In silico methodologies in biochemistry and molecular medicine

University of Oulu
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Course overview


ECTS Credits 5   cr
Language of instruction EN.



MSc yr1-2 spring

Learning outcomes

After a successful completion of this course, students will have

  • Obtained an appreciation of the quantitative aspects of analyzing scientific (big) data either stored in large data databases or generated by sophisticated modeling and simulation tools.
  • Gained a basic understanding of applying various bioinformatics methods to large biological data sets.
  • Realized the potential of scientific computing for the study of the behavior of biological systems, in particular large biological macromolecules.

This course aims at emphasizing the quantitative aspects of scientific research. For this, the course contains three intertwined components: (i) searching and evaluating nucleic acid and protein structural data from various databases, (ii) use of scientific computing to study structural, dynamical, functional and thermodynamical properties of proteins and membranes and their interaction with other molecules, and (iii) using biocomputing tools to access and analyze large and high-throughput data produced and accessible through biochemical and computational experiments.

Students will learn to access biological databases, search and retrieve relevant data, analyze data in a meaningful manner, and link data and results obtained from different tools. A very brief introduction to metabases and data compilation is provided as well. Interaction studies are emphasized through genome-wide mapping of protein-DNA interaction, proteomics-based bioinformatics, and high-throughput mapping of protein-protein interaction networks. Commonly employed modeling and simulation techniques will also be dealt with. These include molecular dynamics, Monte Carlo and Langevin (stochastic, Brownian) dynamics, continuum electrostatics, statistical thermodynamics, protein modeling techniques, protein-ligand docking, protein-ligand affinity calculations and the computer simulation of the protein folding process and enzyme action.

Mode of delivery

Face to face teaching

Learning activities and teaching methods

74 h contact sessions. Lectures and practicals, student tasks, including the presentation of an original article. Attendance to practicals and article presentation are mandatory.

Target group

MSc / Protein science and biotechnology

Prerequisites and co-requisites


Recommended optional programme components


Recommended or required reading

Books, articles:

1. Big data in biomedicine (

2. Holzinger, A. Biomedical informatics, Springer, Heidelberg, 2014. 3. PubMed (Publications) (

4. Leach, A.R., Molecular modelling. Principles and applications, Second edition, Prentice Hall, New York, 2001

5. Berendsen, H.J.C Simulating the physical world. Hierarchial modeling from quantum mechanics to fluid dynamics., Cambridge University Press, Cambridge, 2007


Useful databases:

1. GenBank (DNA) (

 2. Ensembl and Ensembl Genomes (Genome) ( and

3. UniProt (Protein) (

4. DIP and BioGrid (Protein Interaction) ( and

5. PDB (protein structure database) (

6. Entrez (

Assessment Methods and criteria

Practicals evaluation, article presentation, group discussion, and project report. No exam.



Person responsible Juffer Andre Harrus Deborah Wei Gonghong Leprovost Pierre
Work placements


Other information

Location of instruction: Kontinkangas campus

University of Oulu oulun.yliopisto(at)
Tel. +358 294 48 0000
Fax +358 8 553 4112
PL 8000
FI-90014 Oulun yliopisto