Course Catalog 2008-2009 |
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SGN-6236 Modeling Techniques for Stochastic Gene Regulatory Networks, 3 cr |
Andre Sanches Ribeiro
| Lecture times and places | Target group recommended to | |
| Implementation 1 |
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Biotekniikan koulutusohjelma
DI-Opiskelijat Jatko-opiskelijat Teknis-luonnontieteellinen koulutusohjelma Tieto- ja sähkötekniikan tiedekunta Tietotekniikan koulutusohjelma Ympäristö- ja energiatekniikan koulutusohjelma |
Project work (20% of the final grade), exercises (1 per exercises lesson, 40% of the final grade) and final exam (40% of the final grade). The student is required to pass the course: a) must execute all the three requirements. b) must attend and complete at least 50% of the exercises lessons
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Student will know how to do exact stochastic simulations, delayed stochastic simulations, and how to create models of delayed stochastic gene regulatory networks.
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | The Stochastic Simulation Algorithm and The Delayed Stochastic Simulation Algorithm | ||
| 2. | Modeling single gene expression with the delayed Stochastic Simulation Algorithm | ||
| 3. | A stochastic delayed modeling strategy of Gene Regulatory Networks: models of noisy attractors as cell types, and ergodic sets | ||
| 4. | Stochastic models of cell differentiation | ||
| 5. | Examples and applications of the modeling strategies |
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Journal | A General Modeling Strategy for Gene Regulatory Networks with Stochastic Dynamics | Andre S. Ribeiro, R. Zhu, S. A. Kauffman | Andre S. Ribeiro, R. Zhu, S. A. Kauffman, A General Modeling Strategy for Gene Regulatory Networks with Stochastic Dynamics, Journal of Computational Biology, Vol. 13 (9), 1630-1639, 2006. | English | |||
| Journal | A general method for numerically simulating the stochastic time evolution of coupled chemical reactions | Gillespie, D. T. | Gillespie, D. T., A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J. Comput. Phys., 22, 1976, 403-434. | English | |||
| Journal | Exact stochastic simulation of coupled chemical reactions | Gillespie, D. T. | Gillespie, D. T., Exact stochastic simulation of coupled chemical reactions, J. Phys. Chem., 81, 1977, 2340-2361 | English | |||
| Journal | Modeling and Simulation of Genetic Regulatory Systems: A Literature Review | Hidde de Jong | Hidde de Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review, Journal of Computational Biology. 2002, 9(1): 67-103. | English | |||
| Journal | Noisy Attractors and Ergodic Sets in Models of Genetic Regulatory Networks | Andre S. Ribeiro, S. A. Kauffman | Andre S. Ribeiro, S. A. Kauffman, Noisy Attractors and Ergodic Sets in Models of Genetic Regulatory Networks, J. of Theoretical Bio., 247, Issue 4, 2007, Pgs 743-755 | English | |||
| Journal | SGNSim a stochastic gene network simulator | Andre S. Ribeiro and Jason Lloyd-Price | Bioinformatics | English | |||
| Journal | Studying genetic regulatory networks at the molecular level: Delayed reaction stochastic models | Rui Zhu, Andre S. Ribeiro, Dennis Salahub, and Stuart A. Kauffman | Rui Zhu, Andre S. Ribeiro, Dennis Salahub, and Stuart A. Kauffman, "Studying genetic regulatory networks at the molecular level: Delayed reaction stochastic models", Journal of Theoretical Biology, 246(4):725-45, 2007. | English | |||
| Other literature | A Model of Genetic Networks with Delayed Stochastic Dynamics | Andre S. Ribeiro | Andre S. Ribeiro, A Model of Genetic Networks with Delayed Stochastic Dynamics, in “Analysis of Microarray Data: Network based Approaches”, Wiley, Matthias Dehmer and Frank Emmert-Streib (Editors), 2007. | English |
| Course | O/R |
| SGN-6056 Introduction to Computational Systems Biology | Recommended |
The course is lectured every other year. Course webpage: http://www.cs.tut.fi/~sanchesr/SGN-6236/index.htm
| Description | Methods of instruction | Implementation | |
| Implementation 1 | From this course the student will know how to do exact stochastic simulations, delayed stochastic simulations, and how to create models of delayed stochastic gene regulatory networks. Students will become familiar with detailed models and experimental results related to single gene expression and its underlying mechanisms. Also, the student will be introduced to basic concepts of cell type and cell differentiation and learn the latest modeling techniques in these topics. | Lectures Excercises ITC Utilization Practical works |
Contact teaching: 50 % Distance learning: 0 % Self-directed learning: 50 % |