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SGN-6236 Modeling Techniques for Stochastic Gene Regulatory Networks, 3 cr
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Per 1 :
Monday 14 - 16, TB214
Friday 14 - 17, TC303
Tuesday 14 - 16, TC165
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.
Andre Sanches Ribeiro
Numerical evaluation scale (1-5) will be used on the course
Methods of instruction | Hours |
Lectures | 36.0 |
Excercises | 36.0 |
Practical works | 9.0 |
Other measurings | |
Sum of all | 81.0 |
Contact teaching: 50 %
Distance learning: 0 %
Proportion of a studentĀ“s independent study: 50 %
Content 1 week (2 lessons, 45 mins each + 90 min Exercises) SSA. Examples. 2 week (2 lessons, 45 mins each + 90 min Exercises): .Delayed SSA .Examples 3 week (2 lessons, 45 mins each + 90 min Exercises): .Single expression with the delayed SSA .Examples 4 week (2 lessons, 45 mins each + 90 min Exercises): .Ergodic sets, noisy attractors as cell types .Examples: the toggle switch. 5 week (2 lessons, 45 mins each + 90 min Exercises): .Stochastic models of cell differentiation. .Examples and applications: Self-repressed gene and the repressilator 6 week (2 lessons, 45 mins each + 90 min Exercises): .Examples and applications. The P53-Mdm2 feedback loop. A self-repressed gene that eliminates a toxin.