BMT-52406 Models of Gene Networks, 3 cr

 

Course responsible: Andre S. Ribeiro (andre dot sanchesribeiro at tut.fi)

Learning Outcomes

Learning outcomes of the course: 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.

Requirements to complete course and evaluation:

a) Exercises (Must complete at least 50% of exercise points. 80% provides bonus of 0.5).
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.

Exercises

Exercises are mandatory. Half of the exercise points are required to pass the course. 80% or more results in a bonus of +0.5 in the final grade. 
Exercises are made available in POP.

Lecture Summaries

In the end of each lecture, 5-10 minutes will be used to do a very short (1-5 lines) summary of the content of the lecture, that must be delivered at the end of the lecture. 3 (pass) summaries are required for having a bonus.

Project description

The project involves choosing one simple gene network model (as those studied in the lectures, like the Toggle Switch, Repressilator, etc..), and explore its dynamics by varying its several parameters. The aim is to explore the parameter space of the model and determine what parameters most influence the dynamics (e.g. promoter delay, rate constants, etc). You can also consider external effects such as the response of the model to perturbations at specific timepoints.

The student is required to prepare a Powerpoint presentation with introduction to the model, description of what was simulated, and presentation of the results. The Powerpoint presentation is to be used in the oral presentation (10 min. long) of the results and must be delivered to the lecturer by USB drive or email, in PPT or PDF format, at the time of the lecture when the presentation is given.

Useful material

The simulator used in this course is SGNSim by A. Ribeiro and J. Lloyd-Price. The simulator can be obtained from here. It is freeware and runs on Windows. The simulator is called sgns.exe in the zip package, and is a command-line utility. A simple example reactions file for quick review of the simulator input file syntax, and a MATLAB frontend to the simulator (instructions are written in the file, and can be shown e.g. by typing help sgnsimulate in the MATLAB prompt) can be found here.

For exercise 5, please download this.

For exercise 6, please download this.

Study material

The lecture slides will be made available in POP. A sample exam is available here.

Type

Reference

Exam material

Language

Article

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. 

Yes 

English 

Book chapter

Andre S. Ribeiro, A Model of Genetic Networks with Delayed Stochastic Dynamics, in “Analysis of Microarray Data: Network based Approaches”, Wiley, 2007.

Yes 

English 

Article

Gillespie, D. T., A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J. Comput. Phys., 22, 1976, 403-434.

Yes

English 

Article

Gillespie, D. T., Exact stochastic simulation of coupled chemical reactions, J. Phys. Chem., 81, 1977, 2340-2361.

Yes

English 

Article

Hidde de Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review, Journal of Computational Biology. 2002, 9(1): 67-103.

No

English 

Article

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

No 

English 

Article

Andre S. Ribeiro and J. Lloyd-Price, SGN Sim, a Stochastic Genetic Networks Simulator, Bioinformatics, 23(6):777-779.

Yes

English 

Article

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.

No

English

Prerequisites: Basic knowledge of programming in MatLab (recommended: SGN-84006 Introduction to Scientific Computing with Matlab) or C++.

Remarks: The course is suitable for postgraduate studies.

Methods of instruction

Hours

Lectures

14

Exercises

14

 

 

Hours

Exam/midterm exam

3

Total sum

31

 

Additional information related to course: The course is lectured every year.