TOM – 16.11.2016

Venue: Biomedicum 1, Seminar room 1-2

Timing: 15:30 onwards

Presenter: Teemu Kivioja, Taipale Lab, Genome-Scale Biology Program

Utilizing integer linear programming in experiment design and analysis

Many tasks in bioinformatics involve choosing the best combination
from a vast number of different alternatives, for example the optimal
set of molecular probes from a large number of candidate probes. Many
such discrete optimization problems can be conveniently expressed in
Integer Linear Programming (ILP) or more generally Mixed Integer
Linear Programming (MILP) framework. The improvement of general ILP
solvers together with the increase in computing power has made it
possible to optimally solve many problem instances of small to medium
sizes without developing a task-specific algorithm or resorting to
heuristics. I will introduce how one can use ILP to solve experiment
design and analysis problems through simple examples taken from my own
research including molecular barcode design, CRISPR/Cas9 guide RNA
selection, and selecting a representative set of transcription factor
binding motifs.