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Optimisation Techniques in Bioinformatics and Systems Biology

Part of the NBIC PhD school

  • Dr. J.A. Kaandorp (Jaap; coordinator)
    • Section Computational Science, Faculty of Science, University of Amsterdam

  • Prof. Dr. A.H.C. van Kampen (Antoine)
    • Bioinformatics Laboratory, Academic Medical Center
    • Biosystems Data Analysis group, University of Amsterdam

  • Prof. J. Heringa (Jaap)
  • Dr. T. Binsl (Thomas)
  • Dr. H. Hettling (Hannes)
    • Centre for Integrative Bioinformatics, Free University

Course dates: 17 may - 21 may (2010; full week course)

Location: Room F3.20, F-building (entrance NIKHEFH building) Science Park 107; Faculty of Science, University of Amsterdam


  • Bring your laptop (and pocket calculator).

NBIC PhD School

NBIC PhD School: advanced courses for bioinformaticians
In the field of bioinformatics, there is a continuous flow of new insights, tools and applications. The NBIC PhD School targets the need to stay up to date through an advanced programme developed and taught by experts with hands-on experience. The courses cover a variety of topics and technologies and allow the creation of a personalised education programme that specifically fits your research and interests. The NBIC PhD school courses are accessible for PhD students and post-docs worldwide. to broaden the international scope of the NBIC PhD School, partnerships with other institutes, for example with the Swiss Institute for Bioinformatics (SIB), are developed.

In short, the NBIC PhD School aims to:
  • Offer a top-level education and training programme in bioinformatics
  • Create opportunities for PhD students to broaden their scientific scope
  • Provide an environment for international networking and exchange

Optimisation module

*Goal optimisation module *
After this course a student should have an overview and basic understanding of optimisation methods frequently applied in bioinformatics and systems biology.

Target audience
The course is aimed at at PhD students with a background in bioinformatics, systems biology, computer science or a related field. A working knowledge of basic statistics, linear algebra and differential equations is assumed but will be reviewed during the first day of the course.

Optimization, in general, is concerned with finding one or more optimal solutions given a problem. Many optimization problems are very difficult to solve. In many different problems from bioinformatics and systems biology (e.g. multi parameter estimation, reverse engineering of gene networks, multi-alignment problem, 3D structure prediction etc.) various optimisation methods are applied.

In this course you will get acquainted with the underlying mathematics of optimization and with a selection of local and global optimization methods. In addition, several examples of optimization problems in life sciences will be presented and discussed.

We will compare methods like linear programming, steepest descent and conjugate gradient. We will discuss global optimisation methods like Monte-Carlo sampling, the Basin hopping techniques (aka Monte-Carlo with minimization - e.g. ICM (internal co-ordinates system for protein 3D structure prediction), simulated annealing and evolutionary algorithms. Applications of stochastic optimisation in systems biology, hybrid methods using stochastic optimisation in combination with local search will be discussed.

Examples will that will be discussed during the course include: (a) multi-sequence alignment with simulated annealing and evolutionary algorithms and comparison to dynamic programming, (b) parameter estimation in models of large biochemical networks and the application in reverse engineering of spatio-temporal models of gene regulation, (c) others

Download the Optimization flyer (docx).


Teaching material

Topic revision: r9 - 27 May 2020, UnknownUser Search
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