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Optimisation Techniques in bioinformatics and systems biology

General info

Date 
17 May 2010 - 21 May 2010 
Location
Science Park, Amsterdam
Website
www.nbic.nl/education/nbic-phd-school/course-sched...
Keywords
local optimalisation methods, Global Optimalisation (e.g. Monte-Carlo sampling, Basin hopping technique), Global Stochastic Optimalisation (simulated annealing, evolutiony algorithms), Dynamic Programming
Organiser
UVA, Section Computational Science
Teacher(s)
Jaap Kaandorp
Kampen, van Antoine Prof. dr.
Heringa, Jaap Prof. dr.
Contact(s)
Gelder, van Celia Dr.

Description

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.

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