Computational Biology, Genomes and Evolution

How can innovative analyses be used to explore natural variation and uncover novel biological mechanisms?

To understand the vast complexity in biology, many research projects at the Vienna BioCenter have integrated algorithm development, modeling, and high-throughput processing of data. These computational tools enable the next-level analysis of large amounts of biological data, ranging from high-throughput microscopic images to genome sequences or thousands of single-cell transcriptomes, and are also indispensable for evolutionary biology research.

Computational biology has become the common thread that runs through all the Research Areas at the Vienna BioCenter. It involves developing and applying data-analytical and theoretical methods, mathematical modeling, and computational simulations to describe diverse biological functions at different spatial scales. Since the sequencing of the first whole genome in 1995, all major model organisms and hundreds of other species have had their genomes wholly or partially sequenced. This has led to genome-wide analyses becoming commonplace in many research institutions, and groups at the Vienna BioCenter use them extensively to study genomes (including their 3D organization), epigenomes, and transcriptomes. In turn, this has necessitated innovative strategies to process, analyze, and store the resulting datasets. Computational methods are also heavily used in biophysics, structural biology, and imaging at the Vienna BioCenter; for example, molecular dynamics uses computer simulations to model the structure of biomolecules and their interactions with the environment. 

Evolution is the unifying theory of the biological sciences: as Theodosius Dobzhansky put it, “nothing in biology makes sense except in the light of evolution”. Various aspects of evolutionary biology research are ongoing at the Vienna BioCenter, with one particular stronghold being the analysis of genomic and epigenomic variants to assess natural variation within and between populations. Such analyses provide insights into complex traits, adaptation, speciation,and evolutionary ecology (e.g., how competition between and within species has evolved). Some groups use comparative genomics to focus on the evolution of specific biological systems, such as biological clocks, hormone systems, or gene regulation.

Finally, the Mathematics and BioSciences Group and the Center for Integrative Bioinformatics Vienna develop mathematical methods and models that mimic the process of evolution.

Research Groups "Computational Biology, Genomes and Evolution"

Research Group Institute Topic
Becker GMI Genomics and epigenomics of plant-plant and plant-environment interactions
Berger GMI Chromatin Architecture and Function
Burga IMBA Molecular determinants of biological idiosyncrasy
Campbell Max Perutz Labs Mechanisms that ensure chromosome segregation fidelity in mitosis
Dolan GMI Development and Evolution of Land Plants
Goloborodko IMBA Theoretical Models of Chromosome Structure
Hermisson Max Perutz Labs Mathematics and BioSciences Group (MaBS)
Mari-Ordonez GMI Mechanisms of recognition and silencing of transposons in plants
Menche Max Perutz Labs Quantitative Modelling of Biological Networks
Nordborg GMI Population Genetics
Raible Max Perutz Labs Origin and Diversification of Hormone Systems
Ramundo GMI Organelle biogenesis, Organelle protein quality control, Chloroplast biotechnology
Rivron IMBA Synthetic Development
Stark IMP Understanding transcriptional regulation
Swarts Max Perutz Labs Tree-ring genomics
Swarts GMI Tree-ring genomics
Tanaka IMP Molecular mechanisms of vertebrate regeneration
Tessmar Max Perutz Labs Lunar periodicity and inner brain photoreceptors
von Haeseler Max Perutz Labs CIBIV - Center for Integrative Bioinformatics Vienna
Zagrovic Max Perutz Labs Molecular Biophysics