Providing expertise, advice, and training for scientists across EMBL, in topics and techniques relevant for many groups and units.
Bio-IT is a community initiative to provide support to computational biology research at EMBL, through training, courses, consulting, networking opportunities and computational resources and tools. As a community project, efforts are driven by users and for users through volunteer sharing of knowledge, experience and skills.
EMBL staff collaborates to a very high degree, in a network of spontaneous interactions that usually spring up between two or more research groups from different disciplines. Complementing these excellent interactions, new interdisciplinary research instruments are required to meet the challenge of integrating more than two groups across disciplines. EMBL Centres have been established as a new type of 'horizontal', cross-departmental structure to promote innovative research projects across disciplines.
The Centre for Bioimage Analysis (CBA) supports scientists in extracting quantitative information from images acquired with light- or electron-microscopy.
Support is provided on various aspects of image analysis, such as:
The CBA closely collaborates with the Advanced Light Microscopy Facility and the Electron Microscopy Core Facility in order to ensure optimal image acquisition modalities for downstream quantitative analysis.
Together with ALMF, EMCF and EMBL’s IT department, the CBA provides access to dedicated image analysis software, running on high-performance virtual workstations and the high performance compute cluster (internal access only).
In addition to project specific consultancy, the CBA also offers regular courses on image analysis and software packages such as ImageJ, CellProfiler and Imaris.
Finally, for bioimage analysts spread across EMBL research units, the CBA serves as a platform to share developments, exchange expertise, and learn about advances and new approaches in computational image analysis.
Building up on the breakthroughs of molecular biology, scientists are increasingly considering the complex interactions between the many identified molecules rather than the individual molecules alone. These interactions are inherently non-linear, often include spatial aspects such as dynamically induced relocations and occur on widely varying spatial and temporal scales.
The investigation of such systems is inherently difficult. In the last decade, the realisation of these challenges has lead to the increased adoption of mathematical modelling techniques originally developed in physics and engineering into biological research. As a rather novel development, these methods have not yet found wide consideration in undergraduate teachings.
The general aim of the Centre for Biological Modelling is to assist researchers at EMBL in the adoption of modelling into their research workflows. To this end, it provides weekly consultation appointments, performs collaborative modelling tasks, trains EMBL members in relevant programming languages and software tools and will foster collaboration and interactions between EMBL researchers using seminars, journal clubs as well as interdisciplinary retreats. The expertise of the centre resides in first principles modelling using Ordinary, Partial and Stochastic Differential Equations, Constraint-Based Modelling such as Flux Balance Analysis as well as Boolean and Fuzzy Logic Modelling approaches.
The Centre for Statistical Data Analysis (CSDA) helps EMBL scientists to use adequate statistical methods throughout their research projects, from the planning phase to data analysis. The aim is to provide expertise on a broad range of computational methods, such as data visualization, testing, regression, clustering, classification, and quality control.
The CSDA offers (1) consulting sessions, where the focus is on the individual research questions and how to quantitatively address them, (2) collaborations, which may arise from consulting projects, and (3) courses on statistical topics and scientific programming.
The mission of the centre (CBNA) is to disseminate expertise, know-how and guidance in the field of biological network integration and analysis throughout EMBL to computational biologists and experimentalists alike. For expert users, bioinformaticians spread across EMBL Research Units, it serves as a platform to share resources, exchange expertise, and learn about advances and new approaches in the computational analysis of networks. At the same time offering support to experimentalists with less computational expertise, who wish to pursue large-scale biology or to place small-scale mechanistic experiments in the context of existing networks.
Scientific support is focused on the systematic reconstruction and analysis of networks from a functional and topological perspective through the application of graph-theoretic approaches. Exploration of the structural behaviour of networks, characterisation of molecular functions, and differential analysis in the context of integrated experimental data on a global scale provide effective means for analysing complex systems with great potential for biomedical applications.
As an EMBL-wide platform with the aim to work for you and with you, we offer collaboration, consulting on research projects, and organization of seminars and courses.
Chemistry has never been more important for biological research than today. The continual development of new reagents, probes and inhibitor technologies are enabling increasingly precise and fine grained understanding of macromolecular function in the cell.
The EMBL Centre for Chemical Biology promotes and supports researchers using chemical approaches across all the EMBL sites. The Centre organises a regular Chemical Biology retreat bringing together EMBL researchers seeking to create even better molecular tools for helping to understand biology. It also organises biannually one of the largest Chemical Biology conferences worldwide.
From microscopy to mycology, from development to disease modelling, EMBL researchers cover a wide range of topics in the biological sciences.