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Introduction to computational and systems biology

Description

This course aims to give an introduction to the broad field of computational and systems biology. We discuss in the course four main topics:

  • Basics of biology and biotechnology for computer scientists
  • Algorithms in computational biology
  • Network modelling
  • Molecular programming

Prerequisites

The course is offering a gentle introduction to all the topics it discusses, both on the bio side and on the computational side. Advanced undergraduate students, as well as graduate students in computer science, mathematics, and life sciences are welcome to the course. Computer scientists and mathematicians will discover through this course an exciting world of applications in biology and biomedicine where the computational perspective is an essential driver. Life scientists will discover through this course a new computational perspective on their field and a whole spectrum of new questions they can ask and answer through this perspective.

Teaching content

  • Introduction
  • Elements of molecular biology
  • Elements of biotechnology
  • Molecular computing, molecular self-assembly, basics of synthetic biology
  • Combinatorial pattern matching in biology
  • Gene mapping
  • Sequence assembly
  • Genome rearrangements
  • Bioinformatics tools and databases

Projects

Each student will carry out a project as a part of the course, counting towards 60% of the final grade. The students will choose themselves a disease that will form the topic of the project. They will build a large protein-protein interaction network model for that disease and will analyse the model. They will identify topological properties of the model, major disease drivers, and even potential combination of commercially available drugs that may be effective in a treatment for that disease. Both individual and team projects are allowed, with teams of at most 2 students. Each team will give a number of presentations throughout the course on their modelling work and will write an academic style report on the project by the end of the course. The tools and data sources that the teams will become familiar with during their project work include:

Here is a link to the student project from the last edition of the course.

Student projects:

Course materials:

  • N.C. Jones, P.A. Pevzner. An introduction to bioinformatics algorithms. The MIT Press, 2004.
  • U. Alon: An introduction to systems biology. Design principles of biological circuits. Chapman & Hall, CRC, 2007.
  • A. Kriete, R. Eils (Eds.): Computational systems biology, Elsevier Academic Press, 2006.
  • E. Klipp, R. Herwig, A. Kowald, C. Wierling, H. Lehrach: Systems biology in practice, Wiley, 2006.
  • M. Buchanan, G. Caldarelli, P. de los Rios, F. Rao, M. Vendrscolo (Eds.): Networks in cell biology. Cambridge University Press, 2010.

Credits: 5 study points.

Grading components:

  • Lectures-based final exam counting for 40% of the grade
  • A modelling project counting for 60% of the grade

Time schedule: October 28 – December 12, 2019.

  • Lectures: Mondays and Wednesdays 12.15 – 14.00 in room XVII (Natura).
  • Project discussions and exercises (when needed): Thursdays (from 7.11 on) 10.15-12.00 in room M3 (Quantum).

Lecturers

Ion Petre and Mikhail Barash, Department of Mathematics and Statistics, University of Turku, ion.petre, mikbar ‘AT’utu.fi.

Exam dates:

  • First course exam: 16.12.2019, 14-18, rooms IX, X.
  • Second course exam: 8.1.2019, 9-13, rooms IX, X.

Lecture slides:

Exercises

  • Set 1. Due: November 28, 2019. Solutions can be found here.
  • Set 2. Due: December 5, 2019. Solutions can be found here.
  • Set 3. Due: December 11, 2019. Solutions can be found here.
  • Set 4. Due: December 12, 2019. Solutions can be found here.