Course Catalog 2012-2013
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Basic Pori International Postgraduate Open University

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Course Catalog 2012-2013

SGN-6236 Modeling Techniques for Stochastic Gene Regulatory Networks, 3 cr

Additional information

The course is lectured every year. Course webpage: http://www.cs.tut.fi/~sanchesr/SGN-6236/index.htm
Suitable for postgraduate studies

Person responsible

Andre Sanches Ribeiro

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
 2 h/week
 2 h/week


 


 


 


 
SGN-6236 2012-01 Wednesday 14 - 16, TB222

Requirements

a) Project work (20% of the final grade). b) Exercises(1 per exercises lesson, 40% of the final grade). c) Final exam (40% of the final grade). d) Must attend at least 50% of the lectures and must complete all the three requirements above.

Principles and baselines related to teaching and learning

-

Learning outcomes

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

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     

Evaluation criteria for the course

Grading is 0 to 5. 2 for exams, 2 for project, 1 for exercises.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

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  

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-6236 Modeling Techniques for Stochastic Gene Regulatory Networks, 3 cr SGN-52406 Models of Gene Networks, 3 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-6236 2012-01 In this course, the student will learn how to perform exact stochastic simulations of chemical reaction systems. This method can be used to model systems ranging from simple bimolecular reaction systems to models of gene expression. Next, the student will learn how to implement delayed stochastic simulations of models of gene expression and gene regulatory networks. Students will become familiar with detailed models and experimental measurements of single gene expression and many underlying regulatory mechanisms of gene expression. Finally, the student will be introduced to the latest modeling strategies of cell differentiation, and transcriptional and translational elongation at the nucleotide and codon levels.        

Last modified15.08.2012