Computational Biology, Genomes and Evolution
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.
|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|
|Raible||Max Perutz Labs||Origin and Diversification of Hormone Systems|
|Ramundo||GMI||Organelle biogenesis, Organelle protein quality control, Chloroplast biotechnology|
|Stark||IMP||Understanding transcriptional regulation|
|Swarts||Max Perutz Labs||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|