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Computational Systems Biology: Stochastic Approaches
Last Updated: 2026-02-05 16:38:51
Abstract
This course is concerned with the development of computational methods for modeling, simulation, and analysis of stochasticity in living cells. Using these tools, the course explores the richness of stochastic phenomena, how it arises from the interactions of dynamics and noise, and its biological implications.
Objective
To understand the origins and implications of stochastic noise in living cells, and to learn the computational tools for the modeling, simulation, analysis, and identification of stochastic biochemical reaction networks.
Content
The cellular environment is abuzz with noise. A key source of this noise is the randomness that characterizes the motion of cellular constituents at the molecular level. Cellular noise not only results in random fluctuations (over time) within individual cells, but it is also a main source of phenotypic variability among clonal cell populations. Review of basic probability and stochastic processes; Introduction to stochastic gene expression; deterministic vs. stochastic models; the stochastic chemical kinetics framework; a rigorous derivation of the chemical master equation; moment computations; linear vs. nonlinear propensities; linear noise approximations; Monte Carlo simulations; Gillespie's Stochastic Simulation Algorithm (SSA) and variants; direct methods for the solution of the Chemical Master Equation; moment closure methods; intrinsic and extrinsic noise in gene expression; parameter identification from noise; propagation of noise in cell networks; noise suppression in cells; the role of feedback; exploiting noise; bimodality and stochastic switches.
Resources
Literature
Literature will be distributed during the course as needed.
General Information
- Language
- English
- Levels
- BSC , DR , MSC , NDS
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 180 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Computational Systems Biology: Stochastic Approaches
Does not take place this semester.
The lecture will take place in person in Basel.
|
No time listed | 3 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Electives (The electives list in the ETH course catalogue is an open list, and the courses listed in the ETH course catalogue provide just examples for possible elective courses, e.g. a selection of eligible courses. Students are expected to look for relevant courses in the ETH and University of Basel course catalogue and ask their mentor for approval. Courses from the advanced course category may also be taken as electives. We particularly recommend browsing the University of Basel course catalogue for elective courses of relevant master's degree programes (using the filter "programe structure" on the course catalogue website), such as for example: Biomedical Engineering, Chemistry, Drug Sciences, Epidemiology, Infection Biology, Molecular Biology, Nanosciences.)
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Computational Biology and Bioinformatics Master (More informations at: )
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Core Courses (Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed. Students need to pass at least one course in each core subcategory. A total of 40 ECTS needs to be acquired in the core course category.)
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Doctorate Biosystems Science and Engineering (More Information at: )