BMT-52407 Models of Gene Networks, 5 cr
The course is lectured every year.
Course webpage: https://sites.google.com/view/andreribeirolab/home/teaching
Suitable for postgraduate studies.
Andre Sanches Ribeiro
Andre Sanches Ribeiro
||a) Exercises (must complete at least 50% of exercise points. 80% gives bonus of 0.5). Exercises solutions can be delivered until the day preceding the next exercise lecture.
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, to be delivered at the end of each lecture. The grade of each summary is: PASS/FAIL. If 3 or more 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 in order to pass the course.
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.
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.
|1.||Modeling Gene Networks, Noise in Gene Expression, and the Stochastic Simulation Algorithm. Examples and Applications.|
|2.||Delays in Gene Expression. The Delayed Stochastic Simulation Algorithm. Examples and Applications.|
|3.||Stochastic Models of Gene Expression and Gene-Gene Interactions. Examples and Applications.|
|4.||Models of Small Genetic Circuits. Examples and Applications.|
|5.||Large genetic circuits, Boolean Networks, Attractors and Ergodic Sets. Examples and Applications.|
|6.||Single-gene Models at the Nucleotide Level and Partitioning in Cell Division. Examples of applications of the modeling strategies to other biology related problems.|
|Journal||Bioinformatics||Ribeiro and Lloyd-Price||SGN Sim, a Stochastic Genetic Networks Simulator||No|
|Journal||J. Phys. Chem||Gillespie, D. T.||Exact stochastic simulation of coupled chemical reactions||No|
|Journal||J. of Theor. Biol.||Ribeiro and Kauffman||Noisy Attractors and Ergodic Sets in Models of Genetic Regulatory Networks||No|
|Journal||Journal of Computational Biology||AS Ribeiro et al||A General Modeling Strategy for Gene Regulatory Networks with Stochastic Dynamics||No|
|Journal||Journal of Computational Biology||Hidde de Jong||Modeling and Simulation of Genetic Regulatory Systems: A Literature Review||No|
Basic knowledge of programming in MatLab and basic knowledge of biology/systems biology are recommended.
|BMT-52407 Models of Gene Networks, 5 cr||BMT-52406 Models of Gene Networks, 3 cr|