Amsterdam Data Science – Life Science Node

The life science faculties of both VU and UvA will move to a single new location (O|2 Life Sciences building) on the VU campus in 2015, where a number of bioinformatics, data integration and systems biology experts will be housed together. Together they will form a comprehensive group with critical mass and breadth in computational (but also in-house experimental) biology. The life science data expertise is additionally concentrated within the nascent Amsterdam Data Science -Life Science node (ADS-Life), which is new Life Science data expertise centre with a data stewardship and support function. The ADS-Life node will operate in conjunction with the Amsterdam Data Science centre (http://amsterdamdatascience.nl), endorsed by VU, UvA and CWI.

The combined expertise within ADS-Life covers the complete range of the cycle “from life science data to understanding and back”, allowing for a vast number of combinations to analyse and/or model the data. One of the main missions of the ADS-Life node is bridging the gap between biologists and the computational data scientists, by developing a core group of data stewards who are completely versed in the biomedical issues of the “clients’.

The expertise can be grouped in three broad categories:

==== 1. Genome sequence and protein function/dynamics (Heringa);
1.1 Sequence Analysis – Over the years a number of software methods have been developed in the group (http://www.ibi.vu.nl/programs/). Using these and other software packages, we have significant experience in sequence alignment, motif detection, and structure and function specificity prediction.
1.2 Coarse grained modelling of protein dynamics – We have compiled various coarse-grained techniques for simulating protein-protein interaction (e.g. based upon the Martini force field) or protein agglutination (e.g. lattice modelling).

==== 2. Biological network analyses (Westerhoff, Teusink, Heringa, Klau);
2.1 Genome-scale metabolic models (Westerhoff, Teusink) – we develop new theory, algorithms and also software tools and apply them to solve biological problems related to metabolism, mostly in microorganisms but we also have an interest in mammalian systems such as cancer metabolism.
2.2 Mechanistic (ODE-based) models (Teusink, Westerhoff) – we are experts in enzyme kinetics and development of dynamic models to study systems behaviour, from ligand-bias in GPCR signalling to growth strategies and metabolic regulation. We also apply optimisations under constraints and experimental evolution to understand several aspects of biological network design, from noise management to fitness landscapes. We combine theory and quantitative experimentation.;
2.3 Formal modelling of cellular processes (Heringa) – A software suite has been developed based upon Petri net technology for coarse-grained biological network modelling, including multiple-cell approaches. These techniques are especially helpful when biological data is incomplete or do not have the level of detail (e.g. concentration measurements) to make these amenable to ODE-type modelling techniques. The techniques allow easy integration of various network levels, including signalling, regulation, PPI and metabolic networks.
2.4 Network alignment and module delineation (Klau) – We have developed state-of-the-art computational techniques to compare and analyse large complex biological networks.

==== 3. Multivariate data analysis and integration (Smilde);
3.1 Multivariate data analysis – new theory and methods are developed and applied to large-scale data sets, from metabolomics, transcriptomics to patient information. Smilde is part-time at the AMC to establish the bridge between data analysis and biomedical questions.;
3.2 Integrative data modeling – we develop methods to enrich and refine statistical data integration methods with prior knowledge, either about the experiment or about the biological system under study.;
3.3 Parameter identifiability, estimation and experimental design – here we offer expertise more than specific tools or extensive method development. Complementary technological expertise includes: enzymology, metabolomics, single-cell analysis, fermentation technology. Biological application areas: protein sequencing, protein structure, protein function, biological networks, metagenomics.