles, E. D., Klamt, S., and Schraven, B. (2007). A logical model provides insights into T-cell receptor signaling. PLoS Comput. Biol., 3(8):e163.
  19. Samaga, R., Saez-Rodriguez, J., Alexopoulos, L. G., Sorger, P. K., and Klamt, S. (2009). The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput. Biol., 5(8):e1000438.
  20. Schwartz, A. G., Prysak, G. M., Murphy, V., Lonardo, F., Pass, H., Schwartz, J., and Brooks, S. (2005). Nuclear estrogen receptor beta in lung cancer: expression and survival differences by sex. Clin. Cancer Res., 11(20):7280-7287.
  21. The ENCODE Project Consortium (2011). A user's guide to the Encyclopedia of DNA Elements (ENCODE). PLoS Biol., 9(4):e1001046.
  22. Yoshiura, S., Ohtsuka, T., Takenaka, Y., Nagahara, H., Yoshikawa, K., and Kageyama, R. (2007). Ultradian oscillations of Stat, Smad, and Hes1 expression in response to serum. PNAS, 104(27):11292-11297.
Download


Paper Citation


in Harvard Style

G. Fearnley L., A. Ragan M. and K. Nielsen L. (2014). Towards a Large Integrated Model of Signal Transduction and Gene Regulation Events in Mammalian Cells . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 117-122. DOI: 10.5220/0004739601170122


in Bibtex Style

@conference{bioinformatics14,
author={Liam G. Fearnley and Mark A. Ragan and Lars K. Nielsen},
title={Towards a Large Integrated Model of Signal Transduction and Gene Regulation Events in Mammalian Cells},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={117-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004739601170122},
isbn={978-989-758-012-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - Towards a Large Integrated Model of Signal Transduction and Gene Regulation Events in Mammalian Cells
SN - 978-989-758-012-3
AU - G. Fearnley L.
AU - A. Ragan M.
AU - K. Nielsen L.
PY - 2014
SP - 117
EP - 122
DO - 10.5220/0004739601170122