E-Cell Tutorial 2006

Analyzing Biochemical Systems using the E-Cell System -- Tutorial at the 7th International Conference on Systems Biology (ICSB 2006), Yokohama, Japan

Nathan Addy, Satya Arjunan, Bin Hu, Yuri Matsuzaki, Martin Robert, Takeshi Sakurada, Koichi Takahashi

Registration

Pre-registration is required. Please fill in the registration form: http://www.surveymonkey.com/s.asp?u=949612539496

Contact

For inquiries please email: satya@sfc.keio.ac.jp

Quantitative modeling has become an essential procedure to understand the cell at the systems level. Insights into the inner workings of biological networks can be obtained from mathematical analysis of biochemical models, which might not be possible from direct experimentation alone [1]. As an advanced simulation platform, the E-Cell System ([2], [3] and [4]) is of central relevance to the systems biology community. The E-Cell System allows, among other things, biological pathway analysis (e.g., flux balance analysis, elementary flux modes, stoichiometry matrix, etc.), metabolic control analysis, bifurcation and sensitivity analysis, and time-series analysis as part of its analysis toolkit package [5].

Objectives

The tutorial aims to familiarize attendants with an all-purpose simulation platform, using hands-on session exercises that introduce bifurcation and sensitivity analysis, and metabolic control analysis on real models constructed using the E-Cell System. Biologists will also learn how simulation and mathematical analysis can assist them in their research. At the completion of the tutorial, participants should be able to construct and simulate simple biological models using the E-Cell System and perform mathematical analysis.

Background

Bifurcation and sensitivity analysis can be used to elucidate the relationship between the dynamics of a nonlinear system in biology and the parameters of the system. The bifurcation program in E-Cell numerically computes the stable states of the system, such as the stable or oscillating point, with graphical representation of results. Elasticity coefficients with respect to amplitude and frequency, which indicate the robustness of the oscillation are also represented. Participants will experiment with these features hands-on using a simple oscillation model found in Drosophila [6]. Metabolic control analysis can demonstrate how fluxes and intermediate concentrations in a metabolic pathway are regulated by the enzymes that constitute the system. The analysis includes structural analysis, elasticity coefficients and the sensitivity of metabolites to small changes in individual parameters such as in enzyme concentrations or kinetic parameters. Flux and concentration control coefficients are some of the outcomes of metabolic control analysis. Participants will use metabolic control analysis to evaluate the Kuchel's erythrocyte model [7].

Intended Audience (20 participants)

The primary target audience for this introductory tutorial will be research scientists interested in biochemical simulation and analysis. Others who may be interested include computer scientists or computational biologists concerned with the development of software for cell simulation or systems biology. Participation in the tutorial does not require prior experience in modeling/simulation or skills in computer programming. The contents, however, assume familiarity with the Microsoft Windows or Linux operating system. Participants should bring a laptop PC running Microsoft Windows XP Professional (32-bit) or Fedora Core 5 to the tutorial. A USB flash drive or an internet connection is necessary to install E-Cell 3.

Tutorial Outline (tutorial length is approximately 3 hours)

Brief Introduction to Pathway Modeling on the computer (10 minutes)
- Definition of a Model
- Why model or simulate?
- Types of models
- Example of models
- Basic steps of constructing a model, a pathway map, reactions, parameters, etc.
Introduction to Mathematical Analysis of Biochemical Systems (10 minutes)
- What biological problems can the analysis solve?
The E-Cell Simulation Environment (5 minutes)
- Features and Limitations: What we can and cannot do using E-Cell 3
- E-Cell 3 architecture
- Basic terms used by E-Cell 3 (Event, Process, SIZE, etc.)
The E-Cell Analysis Toolkit (10 minutes)
Analysis with Hands-on Exercises (90 minutes)
Simulation of Dynamic Protein Complexes using Moleculizer and E-Cell (20 minutes)
Discussion (35 minutes)
- The next generation E-Cell simulation technology will focus on space, dynamic structure and parallel computation
- Tutorial wrap-up
- Response to user specific questions
- Additional Materials

References

[1]	Alvarez-Vasquez, F., Sims, K. J., Cowart, L. A., Okamoto, Y., Voit, E. O. And Hannun Y. A. (2005). Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae. Nature 433 (7024): 425-30.

[2]	Takahashi, K., Kaizu, K., Hu, B. and Tomita, M. (2004). A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics 20: 538-546.

[3]

Tomita, M., Hashimoto, K., Takahashi, K., Matsuzaki, Y., Matsushima, R., Saito, K., Yugi, K., Miyoshi, F., Nakano, H., Tanida, S., Saito, Y., Kawase, A., Watanabe, N., Shimizu, T. and Nakayama, Y. (2000). The E-CELL Project: Towards Integrative Simulation of Cellular Processes. New Generation Computing 18(1): 1-12.

[4]	Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T. S., Matsuzaki, Y., Miyoshi, F., Saito, K., Tanida, S., Yugi, K., Venter, J. C. and Hutchison 3rd, C. A. (1999). E-CELL: Software environment for whole-cell simulation. Bioinformatics 15: 72-84.

[5]	Kaizu, K., Miyoshi, F., Nakayama, Y. and Tomita, M. (2004). An Analysis Tool Library for Biochemical Modeling on E-Cell System Version 3. In Proceedings of the 15th International Conference on Genome Informatics.

[6]	Goldbeter, A. (1995). A model for circadian oscillations in the Drosophila period protein (PER). Proc. Biol. Sci. 261 (1362): 319-24.

[7]	Mulquiney, P. J. and Kuchel, P. W. (1999). Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: equations and parameter refinement. Biochem J. 342 (3): 581-96.