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Syllabus for

Academic year
FFR110 - Computational biology 1
 
Syllabus adopted 2012-02-22 by Head of Programme (or corresponding)
Owner: MPCAS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Chemical Engineering, Engineering Physics
Department: 16 - PHYSICS


Teaching language: English
Open for exchange students
Block schedule: B

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0199 Written and oral assignments 7,5 c Grading: TH   7,5 c    

In programs

MPAPP APPLIED PHYSICS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)

Examiner:

Professor  Bernhard Mehlig


Course evaluation:

http://document.chalmers.se/doc/ac515bff-ce32-4075-a244-ee24f864a558


Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Sufficient knowledge of Mathematics (analysis in one real variable, linear algebra), basic programming skills.

Aim

The aim of the course is to introduce students to mathematical modeling of biological systems. The emphasis of this course is on macroscopic phenomena such as population growth, morphogenesis, and ecological problems. The modeling and computer-simulation techniques discussed are essential tools for understanding ecosystems, with applications, for example, to bioconservation. Microscopic phenomena, on the molecular level, are the subject of Computational Biology B (FFR 115).

Learning outcomes (after completion of the course the student should be able to)

explain what can be, and what cannot be expected of mathematical models of biological systems
decide whether deterministic or stochastic models are required in a given context
efficiently simulate deterministic and stochastic models for population dynamics on a computer, understand and describe the implications of the results
perform linear stability analysis, and understand its limitations
efficiently simulate the partial differential equations describing advection-reaction-diffusion systems on a computer
apply non-linear time-series analysis to real data
understand the purpose and predictive power of models of evolution
write well-structured technical reports in English presenting and explaining analytical calculations and numerical results
communicate results and conclusions in a clear and logical fashion

Content

- Deterministic population dynamics: growth models, delay models, linear stability analysis, ecological implications
- nteracting species: Lotka-Volterra systems, phase-plane analysis, realistic predator-prey models
- Enzyme reaction kinetics: Michaelis-Menthen approximation, autocatalysis
- Pattern formation: Belousov-Zhabotinsky reaction, qualitative dynamics of relaxation oscillators, deterministic & stochastic approaches, reaction diffusion systems, traveling waves, spiral waves, morphogenesis
- Time-series analysis: noise in deterministic systems, linear time-series analysis, non-linear time-series analysis
- Evolutionary models
Course home page

Organisation

Lectures, set homework problems, examples classes.
Web-based course evaluation.

Literature

Lecture notes will be made available.
Recommended additional material:
J. D. Murray, Mathematical Biology, Springer, Berlin (1993)
A. Okubo, Diffusion and Ecological Problems: Mathematical Models, Springer, Berlin (1980)
J. Maynard Smith and Eörs Szathmary, The major transitions in evolution, Oxford University Press, Oxford (1995)
as well as original research papers.

Examination

The examination is based on exercises and homework assignments (100%). The examinator must be informed within a week after the course starts if a student would like to receive ECTS grades.


Page manager Published: Thu 03 Nov 2022.