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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
FFR115 - Computational biology 2
Beräkningsbiologi 2
 
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: Yes
Block schedule: A

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

MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)

Examiner:

Marina Rafajlovic


  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 provide an introduction to computational biology on the molecular and cell-biological level. The students are introduced to key concepts in molecular and cell biology. Methods for storage, search, prediction and analysis of information from molecular-biological experiments are studied. Models of molecular evolution (the so-called coalescent) are described and applied to problems in human evolution, and to the analysis of molecular bacterial data. Physical models of the structure and function of biological macromolecules (for example protein folding and RNA structure) are introduced. Computational Biology on the macroscopic level is the subject of the companion course Computational Biology (FFR 110).

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

define the key vocabulary of molecular and cell biology
understand and explain the mechanisms and driving forces of evolution
explain the significance of the coalescent process as an evolutionary model and as a means of interpreting empirical data
efficiently implement the coalescent process on a computer (with mutations, recombination, selective sweeps)
identify the most significant open problems in interpreting molecular-biological data
understand and explain strengths and weaknesses of accepted structural models for biological macromolecules
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

Biochemistry of macromolecules
A brief survey of molecular biology
A genetic glossary
Random genetic drift and the coalescent process
Physical maps of DNA
The map of the human genome
Physical models for the structure of biological macromolecules

Course home page

Organisation

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

Literature

Lecture notes will be made available.

Recommended additional material:
W. J. Ewens, Mathematical population genetics, Springer (1979)
E. S. Lander and M. S. Waterman, eds., Calculating the secrets of life, National Academic Press, Washington (1995). An on-line version of this book is available.
A. Okubo, Diffusion and ecological problems: mathematical models, Springer (1980)
J. D. Murray, Mathematical Biology, Springer (1989)
M. S. Waterman, Introduction to Bioinformatics, Chapman and Hall (1995
M. T. Madigan, J. M. Martinko and J. Parker, Biology of microorganisms, Prentice Hall (2000)

as well as original research papers.

Examination including compulsory elements

The examination is based on exercises and homework assignments (100%). 


Page manager Published: Thu 04 Feb 2021.