Module Identification from Biological High-Throughput Data

Background

A central goal of postgenomic research is to assign a function to every predicted gene. Because genes often cooperate in order to establish and regulate cellular events, the examination of a gene has also included the search for at least a few interacting genes. This requires a strong hypothesis about possible interactions partners, which has often been derived from what was known about the gene or protein beforehand. Many times, though, this prior knowledge has either been completely lacking, biased towards favored concepts, or only partial due to the theoretically vast interaction space. With the advent of high-throughput technology and robotics in biological research, it has become possible to study gene function on a global scale, monitoring entire genomes and proteomes at once. These systematic approaches aim at considering all possible dependencies between genes or their products, thereby exploring the interaction space at a systems scale.

Goals

In this project, the goal is to develop methods for the identification of modules of genes or other entities from high-throughput data. On the one hand, we focus on advanced biclustering techniques that allow to find subgroups of genes that show the same response under a subset of conditions; in comparison to standard clustering methods, this approach often better reflects biological reality as genes can be related to multiple, distinct pathways. On the other hand, we develop concepts for the integration of multiple data types based on multiobjective models.

Collaborators

• Philip Zimmermann and Wilhelm Gruissem, Plant Science Laboratory, ETH Zurich
• Markus Friberg, Institute of Computational Sciences, ETH Zurich.
• Nicola Zamboni and Uwe Sauer, Molecular Systems Biology Laboratory, ETH Zurich.

Funding

Own resources of the professorship.

Materials

• Supplementary material for the study integrating multiple types of high-throughput data [cbz2006a].

Publications

[1 — bzfz2008a]

S. Bleuler, P. Zimmermann, M. Friberg, and E. Zitzler. Discovering Trends in Gene Expression Data Using a Hybrid Evolutionary Algorithm. Algorithmic Operations Research, 3(2), 2008. (bibtex)

[2 — bz2007b]

S. Bleuler and E. Zitzler. Discrimination of Metabolic Flux Profiles Using a Hybrid Evolutionary Algorithm. In Genetic and Evolutionary Computation Conference (GECCO 2007), pages 354–360, 2007. (PDF) (bibtex) (suppl. material) Best paper award at GECCO '07 in 'Biological Applications Track'

[3 — cbz2006a]

M. Calonder, S. Bleuler, and E. Zitzler. Module Identification from Heterogeneous Biological Data Using Multiobjective Evolutionary Algorithms. In T. P. Runarsson et al., editors, Conference on Parallel Problem Solving from Nature (PPSN IX), volume 4193 of LNCS, pages 573–582. Springer, 2006. (PDF) (bibtex) (online access) (suppl. material)

[4 — bz2005a]

S. Bleuler and E. Zitzler. Order Preserving Clustering over Multiple Time Course Experiments. In EvoWorkshops 2005, volume 3449 of LNCS, pages 33–43. Springer, 2005. (PDF) (bibtex) (online access)