Syllabus for |
|
| FFR110 - Computational biology 1 |
| |
| Syllabus adopted 2016-02-13 by Head of Programme (or corresponding) |
| Owner: MPCAS |
|
|
7,5 Credits |
| Grading: TH - Five, Four, Three, Fail |
| Education cycle: Second-cycle |
|
Major subject: Bioengineering, Chemical Engineering, Engineering Physics
|
|
Department: 16 - PHYSICS
|
Teaching language: English
Open for exchange students
Block schedule:
C
| Course module |
|
Credit distribution |
|
Examination dates |
|
Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0199 |
Examination |
7,5 c |
Grading: TH |
|
|
|
7,5 c
|
|
|
|
|
15 Mar 2018 pm SB, |
08 Jun 2018 am M, |
29 Aug 2018 am M |
In programs
MPAPP APPLIED PHYSICS, MSC PROGR, Year 1 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
Examiner:
Forskarassistent
Kristian Gustafsson
Go to Course Homepage
Eligibility:
In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement:
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.
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
- Interacting 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
- Synchronization
Course home page
Organisation
Lectures, set of homework problems, examples classes, and written exam.
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)
as well as original research papers.
Examination
The final grade is based on homework assignments as well as on a written examination.