FYTN12: Systems Biology - Models and Computations
In this course you will learn how tools from mathematics and computational physics can be applied to biological problems. Topics include deterministic and stochastic simulations of biochemical systems, population models, spatial models and parameter optimization.
The tentative fall 2018 (second period) schedule for the course is presented below.
The lectures are based on our slides and lecture notes as well as on papers from the scientific literature. These will be provided to you as we go along.
The introduction meeting and lectures will be in the Hans-Uno Bengtsson room (HUB) at Theoretical Physics (Fysicum, building K).
For students who want guidance on the programming projects, the lecturers will be available in their offices at Theoretical Physics as noted in the schedule. It is also possible to ask questions by email or come and look for the lecturers at other times.
|Date / Room||Topic|
|Tue 6/11 10-12|
Lecture 1.1 (Carl)
|Wed 7/11 10-12|
|Lecture 1.2 / Project 1:
Papers: Gardner (bistable switch) & Elowitz (repressilator)
|Fri 9/11 10-12|
|Project guidance, project 1|
|Tue 13/11 9-12|
|Presentations, project 1|
|Wed 14/11 10-12|
|Fri 16/11 10-12|
|Project 2 (Victor): Stochastic models|
Paper: Olariu et al, plus supplement (stem cells)
Paper: Gillespie (method)
Paper: Shea and Ackers (phage lambda)
Paper: Chickarmane et al (stem cells)
|Tue 20/11 10-12|
|Project guidance, project 2|
|Fri 23/11 9-12|
|Presentations, project 2|
|Tue 27/11 10-12|
Henrik's notes on modeling
|Wed 28/11 10-12|
|Project 3: Spatial models|
Papers: Peña (Brusselator),
Meinhardt (pattern formation),
Schmoller (dilution of Whi5)
|Fri 30/11 10-12|
|Project guidance, project 3|
|Wed 5/12 9-12|
|Presentations, project 3|
|Fri 7/12 10-12|
|Tue 11/12 10-12|
|Project 4: Simpson's paradox|
Papers: Chuang (Simpson's paradox) & Melke (quorum sensing)
|Fri 14/14 10-12|
|Project guidance, project 4|
|Tue 18/12 9-12|
|Presentations, project 4|
|Wed 19/12 10-12|
|Fri 21/12 10-12|
|Project 5: Parameter optimization (Carl)|
Data for optimization
|Tue 8/1 10-12|
|Project checkup and guidance, project 5|
|Tue 15/1 9-12|
|Presentations, project 5|
Questions on parts 1-5
The following is an unofficial translation of parts of the course description.
The course aims to give the student basic knowledge of the most important computational methods in systems biology. The student should gain experience in implementing and applying the methods to relevant biological problems.
- Translation between biology and mathematics: Formulation of equations describing biochemistry, transcription and translation based on various assumptions. Michaelis-Menten kinetics, Hill coefficients and the Shea-Ackers model for transcription.
- Population models and spatial models: Formulation of equations describing how cell populations change. Interaction between identical systems based on spatial relationships.
- Simulations: Deterministic vs. stochastic simulations of mathematical models. Weaknesses, strengths and applicability of ordinary differential equations and stochastic simulations.
- The Gillespie algorithm for stochastic simulations: Naive implementation and possible optimizations for large systems.
- Cost functions: Different approaches for comparing simulations with experimental data.
- Optimization methods: Overview of methods to adapt models to data. Local optimization, thermodynamic methods and evolutionary algorithms.
- Sensitivity analysis: Estimation of the uncertainty in the determined parameter values. Strategies to achieve robustness.