BMT-52406 Models of Gene Networks, 3 cr
Additional information
The course is lectured every year.
Course webpage: http://www.cs.tut.fi/~sanchesr/SGN-52406/index.htm
Suitable for postgraduate studies.
Person responsible
Andre Sanches Ribeiro
Lessons
| Implementation | Period | Person responsible | Requirements |
| BMT-52406 2018-01 | 1 |
Andre Sanches Ribeiro |
a) Exercises (Must complete at least 50% of exercise points. 80% provides a bonus of 0.5 out of 5). b) Final exam (50% of the final grade). c) Project work (50% of the final grade). d) Short summary (1-5 lines) of the lecture at the end of each lecture (in at least 4 lectures to be eligible to get bonus). The grade of each summary is: PASS/FAIL. If 3 or 4 summaries have PASS grade, the student gets a bonus of 0.5 in the final grade. Additionally, a passing grade must be achieved in project, exam, and exercises. Grading: 0 to 5 (0 fails, 1 to 5 passes). 2.5 from exam, 2.5 from project, 0.5 bonus for good performance in exercises, and 0.5 bonus for good performance lecture summaries. |
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. For additional information: http://www.cs.tut.fi/%7Esanchesr/BMT-52406/index.htm
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 |
Instructions for students on how to achieve the learning outcomes
Requirements: a) Exercises (Must complete 50% of exercise points. 80% provides a bonus). b) Final exam (50% of the final grade). c) Project work (50% of the final grade).
Assessment scale:
Numerical evaluation scale (0-5)
Additional information about prerequisites
Basic knowledge of programming in MatLab (recommended: SGN-84006 Introduction to Scientific Computing with Matlab) or C++. Basic knowledge of biology/systems biology.
Correspondence of content
| Course | Corresponds course | Description |
| BMT-52406 Models of Gene Networks, 3 cr | BMT-52407 Models of Gene Networks, 5 cr | |
| BMT-52406 Models of Gene Networks, 3 cr | SGN-52406 Models of Gene Networks, 3 cr |