Amsterdam Data Science – Life Science Node (ADS-Life), a/o Prof.dr. J. Heringa, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands
Prof. dr. J. Heringa; Other PIs: Prof. dr. B. Teusink; Prof. dr. A.K. Smilde; Prof. dr. H.V. Westerhoff; Prof.dr. G.W. Klau
0031 (0)20 5987649 0031 (0)20 5983563
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.
- Biomedical & health
- Industrial biotech
- Bioinformatics/Systems Biology (Computational Biology)
- Single-cell analysis
- Fermentation technology
- Protein structure and function
- Biological networks
Expertise and Track Record
We have a longstanding, international, and integrative tool-oriented and versatile bioinformatics and systems biology experience. The ADS-Life groups have a unique critical mass with wide coverage in methods and application areas. Some of us do experiments as well, and so we are well connected to experiments and biologists. There is partial embedding within the new VU/UvA Department of Informatics, comprising strong liaisons with expert groups aiding formal cellular modelling, semantic web capabilities (big data integration) and computational sciences. Moreover, we will be in the vicinity of expertise in biological chemistry (ligand docking, chemical biology platforms for screening), microbiology and cell biology, all within the new Life Science building O|2 (operational in 2015).
==  Integrative protein motif searching: using biological knowledge about a special cellular secretion system in TB (Type VII) we have written a search tool that combines motif searching
with secondary structure prediction. This tool led to the identification of
large numbers of proteins that are likely to be secreted by the TB type 7 system.
– Daleke MH, Ummels R, Bawono P, Heringa J, Vandenbroucke-Grauls CM, Luirink J, Bitter W (2012). A general secretion signal for the mycobacterial type VII secretion pathway. Proc. Natl. Acad. Sci. USA, 109(28): 11342-11347. Combining various prediction and motif finding techniques to unify excretion signals for the type VII secretion pathway in TB.
==  Protein structure prediction: We have made a homology model of an estrogen receptor and 14-3-3 protein that has lead to the discovery of a novel inhibition mechanism of the estrogen
receptor with a plant fungus compound. As the majority of breast tumors depends on a functional estrogen receptor for tumor cell growth, the compound Fusicoccin now serves as a lead to develop new drugs against this disease.
– Ingrid J. De Vries-van Leeuwen, Daniel da Costa Pereira, Koen D Flach, Sander R Piersma,
Christian Haase, David Bier, Zeliha Yalcin, Rob Michalides, K. Anton
Feenstra, Connie R Jiménez, Tom F.A. de Greef, Luc Brunsveld, Christian
Ottmann, Wilbert Zwart, Albertus H. de Boer (2013). 14-3-3 protein interaction with the Estrogen Receptor Alpha F-domain
provides a drug target interface, PNAS, accepted.
==  Genome-scale modeling: We have developed a genome-scale model for a vaccin
production strain of GSK and used it to optimise production media with
significant higher yields. (patent filed by GSK, paper in preparation).
==  We have developed a large number of multivariate-statistical tools and best practices to aid the analysis of high-end metabolomics data.
– Saccenti E, Hoefsloot HCJ, Smilde AK, Westerhuis JA, Hendriks MMWB (2014) Reflections on univariate and multivariate analysis of metabolomics data, Metabolomics, 10:3 (2014), pages 361 – 374.
==  We have developed a number of network comparison tools and network module detection methods and applied these to a number of biological settings.
– Dinkla K, El-Kebir M, Bucur C-I, Siderius M, Smit M, Westenberg M, Klau GW (2014). eXamine: Exploring annotated modules in networks. BMC Bioinformatics 15:201.
- Boele J, Olivier BG, Teusink B: FAME, the Flux Analysis and Modelling Environment (2012). BMC Syst Biol 6:8. This paper shows a software tool we developed for web-based modelling of genome-scale models.
- Bonzanni, N., Krepska, E., Feenstra, K.A., Fokkink, W., Kielmann, T., Bal, H., and Heringa, J. (2009). Executing Multicellular Differentiation: Quantitative Predictive Modelling of C. elegans Vulval Development. Bioinformatics, 25(16):2049-2056. Coarse-grained modelling technique based upon Petri-net technology applied to multicellular modelling of a developmental process in C. elegans. Biological processes such as cell transport, regulation, signalling, degradation etc. are all included in the model, which is able to reproduce developmental patterns and has generated several testable hypotheses concerning mi-RNA and gene knock-out experiments.
- Bonzanni N, Garg A, Feenstra KA, Kinston S, Miranda-Saavedra D, Schutte J, Heringa J, Xenarios I and Göttgens B (2013). Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model. Bioinformatics, 29(13): i80-i88. State space analysis using a Boolean haematopoietic cis-regulatory model based upon Petri-net technology - in order to reproduce known mutational effects on the regulatory behaviour, a new inhibitory link was inserted in the model, which was subsequently confirmed by wet-lab experimentation.
- Daleke MH, Ummels R, Bawono P, Heringa J, Vandenbroucke-Grauls CM, Luirink J, Bitter W (2012). A general secretion signal for the mycobacterial type VII secretion pathway. Proc. Natl. Acad. Sci. USA, 109(28): 11342-11347. Combining various prediction and motif finding techniques to unify excretion signals for the type VII secretion pathway in TB.
- Khandelwal RA, Olivier BG, Röling WF, Teusink B, Bruggeman FJ. (2013) Community flux balance analysis for microbial consortia at balanced growth. PLoS One. 2013 8(5). An extension of normal flux balance analysis to microbial communities, a step towards modeling ecosystems based on metagenome data.
- Kolodkin A, Sahin N, Phillips A, Hood SR, Bruggeman FJ, Westerhoff HV, Plant N. (2013) Optimization of stress response through the nuclear receptor-mediated cortisol signalling network. Nat Commun. 4:1792. Example how detailed kinetic modeling provides deeper insight in the functioning of signaling networks.
- Smilde AK, Timmerman ME, Hendriks MM, Jansen JJ, Hoefsloot HC. (2012) Generic framework for high-dimensional fixed-effects ANOVA. Brief Bioinform. 13(5):524-35. Review about the different multivariate analysis methods that take advantage of high dimensional data with an underlying experimental design.
- van Heerden JH, Wortel MT, Bruggeman FJ, Heijnen JJ, Bollen YJM, Planqué R, Hulshof J, O'Toole TG, Wahl SA, Teusink B (2014) Lost in transition: startup of glycolysis yields subpopulations of nongrowing cells. Science 343(6174). Bi-stability analysis using an ODE model demonstrating that cellular decisions taken in the first few minutes of yeast glycolysis are critical.
- Vis DJ, Westerhuis JA, Hoefsloot HC, Roelfsema F, Hendriks MM, Smilde AK. (2012) Detecting regulatory mechanisms in endocrine time series measurements. PLoS One. 7(3). Example of time-series analysis, as one of the examples of multivariate data analyses.
- Thiele I, … Westerhoff HV, Kell DB, Mendes P, Palsson BØ. (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol. (5):419-25. A consensus map of human metabolism that provides a basis for advances data integration and modeling of metabolism.
BioSB: Heringa, Scientific Director;
DTL: Heringa, Senior Adv. Comm., Partner Adv. Comm. (representing VU);
ELIXIR (EU ESFRI): Heringa, Deputy Head of Node ELIXIR-NL;
ISBE: Westerhoff, Work Package leader;
NCSB: Teusink, Board Member, Internal Advisory Counsel (Chair);
NMC: Smilde, MT member;
CTMM TraIT: Heringa, CTO Work Package 4/5 (Abeln)
- The ADS-Life groups have a unique critical mass with wide coverage in methods and application areas.
- Overview of Bioinformatics tools: http://www.ibi.vu.nl/programs/