Course Catalog 2009-2010 |
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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 International Students 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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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. After the course, the student will be able to: 1) Identify and define techniques used in modeling gene expression and gene regulatory networks. Demonstrate the accuracy of the models. 2) Interpret data generated from the models, classify strengths and weaknesses of the modeling strategies, summarize results and explain the connection between models and native gene networks. 3) Implement models, apply them to mimic experiments, and calculate statistical features associated to gene expression in cells. Apply the knowledge to construct models of engineered genetic circuits. 4) Analyze results of simulations of models of gene networks. Compare different methodologies for verifying a hypothesis or measuring a variable using such models. 5) Compare and appraise different computational models, and interpret conclusions using different models. 6) Create and develop models of gene networks from experimental data, and use the models to address questions on the dynamics of gene networks and processes regulated by these networks, e.g., cell differentiation.
| 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 | Mandatory/Advisable | Description |
| SGN-6056 Introduction to Computational Systems Biology | Advisable |
Additional information about prerequisites
Basic knowledge of biology/systems biology, e.g. Introduction to Computational Systems Biology (SGN-6056) or equivalent. Basic knowledge of programming in MatLab or C++, and basic knowledge in Mathematics and Physics.
There is no equivalence with any other courses
The course is lectured every 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 Practical works |
Contact teaching: 50 % Distance learning: 0 % Self-directed learning: 50 % |