Swiss-Prot

Swiss-Prot is a manually curated biological database of protein sequences. Swiss-Prot was created in 1986 by Amos Bairoch during his PhD and developed by the Swiss Institute of Bioinformatics and the European Bioinformatics Institute. Swiss-Prot strives to provide reliable protein sequences associated with a high level of annotation (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other databases. It is part of the UniProt Consortium.

SIB Contact: This e-mail address is being protected from spambots. You need JavaScript enabled to view it / Lydie Bougueleret

EPD: Eukaryotic Promoter Database

The Eukaryotic Promoter Database is an annotated non-redundant collection of eukaryotic POL II promoters, for which the transcription start site has been determined experimentally. Access to promoter sequences is provided by pointers to positions in nucleotide sequence entries. The annotation part of an entry includes description of the initiation site mapping data, cross-references to other databases, and bibliographic references. EPD is structured in a way that facilitates dynamic extraction of biologically meaningful promoter subsets for comparative sequence analysis.

Proteomic Software and Infrastructure: Swiss Grid Portal

 The primary goal of this project is to select, enhance, deploy and test a fully functional Grid portal user interface. It will be fully integrated with the infrastructure provided through the Swiss Multi-Science Computing Grid (SMSCG) project. The application driving the project will be the Proteomics Identification Toolbox (swissPIT) developed at SIB. The swissPIT end-users require a high-level, easy-to-use interface in order to perform their research in the domain of life sciences. There already exist several portals for end-user access to Grid resources in many other projects. However, most of these portals are specifically customised to the given application at hand and are not easily adaptable to new applications. Very few have integrated workflow capabilities, which is the prerequisite for most high-level applications. Most existing portals provide a web version of the command-line tools provided by the Grid middleware. By building the portal with the strong involvement of a user community (Life Sciences – Proteomics) the usability concept of existing portals can be improved.
The new swissPIT Swiss Grid Portal (an early prototype portal already exists) will be built with reusability in mind, such that the actual Proteomics specific parts are clearly separated and customisable for future applications, not only in Life Sciences but also from other scientific domains.

 ExPASy

ExPASy (Expert Protein Analysis System) is a proteomics server of the Swiss Institute of Bioinformatics (SIB) which analyzes protein sequences and structures and two-dimensional gel electrophoresis (2-D Page electrophoresis). The server functions in collaboration with the European Bioinformatics Institute. ExPASy also produces the protein sequence knowledgebase, UniProtKB/Swiss-Prot, and its computer annotated supplement, UniProtKB/Trembl.

 EMBnet Facility

The EMBnet group's main focus is to provide bioinformatics services to support biomedical researchers. Our activities are divided among Teaching, Helpdesk, and maintenance of up-to-date Web Tools & Databases.
The EMBnet group supports biomedical researchers by providing a helpdesk that often leads to fruitful collaborations. Our fields of expertise are sequence analysis and protein modelling.

 SWISS-Dock

SwissDock is a web interface being developed to allow researchers around the world to perform EADdock based docking simulations, as well as fragment-based drug design and lead optimization.

 SWISS-MODEL

SWISS-MODEL is a fully automated protein structure homology-modeling server, accessible via the ExPASy web server, or from the program DeepView (Swiss Pdb-Viewer). The purpose of this server is to make Protein Modelling accessible to all biochemists and molecular biologists World Wide.

 SwissRegulon

Swissregulon is a database with genome-wide annotations of regulatory sites. The annotations are based on:

• known sites from the literature

• predictions using orthologous intergenic regions from related species

• predictions using a combination of ChIP-on-chip data and orthologous intergenic regions

• predictiions using a combination of literature target genes and orthologous intergenic regions

STRING

STRING is a database of known and predicted protein interactions.
The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources:
Genomic Context; High-throughput Experiments; (Conserved) Coexpression; and Previous Knowledge.
STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 2,483,276 proteins from 630 organisms.

Activity Type: Service
SIB Contact: Christian von Mering
Date: 01.01.2008-31.12.2011
# of Partners:1
URL: STRING

MirZ: an integrated microRNA expression atlas and target prediction resource

MirZ is a server containing data about miRNA expression as well as miRNA target predictions.

New systems biology software for large-scale modular analysis of data

 The Bergmann Computational Biology Group develops concepts and algorithmic tools for the analysis of large-scale biological data. The focus is on the integration of genotypic and phenotypic datasets from eukaryotic cells or clinical studies. As a service to the community there are three software packages in preparation: (1) QuickTest for fast genome-wide association studies with uncertain genotypes, (2) Bioconductor package for the visualization and analysis of expression modules, (3) R-package for the implementation of the Iterative Signature Algorithm and the Ping-pong Algorithm for module and co-module identification, respectively.

Biostatistics Service

 Vital-IT

Vital-IT is an innovative life science informatics initiative providing computational resources, consultancy and training to connect fundamental and applied research. It is a collaboration between the Swiss Institute of Bioinformatics (SIB), the Universities of Lausanne and Geneva, the Ludwig Institute for Cancer Research, the Swiss Federal Institute of Technology, Lausanne (EPFL), Hewlett Packard Company, Intel Corporation and Oracle. These partners form an alliance of unrivalled expertise in the processing and analysis of biological information.  Using their complementary competencies, they provide fundamental science and leading edge technology for the construction of a world-class high-perfomance computing platform, and the expertise to allow it to be exploited effectively for solution of both scientific and commercial problems.

Vital-IT provides infrastructure and computational expertise to support research conducted primarily by its partners, and develops hardware and software solutions to allow research results to be turned into marketable products. Additionally, the group serves as an interface between academic research and its consumers in the commercial world.

Hardware acceleration of Generalized Born for simulation-based drug design and molecular modelling of proteins

Bio-simulations of proteins bring key insights regarding their conformational mode and their interaction with cellular membranes with a rational approach. Alternatively, such simulations are used to study the binding modes of synthetic compounds on protein active sites. These simulations are part of the molecular modeling field. The molecular modeling field opens new avenues for identifying new target based on gene sequences, identifying and optimizing leads. The full potential of molecular modeling is not achieved because of the limited computing power of currently available computers, the complexity and costs related to deploying large computing centers. Indeed simulations of several months are needed to study the behavior of small proteins in a 100 nanosecond time scale whereas several microseconds are needed to reach strong predictive biological significance. The UNIL-SIB Molecular Modeling Group and XLBiosim SA join forces to develop and validate a new hardware based acceleration system for drug design and protein modeling. This product is based on Generalized Born model which is widely used in the molecular modeling community, for applied as well as fundamental research. It includes a custom hardware chip that is integrated with the widely used software package CHARMm. This product will deliver 10x speedups for molecular simulation based on Generalized Born when compared to commercial processors of the same silicon technology generation.

Image analysis based on physical models and optimal non-lineal state observers

Goals:
• Develop optimal Bayesian estimation methods for image processing
• Understand imaging as a non-linear dynamic system
• Develop theories to include topological constraints in particle filters variational level sets
• Develop sparse, multi-resolution shape representations to reduce the dimensionality of the problem
• Implement the new segmentation and tracking algorithms in a user-friendly software with a GUI
Application goal: Develop and implement robust and quantitative image segmentation and tracking methods for life cell fluorescence microscopy. Further funding by SystemsX.ch

MSight

Proteomics techniques have evolved dramatically over the last ten years and have reached a level of maturity. Most current proteomics experiments are aimed at the quantitative profiling of proteins in complex mixtures using high-resolution separations (such as liquid chromatography (LC), capillary electrophoresis or 1D- and 2D-PAGE) followed by mass spectrometry (MS). MS is increasingly used after the separation step to acquire peptide mass and sequence data at a high sampling rate, producing datasets that are highly correlated. Considering that a single dataset can produce thousands of mass spectrum, a high demand has been raised for tools allowing to efficiently assess the quality and reproducibility from these high-throughput data (often over 100 Mbytes).

MSight, created by the Proteome Informatics Group, was specifically developed for the representation of mass spectra along with data from the separation step. The software allows graphical exploration inside huge dataset and gives the scientist access to information that previously was hidden.

BASysBio: Bacillus Systems Biology

BaSysBio aims to achieve major breakthroughs in the understanding of the regulation of gene transcription in bacteria at a global scale. The highly dynamic gene regulation is mediated by transcription factors (TF) that trigger or repress the expression of their target genes. Transcription control is embedded into a hierarchical flow of information from genes to phenotype in which many regulatory steps can occur. BaSysBio adopts a systems biology approach in which quantitative experimental data will be generated for each step of the information flow, and will fuel computational modelling. High-throughput technologies (living cell arrays, tiling DNA microarrays, multidimensional liquid chromatography proteomics and quantitative metabolomics) will be developed in conjunction with new computational modelling concepts to facilitate the understanding of biological complexity. Models will simulate the cellular transcriptional responses to environmental changes and their impact on metabolism and proteome dynamics.

The iterative process of simulations and model-driven targeted experiments will generate novel hypotheses about the mechanistic nature of dynamic cellular responses, unravel emerging systems properties, and ultimately provide an efficient roadmap to tackle novel, pathogenic organisms. This system-based strategy will enable BaSysBio i) to understand how transcriptional regulation and metabolism are quantitatively integrated at a global level; ii) to unravel cellular transcriptional responses in conditions mimicking pathogenesis.
Finally, the project will validate the general applicability of the knowledge and integrated modelling-experimental strategy developed in the highly tractable B. subtilis model towards an understanding of regulatory networks controlling pathogenesis in disease-causing bacteria. BaSysBio will make a significant contribution towards overcoming the structural obstacles that hinder the development of systems biology in Europe.

Biosapiens: Developing methods and resources in bioinformatics to focus on the annotation of human and other genomes

The objective of the BIOSAPIENS Network of Excellence is to provide an infrastructure to support a large scale, concerted effort to annotate genome data by laboratories distributed around Europe. This will use both informatics tools and input from experimentalists. Experimental validation of a statistically significant subset of the predictions will be an integral part of the process, leading to an iterative improvement in methods. The Network will bring together many of the best laboratories to create a European Virtual Institute for Genome Annotation, divided into nodes, each focussed on one aspect of genome annotation. Through integration the institute will help to improve bioinformatics research in Europe, by providing a focus for annotation and by the organisation of European meetings and workshops to encourage cooperation, rather than duplication of effort. It will also be pro-active in forging closer integration between the experimentalists and bioinformaticians, through a directed programme of genome analysis, focused on specific biological problems.

The annotations generated by the Institute will be available in the public domain and easily accessible through a single portal on the web. This will be achieved through a distributed annotation system (DAS), which will evolve to take advantage of new developments in the GRID. The BIOSAPIENS NoE will increase European competitiveness, especially for SME's, by new discoveries, increased integration, expert training and improved tools and services. The Institute will establish a permanent European School of Bioinformatics, to train bioinformaticians and to encourage best practice in the exploitation of genome annotation data for biologists throughout Europe. In summary the Institute will further a European Research area for Bioinformatics, enhancing Europe's role in the academic and industrial exploitation of genomics.

COBIOS: Engineering and control of biological systems: a new way to tackle complex diseases and biotechnological innovation

The aim of this proposal is to engineer a synthetic biological network for in vivo regular therapeutic delivery of insulin in a rhythm corresponding to normal nutrient uptake. To this end, we will engineer stable synthetic oscillator networks in yeast and mammalian systems able to express mRNA/protein levels with a pre-determined frequency and amplitude.
The synthetic oscillator network has to guarantee stable and synchronized oscillation in the cell population. The yeast system will be used as a test-bed for the synthetic biology design strategies developed in this project. In the context of the mammalian tissue, individual cellular oscillators have to be synchronized in order to fulfill the macroscopic function of an insulin delivery device. Hence, the engineering of the synthetic network involves additional inputs and outputs that enable resetting of the oscillators.
In view of therapeutic application, the desirable system would reset insulin oscillations synchronously with the circadian rhythm. Specifically, the synthetic oscillator in the mammalian system will be connected to circadian signals like PER1 and CRY. To achieve this aim, COBIOS brings together scientists from yeast and mammalian molecular biology, computer science, engineering and control theory. We will employ methods from systems dynamics and control theory to develop and implement modular control networks that enable oscillations in the networks they will be connected to.

In particular, we will address the problems of
- robustness of controller dynamics,
- suitable interfaces to the controlled networks, and
- mechanisms for regulation of the controller's dynamic characteristics (e.g. period and amplitude) through external signals that can be exogeneous (yeast system) or outputs of cellular signal processing (the circadian clock in mammalian system) at the levels of individual cells and tissues.

CRESCENDO: Consortium for Research into Nuclear Receptors in Development and Aging

Nuclear receptors function throughout development and aging as molecular integrators of complex physiological regulations, such as growth, reproduction and metabolism. To translate genetic knowledge from the Human Genome into therapy requires intimate unde rstanding of fundamental regulatory mechanisms. CRESCENDO will build on existing knowledge and infrastructures by drawing together European excellence to focus on signalling dynamics, integration of target gene responses during development and aging, and h uman genetics and pathophysiology, emphasising key nuclear receptors. State-of-the-art genomic and post-genomic techniques will lever advances on mechanisms and kinetics of nuclear receptor signalling in processes underlying development and aging. Informat ion on spatial and temporal aspects of signalling dynamics will feed into a broader vision of how nuclear receptors integrate multiple inputs in whole organisms across the life span. Focus will be on developmental targets, on brain development and aging, a nd the complex interplay between metabolism, particularly glucose and lipid signalling, and brain function. The brain is emphasised, as its disorders often cause most distress in unhealthy aging. A strong investment in bioinformatics will facilitate data e xtraction from multi-level analyses (genes to cells to organisms) and accelerate determination of targets, regulatory networks and integration of physiological responses within individuals and populations. The consortium includes molecular and developmenta l biologists, physiologists and human geneticists, and has access to major national and transnational data banks and resources. Nuclear receptor research is being revolutionised by the combination of technological advances with novel insights into signalli ng processes. CRESCENDO will ensure that European research in the field accelerates its momentum, fulfilling its potential to catalyse therapeutic innovation and to create substantial socio-economic benefit.

EMERGENCE: A Foundation for Synthetic Biology in Europe

Synthetic biology has emerged as a very recent but highly promising approach to re-organizing the scientific biological endeavor by integrating central elements of engineering design.
By applying the tool box of engineering disciplines such as electrical, mechanical, or chemical engineering and computer sciences, including the vigorous application of modeling techniques and organizing the development of novel biological systems along a hierarchical systems architecture with defined and standardized interfaces, synthetic biology aims at no less than revolutionizing the way we do bioengineering today. If successful, synthetic biology will transform bioengineering into a highly successful and sustainable life science industry. However, such an endeavor requires urgently a coordination effort from the very beginning in order to point the transitions into the most promising directions.
We will establish this coordination on several levels:
- we will include the majority of European scientists and engineers currently active in the field and reach out to include crucial developments in the USA and Asia via a communication platform;
- we will establish the intellectual foundation for synthetic biology by recruiting the required competence from neighboring engineering disciplines;
- we will contribute to the formation of concepts and the implementation of state-of-the-art design methodology by starting to implement a dedicated IT infrastructure;
- we will start providing the intellectual fundamentals map the most promising approaches to standardizations of procedures and parts; and
- we will embed the early development of synthetic biology into the most meaningful industrial context by exploring the industrial interface including IP issues.
In addressing these aspects, we will provide a firm foundation for synthetic biology to prosper in Europe and to fulfill indeed its role as a future engine for economic growth.

ENFIN: an experimental network for functional integration

We propose to form a Network of Excellence in the area of bioinformatics to provide a Europe-wide integration of computational approaches in systems biology. This network will be focused on the development and critical assessment of computational approache s in this area, but uniquely will bring together a range of backgrounds and laboratory contexts that will span investigative computer science through to traditional wet-bench molecular biology. Despite the progress in bioinformatics methods and databases, even the best experimental labs use . only a small number of computational tools in their work and rarely exploit the potential of multiple datasets. This network will enable a transformation of the way computational analysis is used in the laboratory and the infrastructure will be entirely open, analogous to the genome information. To achieve its goals, the network will internally have close collaboration between experimental and computational research, with a specific consumables budget for experimentally testing predictions. The computational work includes the development of a distributed database infrastructure appropriate for small laboratories and development of analysis methods including Bayesian networks, metabolite flux modeling and correlations of protein modifications to pathways. The experimental techniques used to test this system include mass spectroscopy, synthetic peptide biochemistry and RNAi knockdown. Where appropriate we have chosen experimental areas connected to intracellular signaling a ssociated with the cell cycle. An additional benefit will be greater understanding in this area. Overall the ENFIN network will deliver four main products: a platform for database provision of diverse biological data (the ENFIN core), a suite of analysis t ools integrated with this platform (the ENFIN analysis layer), extensive documentation written from the wet laboratory perspective and "best practice" guidelines for the use of computers in science.

EURASNET: European alternative splicing network of exelence

Posttranscriptional events must play a more central role in generating the highly complex proteomes of metazoa than previously believed. This is shown by the large discrepancy between the number of coding sequences of metazoan genomes and the known diversi ty of gene products that they generate. Indeed, alternative pathways for processing the primary transcript of a gene can theoretically generate more transcripts and, ultimately, more protein variants (i.e., isoforms) than the number of classically defined genes in an entire organism. Thus, the process of alternative splicing can greatly expand the information content of genomes. Alternative splicing is an essential component of gene regulation, affecting every aspect of eucaryotic biology, and understanding the mechanisms that lead to alternatively spliced transcripts is essential for the functional interpretation of genomic sequences. In addition, a large number of genetic diseases are caused by defects in the proper processing of primary transcripts, and a lterations in alternative splicing are the basis for multiple human pathologies including cancer, viral infection and inflammatory responses. Despite its prevalence and significance, the molecular mechanisms that regulate alternative splicing are poorly un derstood. The EURASNET consortium employs a wide range of complementary approaches as computational, biochemical, proteomic, genomic, cell and organismal biology to study post-transcriptional gene regulation and its role in disease. The primary purpose of this network will be to develop an integrated approach for the study of alternative splicing that will 1 ) provide durable structures that change the way research in this field is carried out in Europe, 2) establish an innovative and multidisciplinary prog ramme of joint research activities, 3)spread excellence within Europe by establishing a career platform for young investigators, and 4) disseminate knowledge to the wider research and medical communities.

EURODIA: Functional genomics of pancreatic beta cells and tissues involved in control of the endocrine pancreas for prevention and treatment of type 2 diabetes

Failure of pancreatic beta cells to maintain adequate functional capacity leads to increasing blood glucose levels and subsequent type 2 diabetes (T2DM). This disease imposes a huge, and growing, socio-economic burden on European and global societies. However, the pathophysiological mechanisms underlying beta cell dysfunction remain poorly understood, limiting the availability of novel approaches to treat or prevent T2DM. Here, we propose an Integrated Project that will lead to an unprecedented understanding of the factors influencing the maintenance (and loss) of normal beta cell functional capacity. This will be realized through the application of functional genomics technologies in an integrated and systematic approach, which employs studies in cellular and animal models as well as genetic analysis of human monogenie and polygenic T2DM patients. The project will integrate leading European experts in islet diabetes research, human geneticists, bioinformaticians and computational scientists as well as SMEs. Specific aims will be to: - Dissect the molecular pathways and identify key regulatory events - including those contributed by tissues located outside the endocrine pancreas - that control the ability of the beta cell to maintain its secretory function over a lifetime. - Identify key pathophysiological events in the above pathways that become dysfunctional in T2DM and determine the contributions of lipotoxicity, glucotoxicity, oxidative stress and me impact of genetic variations in the induced beta cell dysfunction. - Select genes and proteins that are candidates in the pathogenesis of beta cell dysfunction and assess their physiological role and potential value as novel drug targets for prevention and treatment of T2D. Collectively this program will lead to an unprecedented understanding of beta cell molecular physiology and pathophysiology to pave the way for improved health in Europe.

FELICS: Free European Life-science information and Computational Services

FELICS is a project to organise and make available a complete range of biomolecular information to life-science research throughout Europe. It combines the work of the EBI and SIB to make create and provide the public domain databases on which we all biological research depends. The University of Cologne is included as an important partner in order to put the enzyme information in the BRENDA database into the public domain as part of the interlinked collection of information (currently licensing restrictions prevent this).

The fourth partner, the European Patent Office, is included in order to ensure that the biomolecular information from patents is also included, and that people search the databases for patenting reasons are well supported. The five-year project proposed a detailed collection of joint research activities, which develop and enhance the content of the databases and the connections between them. Almost all of these joint research activities will be carried out in the first three years, although the work at the University of Cologne, which will completely redefine the BRENDA business model, lasts for the entire 5 years of the project. Combined with these joint research activities, support is sought for networking activities (to carry on for the entire five years) necessary to manage the project and ensure that the relevant information resources are seamlessly connected.

Aside from this internal connectivity, connections to the wider community will be made through two Commission-funded Networks of Excellence: BioSapiens and EMBRACE. The partners in the project, particularly the EBI and SIB, will, for the five years of the project, continue to provide a wide range of electronic trans-national access to all the information, though only modest support for these activities is sought in this proposal.

ICGRSIB: Vital-IT Integrated Computational Genomics Resource

The Lausanne-based groups of the Swiss Institute of Bioinformatics are maintaining a suite of databases, software packages and web servers for analysing different types of genome-related data with regard to a variety of biological questions. The databases and web servers of the Lausanne-based computational genomics resources are generated and powered by the high-performance computing platform Vital-IT, whose purpose is to develop, optimise and host software for life science applications, in particular those incorporating large data sets from genomics projects.

The specific aim of this proposal is to facilitate transnational European access through the following instruments:
(i) a visiting developer programme, aimed at making a large collection of datasets, computational resources and expertise available to life scientists who are developing new software;
(ii) access to the HPC infrastructure for large-scale data analysis projects, including courses for new users, focussing on the acquisition of the necessary technical skills to use the infrastructure.

LOCCANDIA: Lab-on-chip based protein profiling for Cancer diagnosis

The human plasma proteome holds the promise of a revolution in disease diagnosis and therapy. One major breakthrough should come from the detection of multiprotein disease markers including isoforms. We propose to integrate a full proteomics analysis chain, from blood sample to the diagnosis information, combining bio-, nano, and information-related technologies. It includes an innovative patented lab-on-chip developed at CEA. The clinical application is early pancreatic cancer diagnosis.

The project is based on a panel of 3 identified proteins, a protein isolation protocol, an optimised chromatographic-electrospray lab-on-chip, an Integrated Clinico-Proteomics Environment including a Proteomic Information Management System, a Clinical Information System, and modules for preprocessing, reconstruction, visualisation, protein identification, data mining and knowledge discovery. The clinical validation is applied to a cohort of 92 patients. Our targeted performance is to get at least the sensitivity of an orthogonal ELISA approach, to operate the analysis chain in less than 12 hours, and to demonstrate the interest of multiprotein marker.

The main research outcomes will be an optimised chromatographic-electrospray lab-on-chip, a software environment supporting the integrated device, a proof-of-concept of their application to protein profiling for cancer diagnosis and an exploitation plan. The roadmap of this 36 months project is defined according to three main milestones: 1) at month 12, a first protein profile using a first version of the lab-on-chip on artificial samples is available, 2) at month 24, all the final versions of the sub-systems are ready for integration and validation, 3) at month 33, the validation on clinical samples is completed.

The consortium partnership involves partners over 5 countries, combining basic and applied research (CEA, FORTH, SIB, WWU), 1 large company (ATOS) and 2 SMEs (BVN, GB), including clinicians and end-users (WWU, BVN).

PRODAC: Proteomics data collection

Based on the work of the Human Proteomics Organisation (HUPO) Proteomics Standards Initiative (PSI) and the experience of the HUPO Brain Proteome Project (HUPO BPP), ProDaC will coordinate the development and implementation of international standards for the representation of high-performance proteomics data. Focus is the standardised data collection, and furthermore the standardised data analysis of protein identification by mass spectrometry.
The basis to develop an international data exchange network of proteomics data repositories will be established. This will include the implementation of prototypes of automated data submission pipelines in the leading European laboratories.
In total, 12 core partners, all well-known European Bioinformatics groups and proteomics laboratories will participate over the whole 30 months runtime of this project. Enhancing this efforts data providers, experienced European proteomics laboratories, will provide appropriate data derived from state-of-the-art proteomics technologies for proof-of-concept and utilise the newly developed software tools in the next stage.
Additionally, international, non-EU laboratories, both academic and commercial, will utilise the tools as associated partners in the third phase of the planned activities, and thus benefit from the standardised data repositories developed, to generally observe and guide the standards development.
The whole process will be accompanied by a number of high-ranking scientific journals, which will be actively involved in the standards development, and will define mandatory supplementary information for submitting articles.

TransBIG: Translating molecular knowledge into early breast cancer management: building on the BIG (Breast International Group) network for improved treatment tailoring

A revolution in breast cancer care is likely as we move from empirical towards molecular oncology. For Europe to lead this revolution, the Breast International Group is planning a "sister" network of excellence -TRANS-BIG- dedicated to multinational translational research linked to prospective clinical trials. At the heart of TRANS-BIG is a large, non drug-oriented, molecular-based adjuvant trial for node negative patients, preceded by a validation/standardisation phase linking the genomic analysis of frozen tumour specimens to patient outcome. These exploratory and clinical studies intend, through DNA microarray gene expression, to identify patient subgroups that could be spared toxic/expensive adjuvant treatment. Such treatment tailoring for the highest incidence cancer in women might significantly decrease its economic burden. This trial aims to prospectively validate the 70-gene poor prognosis signature identified by Dutch researchers as a potentially better discriminator of outcome than traditional clinical/pathological factors. Additionally, traditional pathology analysis and bioinformatics/statistics will be centralised, contributing to standardisation/integration within the network. In light of rapidly evolving technologies, TRANS-BIG will ensure appropriate collection and storage of patient tumour/blood samples, allowing for future analysis such as proteomics and the development of user-friendly and commercially available tools in collaboration with several European SMEs. With a dedicated Ethical-Legal Committee TRANS-BIG will comply with all national/international regulations governing such research. Spreading of excellence will be achieved in partnership with Europa Donna and FECS, the latter coordinating a traineeship programme to provide opportunities for European researchers to acquire skills in breast cancer research/associated technologies.

Activity Type: Research
SIB Contact: Mauro Delorenzi
Date:03.01.2004-28.02.2011
# of Partners: 40
URL: TransBIG

Understanding and conserving Earth's biodiversity hotspots

The Earth’s biodiversity is threatened by human activities yet the sustainable use of biodiversity is fundamental to the future development of humanity. Because financial and human resources for nature conservation are limited, it is appropriate to focus efforts on the richest and most threatened reservoirs of biodiversity. About 25 of such biodiversity hotspots have been recently proposed based on available data on plant and vertebrate species richness, endemism and threat status (www.biodiversityhotspots.org). While there is a wide consensus on the choice and geographical delimitation of hotspots, the dynamics of biodiversity in these hotspots and the ecological impacts of predicted biodiversity loss are poorly understood (e.g. Local endemism within the western Ghats-Sri Lanka biodiversity hotspot. Science 306, 2004). In collaboration with partners in third countries, the European HOTSPOTS consortium will work towards increasing the knowledge and understanding of biodiversity hotspots, including the Mediterranean Basin and some European overseas territories. Applying field, molecular and bioinformatics approaches to flagship plants and animals, HOTSPOTS will train a new generation of multidisciplinary biologists in state-of-the-art methods of evolution, ecology, and conservation.

ELIXIR: European life-science infrastructure for biological information

The objective of the ELIXIR preparatory phase is to produce a memorandum or memoranda of understanding between organisations (government agencies, research councils, funding bodies and scientific organisations) within the member states, with the purpose of constructing a world class and globally positioned European infrastructure for the management and integration of information in the life sciences.

To achieve this, we will address the following tasks and issues:
1- Define the scope of the infrastructure, its role and benefits
2- Define an appropriate governance and legal structure
3- Define a long term funding structure to provide a sustainable infrastructure
4- Define the requirements for the European Data Centre in the next 5-10 years and makes plans to meet these needs
5- Involve all relevant stakeholders, including users, data providers, tools providers to ensure that the infrastructure meets their needs
6- Explore integration and interoperability between core and specialised data resources and the development of standards in newly emerging fields
7- Define the critical interdisciplinary links that need to be forged between the biological and related scientific disciplines, including medicine, agriculture and the environment
8- Define the needs of related European industries

IFNACTION: A system view on the differential activities of human type I interferons

Type I interferons (IFNs) form a restricted network of highly related immune cytokines that elicit differential biological responses through a single cell surface receptor comprised of the subunits IFNAR1 and IFNAR2. We have shown that differential signal activation correlates with differential interaction and conformational dynamics of the receptor induced by binding of different member of the IFN family.

The goal of this project is to employ a systems biology approach to identify the molecular and cellular mechanisms responsible for translating receptor dynamics into differential cellular responses by combining biochemical, biophysical and genetic analysis of the signalling outputs. We will collect quantitative data describing type I interferon signalling from ligand recognition until phenomenological cellular responses in a number of well defined cell lines. Based on detailed structure functions studies, we will generate a set of IFN mutants with highly differential cellular responses. Based on this sub-family of ligands, we will explore the molecular and cellular dynamics of the signalling complex on the plasma membrane, as well as the receptor trafficking upon activation.

Moreover, we will analyze the protein-protein interaction network involved in signal transduction and obtain a spatio-temporal picture of key signalling pathways. These studies will be flanked by extensive analyses of gene transcription levels and correlated with cellular responses. Using these data sets, input and output signals will be correlated on different levels by various mathematical approaches to understand how the processing of differential input signals is translated within the cell to produce different responses to binding the same surface receptors. In order to test the validity of these models, experimental and theoretical studies will be tightly coupled, for example, in designing network perturbations.

Modular Networks: Topological and functional modularity in biological regulatory networks

Most functions and structures in living organisms seem to depend on subsets of elements organized as modules. In a modular system a certain process, performed by a module, does not depend heavily on the elements outside the module, and hence it is semi-autonomous. Modularity promotes evolvability, an organism s capacity to generate heritable phenotypic variation, because it permits adjustment of a module without perturbing other functions and allows the combination of previously evolved functions. Understanding modularity is critical for the study of evolution and development of phenotypic traits. As several biological regulatory systems can be represented as directed networks, it would be useful to study these representations of biological systems to search for network traits that could underlie modules, and to test under which evolutionary scenarios these traits may appear.

GEN2PHEN: Genotype to Phenotype Databases- A Holistic Solution

The GEN2PHEN project aims to unify human and model organism genetic variation databases towards increasingly holistic views into Genotype-To-Phenotype (G2P) data, and to link this system into other biomedical knowledge sources via genome browser functionality. The project will establish the technological building-blocks needed for the evolution of today s diverse G2P databases into a future seamless G2P biomedical knowledge environment. The project will then utilise these elements to construct an operational first-version of that knowledge environment, by the projects end. This will consist of a European-centred but globally-networked hierarchy of bioinformatics GRID-linked databases, tools and standards, all tied into the Ensembl genome browser.
The project has the following specific objectives:
1) To analyse the G2P field and thus determine emerging needs and practices;
2) To develop key standards for the G2P database field;
3) To create generic database components, services, and integration infrastructures for the G2P database domain;
4) To create search modalities and data presentation solutions for G2P knowledge;
5) To facilitate the process of populating G2P databases;
6) To build a major G2P internet portal;
7) To deploy GEN2PHEN solutions to the community

 UNICELLSYS

The overall objective of UNICELLSYS is a quantitative understanding of fundamental characteristics of eukaryotic unicellular organism biology: how cell growth and proliferation are controlled and coordinated by extracellular and intrinsic stimuli. Achieving an understanding of the principles with which bio-molecular systems function requires integrating quantitative experimentation with simulations of dynamic mathematical models. UNICELLSYS bring together a consortium of leading European experimental and computational systems biologists that will study cell growth and proliferation at the levels of cell population, single cell, cellular network, large-scale dynamic systems and functional module.
Building computational reconstructions and dynamic models will involve different precise quantitative measurements as well as complementary approaches of mathematical modelling. A major challenge will be the generation of comprehensive dynamic models of the entire control system of cell growth and proliferation, which will require integration of smaller sub-models and reduction of complexity. Implementation of the models will allow observing responses to altered growth conditions zooming in seamlessly from populations consisting of cells of different replicative age and cell cycle stage via genome-wide molecular networks.

A comprenhensive spatio-temporal map of elongating transcripts to dissect circadian expression systems

Recent studies in yeast, flies and mammals have uncovered broad programs of rhythmic gene expression, reflecting the importance of molecular oscillators and their output circuits in determining cell physiology and behavior. We will quantify how the circadian clock acts as cellular metronome from a comprehensive survey of active transcription units in mouse. Specifically we will study the spatio-temporal patterns of elongating polymerase II to decipher the role of rhythmic/circadian transcription initiation in controlling cellular functions and transcript homeostasis. We will combine molecular biology experiments with bioinformatics/modeling and use genome-wide location analyses based on chromatin immuno-precipitation (ChIP). The experimental part will be performed in collaboration with Prof. Schibler’s Laboratory and our group will develop the bioinformatics part. Circadian time series data will be generated from mouse liver and provide (1) a time dependent repertoire of actively transcribed units with high spatial resolution, including possible new functional non-coding RNAs; (2) a detailed view of the kinetics of transcript elongation and how it depends on the genetic sequence or pre-mRNA splicing; (3) a connection between transcription and cytoplasmic mRNA levels.
Bioinformatics methods will be specifically developed to optimally exploit tiling arrays, the latter being the basis of the high resolution location analysis. Analyses will include (1) the detection of spatially extended and rhythmic transcription units with both circadian and ultradian periods; (2) the development of models for populations of circadian phase oscillators. These will serve to interpret follow up studies based on luminescence recordings. Our current phase models account for recent experimental findings: the observed drifts in individual frequencies and the possibility of inter-cellular coupling between oscillators. We expect to identify novel genomic loci which are rhythmically transcribed. Such loci could include previously unnoticed protein coding genes with a role in the circadian molecular clock, but possibly also non-coding regulatory RNAs such as miRNA. Candidate RNAs will be chosen for further analysis. Circadian fluorescence rhythms and cell division cycles will then be monitored by fluorescence time lapse microscopy and bioluminescence recordings. The latter will be analyzed using phase models.
In summary this project will further investigate the connection between basic transcription and the circadian molecular oscillator. We expect to uncover broader rhythmic transcription in mouse than typically found in cytoplasmic mRNAs (5-10% of all transcripts). Moreover the experiment might highlight ultradian rhythms revealing the existence of alternate oscillators in mammalian cells. As ChIP experiments combined with tiling arrays are becoming a major tool to study transcription regulatory networks, we further expect that our methods will be beneficial to a wide community.

CEXDA- Combining experimental data analysis and computational predictions to identify modulators of miRNA activity

MicroRNAs (miRNAs) are short regulatory RNAs that are encoded in the genome. They act on messenger RNAs (mRNAs), which they recognize based on sequence complementarity, reducing the rate of protein translation and inducing to some extent the degradation of these target mRNAs. Hundreds of miRNAs have been found in the human genome, and computational models estimate that a miRNA targets on average hundreds of mRNAs. It is thus clear that miRNAs are part of extensive regulatory networks. An interesting question that emerges now is what factors can modulate the effects that miRNAs have on their targets.
Various analyses of miRNA targets suggested that many mRNAs contain more than one binding site for a single miRNA or can be recognized by several different miRNAs that are simultaneously expressed. This observation prompted the speculation that cross-talk between multiple complexes containing miRNAs, assembled on the same mRNA molecule, may increase the robustness of the translation regulatory response. Several studies have also reported that the activity of miRNAs can be modulated by RNA-binding proteins: HuR has been shown to relieve the miR-122-dependent inhibition of the cationic amino acid transporter 1 message under stress, while the dead-end protein (Dnd1) was shown to inhibit the access of miRNAs to their target sites in the primordial cells of zebrafish. In this study we will combine analyses of experimental data with computational modeling in order to determine such modulatory interactions and to understand the nature of the selection pressures that act on mRNAs in evolution.

Comparative modular analysis of gene expression in vertebrate development

Understanding how the evolution of animal form is encoded in changes in the genome is a major challenge of modern biology. One of the issues confronting this intersection of genomics and evolutionary developmental biology is the complexity of the data and of the phenomena involved. Whenever we face such a large number of individual elements that have heterogeneous properties, grouping elements with similar properties together can help to obtain a better understanding of the entire ensemble. For example, individual genes can be categorized according to their properties to obtain a global picture of their organization in the genome. An advantage of studying modules is the reduction in variance relatively to individual measures, which is especially useful for noisy functional genomics data (e.g. gene expression). In terms of evolutionary biology, modules may be found at three levels: the "characters" of taxonomy, discrete parts which evolve semi-independently (e.g. the tetrapode forelimb); gene regulatory networks, which (to some extent) underlie these morphological modules; and sets of genes which act in a correlated manner, which reflect (again to some extent) the regulatory modules. Thus, we expect that the modularity at the morphological level is related to the modular organization of gene expression. In this project, we propose to conduct modular analyses of gene expression in the embryonic development of different animal species. The comparison of conserved or specific modules of gene expression will then provide us with information on the manner in which changes in genome regulation underlie the evolution of animal morphology. Importantly, this is a collaboration between the Robinson-Rechavi lab, specialized in the bioinformatics of genome and development evolution, and the Bergmann lab, specialized in the computational analysis of large and complex biological datasets. We expect that our collaboration will shed light on the fundamental modular nature of the regulation of development in animals, and its evolution. Our study will also result in methodological advances that will be useful to other applications, from biomedicine to the comparative study of plants.

Computational comparative analysis of insect genomes

Most of the important macromolecules in living organisms are encoded by their genomes, which are inherited from parents to progeny. In recent, years we have learned how to read this molecular genomic information, and by using this technology we have already decoded genomes of human and several insects into a computer readable format. However, we are still learning how to understand as proteins and other functional molecules are encoded there. The aim of this project is to advance our computer-based analysis to extract biological knowledge from the accumulating data. Particularly, we are focusing on the analysis of insect genomes, many of which are currently being sequenced and some are of high agricultural or medical importance as they transmit human diseases responsible for the death of millions of people every year. Specifically, we use multiple comparisons among these genomes to identify common genes, and to identify the differences that define the species-specific biology.

Computational identification of microRNA genes and their targets

Genomics open new avenues to study biological processes at the molecular level. Yet, many of these approaches rely on our currently incomplete knowledge of the underlying functional elements. An example is the recently discovered class of small non-protein coding RNA molecules, termed microRNAs, that have been recognized as major regulators of many processes in health and disease. The objective of this proposal is to develop new and enhance current computational approaches to analyze genome wide data to recognize the microRNA genes and elucidate their mRNA targets. Computational, and therefore comprehensive, identification of the microRNA gene repertoire and their targets will facilitate interpretation of functional genomics data with respect to the observed phenotypes and it is likely to have a profound contribution to our understanding of molecular biology.

Computational inference of small RNA-dependent regulatory network

Small RNA molecules have emerged as important regulators of gene expression. The most studied among them are the microRNAs (miRNAs), many of which are conserved over large evolutionary distances, such as between worm and human. Experimental data indicates that miRNAs are involved in many developmental and physiological processes, such as cell lineage decisions and proliferation, apoptosis, morphogenesis, fat metabolism, and hormone secretion. They are incorporated into a ribonucleoprotein complex called the RISC complex, which induces the translation inhibition or degradation of target mRNAs that are partially complementary to the miRNA sequence in the complex.

Whereas over 400 miRNA genes have now been identified in human, experimental identification of their mRNA targets has lagged markedly. Based on a handful of experimentally verified targets and a few landmark mutational studies, a number of researchers have proposed computational methods for identifying the mRNA targets of miRNAs. The main conclusion that emerged out of these studies is that functional miRNA binding sites can be most reliably identified in mRNAs by searching for matches to the 5' end of the miRNA (``seed'' sequence) that are conserved across different species. However, the computational approaches have raised many more questions that remain to be answered. It is, for instance, unclear what fraction of the large number of mRNAs that contain one or more matches to a miRNA ``seed'' represents functional targets, how much the regulatory effect varies across target sites, how the regulatory effects of small RNAs depend on the concentrations of both the small RNAs and their targets, and how much the target sets of related miRNAs overlap (within and across species).

To address these questions we propose to develop new computational models of miRNA-target interactions and to use these to reconstruct miRNA-dependent regulatory networks. The basic premise of our computational approach is that factors beyond the ``seed'' of the miRNA contribute to the specificity of miRNA action, and that we can improve the quality of target prediction by incorporating these additional constraints as well as through more refined models of miRNA target site evolution. These tools will then allow us to then study the structure of small RNA-dependent regulatory networks, that include positive and negative feedback loops about which virtually nothing is currently known.

DENGUE- Atomistic Modelling and Experinetal Validation of Enzyme-Inhibitor Interactions of Fengue Fever Virus Methyl transferase : Towards new approaches to target neglected tropical diseases

Dengue fever is a viral infectious disease that is prevalent in tropical regions. It is transmitted by mosquitoes and annually affects 50 to 100 million people worldwide. No vaccinations or specific drug treatments are available. Several of the virus’ proteins are essential for its pathogenicity and are required by the virus to reproduce in host cells. In cases where three-dimensional structural models of the binding site of the proteins are known, computer simulations (i.e. virtual screening or molecular docking) can be used to simulate possible interaction between these proteins and small inhibitor molecules with the potential to become drug candidates. However, current algorithms used in virtual screening must make a number of approximations in order to be able to screen a large number of compounds within reasonable time. In contrast, atomistic simulations based on the physicochemical properties of molecules using Newton’s laws of motion to determine the strength of binding between ligand and receptor molecules can provide more accurate, but computationally more demanding, predictions of the affinity with which a molecule binds to specific site in a protein. In this study, we focus our computational work on simulating the binding of small-molecule inhibitors to the viral enzyme NS5 methyltransferase. In a first step, a library of commercially available chemical compounds is searched by virtual screening for molecules likely to bind to the viral enzyme. Chemical compounds emerging from this study as possible inhibitors of dengue methyltransferase will be tested in biochemical and biological assays for their ability to inhibit viral replication in cultured cells. For this point, we are closely collaborating with the Novartis Institute for Tropical Diseases in Singapore. The results of the experimental measurements will in return increase our understanding of the physicochemical interactions governing methyltransferase inhibitor interactions and allow us to improve the accuracy of our molecular modeling simulations by calibrating interaction parameters with experimental binding affinities. The project aims at method development in computational modeling of protein-ligand interactions, and their application to dengue virus methyltransferase inhibitors. We moreover hope that this research will contribute to the discovery of new lead compounds against neglected tropical diseases in a public-private partnership, leading to the development of drugs that are offered at cost in the affected countries.

Developing a phyloinformatic framework for analysing multigene data matrices: grass database, missing data and mixed models

This project proposes to develop a phyloinformatic framework aiming at improving several aspects of the analysis of multigene data matrices. The project has four distinct goals. (i) The implementation of a database storing aligned DNA sequences for the grass family. The database will be regularly updated by querying existing sequence databases. Automated tools will sift the sequences in order to obtain the maximum number of DNA regions and species usable for phylogenetic studies. Beside alignments, the database will store phylogenetic trees for each DNA regions considered, as well as a tree for the combined DNA regions, which will represent the largest existing grass phylogenetic tree. An instantaneous view of grass evolutionary history will be available online and will help future sampling strategies aiming at builing the grass Tree of Life. (ii) The development and assessment of algorithms allowing an efficient tree reconstruction of multigene matrices containing large amount of missing data. Such matrices are becoming more and more common in phylogenetic studies and the inclusion of large amount of missing data can potentially impact the accuracy, resolution and support of the estimated phylogenetic tree. Removing taxa with missing data is often not efficient in macro-evolutionary studies and obtaining the most resolved tree is important if we want statistical power for analyses using trees as starting points. (iii) The characterisation of model parameters important in the analysis of multigene data matrices. When analysing multigene matrices, a single model of DNA evolution is either applied to all partitions, or different models are applied to each partition separately. In the first case, oversimplication can result in inconsistent inference, while the second case could lead to overparameterisation. We will use computer simulations to investigate the effect of using different model parameters in order to get accurate topologies and branch lengths estimations. This part of the project will propose guidelines as to how best analyse multigene data matrices. (iv) The development of a tool selecting appropriate models of DNA evolution for multigene data matrices. This tool will determine which models are shared among partitions and which model parameters should be linked or unlinked across partitions of the data set. It will be built upon a performance-based approach to model selection. This tool will help selecting appropriate models of DNA evolution, and therefore should be useful in reducing inconsistent inference.

Evolutionary epidemiology of mobile DNA

A DNA molecule is called “mobile” if it can change its position within a genome. Mobile DNA exists in many genomes. Some of the simplest kinds of mobile DNA are bacterial insertion sequences (ISs). These are short mobile DNA molecules that occur in bacterial genomes, and that cause mutations of their host genome when they change position. After changing position, a copy of an insertion sequence is often left at its original location. Despite thirty years of research, the question why mobile DNA persists in genomes still has no conclusive answer. On one hand, mobile DNA may be a very effective parasite of its host genome, making copies of itself at the expense of the host, where it causes deleterious mutations. On the other hand, mobile DNA can also have beneficial effects, such as occasionally beneficial mutations. Bacterial ISs are also implicated in an important public health threat: the spreading of drug resistance genes among pathogenic bacteria. We cannot fully understand bacterial genome evolution, and its consequences, unless we understand how such mobile DNA is maintained, and how it spreads among bacterial genomes. The availability of hundreds of completely sequenced bacterial genomes provides a unique opportunity to study the persistence of mobile DNA. The proposed work will study members of multiple IS families in several hundred completely sequenced bacterial genomes. To this end, we will first develop a software tool to identify ISs in hundreds of different genomes, and to study their sequence similarity. With this tool, we will then analyze IS sequence evolution, which will allow us to characterize how ISs spread within and among genomes. In a third stage of our work, we will use epidemiological models to study IS spreading among genomes. In a final stage, we will study the evolution of select transposable elements in eukaryotic genomes. This will help us understand whether the principles of prokaryotic mobile DNA evolution also apply to higher organisms.

Evolutionary niche dynamics of invasive species

The main goal of this project is to understand the influence of evolutionary history, especially the history of shifts of the climatic niche, on the invasiveness of exotic species. Studying the evolutionary history of niche dynamics is not specific to invasive species. Invasive species are a convenient set of model systems (i.e. genera) in which the literature suggests that it is likely that evolution of the climatic niche influences the presence of a detectable ecological quality:invasiveness. Without the criterion of presence of invasives, we could have selected genera at random or used other criteria that would not likely lead the research to have similarly broad interest. In this research we: (a) use a bioinformatics approach to obtain existing data on species distribution and molecular variation within genera that contain invasive species; (b) modify and use existing software to test the degree to which alternative evolutionary models suffice in describing phylogenetic patterns of niche evolution within these genera; (c) test for historical correlates of niche shift and invasiveness by drawing on information from phylogenetic reconstructions, plant functional traits, and climatic niche characteristics of species.

From modules to models: towards a better understanding of disease through advanced analysis of large-scale data

The possibilities to measure the properties and the behavior of biological systems advance at a rapid pace. Sophisticated experimental techniques allow for the monitoring of protein and RNA levels at great resolution in time and space. Whole-genome sequencing provides not only an inventory of genes, including their regulatory regions, but has paved the way for high-throughput technologies that take snapshots of the regulatory programs governing the expression of these genes. In particular, DNA microarrays have firmly established themselves as a standard tool in biological and biomedical research, and are beginning to enter the clinical arena. The promises of modern data acquisition for a better understanding of biological systems can only be matched by advanced data analysis and modeling. Large-scale genomic data require innovative tools for data normalization, visualization and organization. Combining related entities in modular units reduces the complexity of the data and is an important step towards understanding the intricate behavior of thousands of genes under a variety of internal and external changes. The comparison and integration of large datasets from different sources present major conceptual and computational challenges. New approaches are needed to generate quantitative models from the massive information generated by array-based technologies. Ultimately such mathematical models should be not only descriptive, but also predictive and provide insight into the design features of the biological systems. The research project funded by the Swiss National Science Foundation focuses on the modular analysis of large-scale mammalian expression data. In other words we would like to dissect large tables of data reporting the activity of thousands of genes under hundreds different of conditions (e.g. different tumor samples) into manageable blocks (sub-tables) featuring only those genes that exhibit a similar activity pattern over a subset of conditions. The elementary building blocks are then used for further analysis, in order to learn which genes act together and how the regulatory program is structured in general. Our project contains two major parts: In the first sub-project our goal is to adapt and improve existing tools into an efficient and general platform for the analysis of massive mammalian microarray data, to test and use this platform using public data, and to make it available for other researchers. The second goal is to establish and apply new methodologies for the integrative analysis of multiple datasets, such as different types of clinical data covering expression (what genes are active), genotypic (what variants of genes are present) and phenotypic (how does the cell or organism behave) information. Our project attacks pressing issues of contemporary life sciences and has the potential to generate testable hypothesis, provide practical analysis tools, as well as new concepts and ideas for the biological and biomedical communities. We pursue our ambitious goals in interdisciplinary collaboration with experimental colleagues from my Department, the Universities of Lausanne and Geneva, as well as from abroad.

Genotypic basis of microbial diversity in the environment

Fast alle Bereiche unserer Umwelt enthalten eine unübersehbare Vielfalt an Bakterien. Man findet sie im Erdboden, im Wasser, auf Pflanzen und sogar in der Luft. Viele dieser Umweltbakterien sind bis heute vollkommen unbekannt, weil man sie im Labor oft nicht züchten kann und sie sich im Mikroskop kaum voneinander unterscheiden lassen. Diesen "weissen Fleck" auf der Karte der Arten versucht das vorliegende Forschungsprojekt zu schliessen. Grundlage dafür ist die neue Technologie der Hochdurchsatz-Gensequenzierung. Mit Hilfe dieser Technologie versuchen Forscher heute, die gesamte Erbsubstanz einer Umweltprobe in einem Durchgang zu entschluesseln. Beispielsweise werden aus einigen wenigen Gramm Erdboden alle dort vorhandenen Einzeller mechanisch zerstört, und die freiwerdende Erbsubstanz in unzähligen kleinen Stücken sequenziert. Diese Art der Untersuchung stellt enorme Herausforderungen an die 'Bioinformatik', d.h. an die Computer-gestützte Biologie. Die einzelnen Genstücke müssen wie bei einem grossen Puzzle wieder zusammengesetzt werden, um einen Einblick in die Genome der Lebewesen zu erhalten. Wo dies aufgrund der Komplexität nicht möglich ist, muss zumindest aus den vorhandenen Bruchstücken möglichst viel Information gewonnen werden. Wir stellen uns Fragen üeber die Zusammensetzung und Funktion der Artengemeinschaft in verschiedenen Umweltproben: welche Organismen treffen wir wo an? Sind auch Bakterien perfekt an ihre Umgebung angepasst, oder werden sie häufig von Wind und Strömung an wechselnde Orte getragen und müssen daher 'Generalisten' sein? Tauschen Bakterien häufig untereinander Gene aus? Wie reagieren die Gemeinschaften auf veränderte Umweltbedingunge? Diese und ähnliche Fragen wollen wir anhand der bruchstückhaften Informationen aus den Gensequenzen beantworten.

High performance hierarchical storage system for computation and systems biology

abstract not yet available

Local adaptation and complex demography in a spatially explicit landscape and their effect on molecular diversity: Application to humans and voles

In this project we propose to develop new methods to detect genome regions under selection from the observed pattern of molecular diversity within and among samples. For this, we shall explicitly take into account, and potentially simultaneously estimate, the past demography of the sampled populations. This is necessary because genetic diversity is deeply affected by past historical events such as bottlenecks, range expansions or migration, which thus need to be accounted for when predicting the expected range of neutral variation. We shall develop a realistic and spatially explicit simulation program, allowing us to generate neutral and selected genetic diversity at an arbitrary number of loci in populations evolving in a potentially heterogeneous landscape. These simulations will be integrated into a Bayesian estimation framework, which will enable us to estimate demographic parameters as well as selection coefficients, and to examine the relative probability of various evolutionary models. These developments will be applied to human and vole populations. In humans, we shall first improve our knowledge of past evolutionary scenarios, by considering the effect of long-range dispersal and fast coastal migrations in the Upper Paleolithic. We shall then predict the effect of complex evolutionary scenarios, adaptive selection and density-dependent selection on molecular diversity, to provide us with better tools to analyze genetic data resulting from available genome scans and to recognize genome regions under selection. In the common vole (Microtus arvalis), we shall focus on their recent colonization of the Orkney Islands having lead to morphological adaptations. We shall develop a large array of AFLP markers enabling us to scan the genome of Orkney voles for loci showing signs of recent adaptations. AFLP scans will also be used to detect signals of balancing selection among very distinct populations of continental Europe, since this type of selection is very difficult to evidence in species with little differentiation like humans. The methodological developments proposed here should lead to a more accurate detection of genomic regions under selection, which should have very important implications for both evolutionary and medical genetics

MBDS: an enabling storage mechanism for life science research activities

Providing long term storage and computational resources to the life scientists in Switzerland. The aim of this project is to develop computational solutions to store, manage and analyze biological data ranging from different technologies such as next generation sequencing, imaging, proteomics.

Modeling the function and evolution of transcriptional regulatory networks

Origin and Function of Codon-Bias

This project, a collaboration of computer scientists and biologists, focuses on sources and mechanisms of codon bias. What is codon bias? The central dogma of molecular biology states that DNA is transcribed to mRNA which is translated to proteins at the ribosomes. tRNA brings the appropriate amino acids to the ribosome during the production of the protein. There are sixty-four codons (words in DNA) coding for twenty amino acids. This means that some amino acids are translated by more than one codon. In fact, amino acids can be translated by up to six different codons. The codons are not used at random, however, and sometimes one may not be used at all. This nonrandom usage of codons is codon bias.

This project has investigated the sources of codon bias and has found that if the tRNAs can be reused at the ribosome during the production of the protein, this offers an advantage to the organism and thus tRNA reuse has been selected for by Darwinian evolution over time. tRNA reusage occurs more often when the encoded protein has to be translated quickly because in these cases, reusing available tRNAS speeds translation. This phenomenon has been found theoretically by looking at the frequency of tRNA reusage found in the coding sequences of many genomes and measured experimentally in Bakers yeast, S. cerevisiae. A novel index, the tRNA pairing index, has also been developed to quantify tRNA reusage.

Predictive physical models and large-scale computer simulations to study the role of electrostatics and couplings in the nonequilibrium dynamics of biological membranes in relation to lipid rafts and endocytosis

Membranes in live cells are complex, organized mixtures of different lipids and proteins. They contain several functional sub-domains, called "rafts”.The existence of lipid rafts is, however, still not unanimously accepted. Nevertheless, rafts are suspected to have many important biological functions, e.g., in signal transduction, protein transport and sorting, or virus uptake. Membrane sub-domains are currently intensively studied using both theoretical and experimental methods. To the best of our knowledge, however, no simulation studies have been carried out so far and the physical mechanisms and dynamics of raft formation and evolution are largely unknown. We propose a novel physical model that will, for the first time, fully account for the feed-back loops, electrostatic, and hydrodynamic effects in biomembranes. This holistic model will allow us to characterize the dynamics of both the lipids and the proteins simultaneously and in a fully coupled manner. Using the new simulation model, we will study the membrane sub-domain (raft) structure and the dynamics of formation of these domains. The knowledge gained in the present project will be of importance in understanding cell membrane systems and in developing novel anti-viral drugs that operate by altering the biophysical properties of membrane rafts.

ProDoc: Swiss PhD Training Network in Bioinformatics

The Swiss Institute of Bioinformatics has initiated in 2006 a Swiss PhD Training Network in Bioinformatics, open to graduate students from all Swiss Universities. The objectives of the network are twofold: to offer graduate students in bioinformatics a set of cutting-edge courses that would provide both the theoretical and the practical knowledge necessary for a successful PhD research project in bioinformatics, and to foster the emergence of a network of PhD students, promote the exchange of ideas, as well as the mobility of the students between participating institutions. These objectives are pursued through the following complementary activities:
• International summer schools in bioinformatics to provide the students with state of the art knowledge on cutting edge topics
• Graduate level courses in bioinformatics, organized in compact blocks on the campus of one of the SIB partner Schools of Higher Education
• Workshops combining presentations from very few invited speakers and interactive sessions in which the students can both present their work as well as discuss articles and topics with their peers
• Lab rotations in which the PhD students can expand their knowledge by participating in the research activities of multiple SIB groups

SCORE: Rational optimization of peptide vaccines for immunotherapy of cancer

Recognition of antigenic peptides (p) presented in the context of the Major Histocompatibility Complex (MHC) by the T cell receptor (TCR) of cytotoxic T lymphocytes (CTL) is the key event implicated in the immune defense against tumors. Two main therapeutic approaches are currently being investigated to confer an immune protection in patients, peptide based immunotherapy and adoptive transfer. We have developed in silico approaches to help the rational design of both, peptide vaccines and optimized TCR sequences. For the former, we have designed a general approach to predict the structure of any peptide bound to any MHC molecule using molecular dynamics at various temperatures. In the case of HLA A2 restricted peptides, the 14 predicted structures were not distinguishable from the X-ray (all atom RMSD lower than 1.5 A). This approach is now used to predict the structures of tumor peptides modified to increase their affinity and lower their off rate. For the latter, a method has been developed to predict the binding free energy associated with each residue of the CDR loops of the TCR. This approach has been shown to be well correlated to results of in vitro alanin scanning experiments. It is now used to guide rational TCR optimization to increase pMHC binding. The interesting candidates are tested in vitro using Biacore techniques and will be transfected to patient’s lymphocytes for adoptive transfer therapies.

swissPIT – Extracting knowledge from mass spectrometry data

Proteomics is is the study of the proteome, the set of proteins produced by a species. Proteomics studies how proteins are expressed and modified, how there are differentially expressed according to varying conditions such as diseases or different drug concentrations.
While each species has only one genome (the set of genes), they have many thousands proteomes, as the proteome varies depending on cellular localisation, time, external influences, etc. Recent technological developments in protein separation techniques and mass spectrometry have made proteomics into one of the key fields of research in life sciences during the last decade. Typically, proteomics involves the separation of proteins contained in biological samples, followed by their analysis by mass spectrometry (MS). The resulting data are fed to specific programs that search their corresponding sequences in protein sequence databases for identification and quantification. Proteomics experiments typically produce up to thousands of MS spectra per day.
While several existing identification programs are routinely used, this part of the process still represents a major bottleneck in proteomics research. Several aspects of protein identification are handled manually and results must be visually validated. More importantly, many (and often most) of the MS data d onot lead to any results at all for multiple causes.
The aims of the swissPIT project are threefold. First it intends to automate the identification process so as to reduce data analysis time, which usually takes longer than data production. Second, it seeks to increase the fraction of spectra that may be identified and to improve the quality and confidence in the identification results by combining several identification algorithms and programs in an automated platform. Third, the platform will be used to carry out a detailed study on the effects and relative importance of the various parameters and algorithms on the identification of proteins, and thus propose optimized identification strategies.
The swissPIT platform will comprise several identification programs developed either in-house or by other international laboratories. Spectra will be successively submitted to several programs The various analysis steps will be uncoupled so as to optimally combine the various strategies and the identification results will be merged using a meta-score. The platform with its many components will be used both for live research by the scientific community and as a benchmarking tool for identification strategies. swissPIT will be linked to the SwissBioGrid intitiative (a GRID for the life sciences) as to benefit from its distributed computing architecture and speed up the analysis time.
The project is expected to solve one the major bottlenecks of current proteomics research by allowing the identification of a larger number of proteins with more confidence and give the community an enhanced automated proteome informatics platform that shall be accessible through the SwissBioGrid.

The evolutionary significance of whole genome duplication in teleost fishes

Many groups of species have had their genome shaped by a complete duplication in their evolutionary past, tens of millions of years ago. We feel, intuitively, that an event as dramatic as genome duplication should impact the organism in a visible manner, but details have been elusive. Do the copies share an ancestral function, or does one keep it while the other evolves a new function? Are these functional changes mostly at the level of the protein structure, or do they affect more where and when the gene is expressed? Are the consequences of genome duplication visible in the evolution of new morphological adaptations, or of more species? To answer such questions, we focus on teleost fishes, which constitute about half of all vertebrate species. The genome duplication which occurred at the origin of this group, about 200 to 300 million years ago, is conclusively demonstrated, and abundant data is available for fishes. In this project, we will use bioinformatics to study on the one hand the evolution of all proteins from fish genomes, after whole genome duplication. And on the other hand, to investigate more specifically the evolution of genes involved in embryonic development. This should help us link genome duplication to features of animal diversity. The relation between these two fields has remained a fascinating but open question for two decades.

ProSite

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them.
PROSITE is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids.

UniMed

The Unimed project addresses the important problem of increasing interoperability between data resources from the medical informatics and the bioinformatics domains. We propose to link UniProtKB entries to the two most widely used disease terminologies:

1. ICD-10, which is the official disease classification provided by the WHO,
2. MeSH, which is the controlled vocabulary thesaurus used for biomedical and health-related document indexing.

The UniProt Knowledgebase (http://www.uniprot.org) is the most comprehensive protein warehouse which makes extensive cross-references to other biological resources. Although it is not a medical-oriented database, about 2’500 human proteins contain manually curated information related to their involvement in pathologies. While clearly of value, such information is not easily accessible for the clinical management of patients. This is mainly due to the absence of controlled vocabularies in the disease annotation process. In the medical/clinical domain, there have already been numerous and successful efforts to implement controlled vocabularies for pathologies. These common terminologies can act as a metadata layer to provide the missing links between protein information and disease information, and help bridge the gap between clinical medicine and molecular biology for the benefit of both research and public health.
Within the framework of this study, it is also our aim to increase the coverage of medical-oriented information in the UniProtKB.

The terminology mapping task will be addressed by taking advantage of :

1. the manually curated links of UniProtKB entries which are made to the OMIM database, a comprehensive knowledgebase of human genes and genetic diseases;
2. the many biomedical article citations provided in UniProt entries.

The main technical development we plan to achieve in this project will consist in the adaptation and refinement of terms matching algorithms for the specific task of biomedical terminology mapping. We will also use the conceptual structure of UMLS, a major repository of biomedical standard terminologies, to assess our results. A final manual evaluation will contribute to ensure the quality of the mappings. The results will be made public, thus allowing the clinicians and researchers to navigate efficiently from epidemiological to genomic data, and vice versa.

Protein Model Portal

The protein Model Portal is being developed as a module of the Protein Structure Initiative Knowledgebase (PSI KB). The goal of the Models Module is to develop a portal that gives access to the various models that can be leveraged from PSI targets and other experimental protein structures. The Protein Structure Initiative has been successful in determining the structures of many unique proteins in a high throughput manner. Still, the number of known protein sequences is much larger than the number of experimentally solved protein structures. Homology (or comparative) modeling methods make use of experimental protein structures to build models for evolutionary related proteins. Structural genomics and homology modeling thereby complement each other in the exploration of the protein structure space.

Mechanisms of cyclic di-GMP signalling

Living cells employ small diffusible molecules, so-called second messengers, to signal environmental cues from sensory proteins to cellular receptors. Only recently, it has become apparent that bacteria utilize the cyclic dinucleotide c-di-GMP as a ubiquitous second messenger to switch between rapidly growing single cells and a quiescent life style, called biofilm. In pathogenic bacteria, this switch is often accompanied by the transition from an acute to a chronic phase of infection. This makes c-di-GMP signal transduction an attractive target for novel antibiotics that interfere with bacterial persistence. The cellular concentration of c-di-GMP is the result of the opposing activities of diguanylate cyclases that synthesize c-di-GMP from two GTP molecules, and phosphodiesterases that degrade the compound. These two key enzymatic activities regulate c-di-GMP and thus the state of the various c-di-GMP receptors and their associated activities within the cell.

To uncover the molecular mechanisms of the c-di-GMP signaling network, this Sinergia project aims at combining in vivo studies with pathogenic and non-pathogenic bacterial model systems with the analysis of the isolated and purified proteins of the network. This involves their enzymatic and biophysical characterization, 3D-structure determination by X-ray crystallography and the study of their dynamic properties by fluorescence energy transfer measurements. Furthermore, the vast amount of bioinformatic data available will be exploited for structure prediction and the identification of yet unrecognized members of the network. The results will further our general knowledge about c-di-GMP mediated cell signaling and behavior and contribute important information towards the successful control of chronic infections by animal and human pathogens. This collaboration with Urs Jenal, Tilman Schirmer and Dagmar Klostermeier (Biozentrum Uni Basel) is funded by the SNSF Swiss National Science Foundation.
 

Multi-scale simulations and modelling of membrane proteins and their role in cell signalling

Membrane proteins are dynamical structures that change conformation in response to the environment, allowing for their contribution to different signalling processes. Ion channels are notably involved in the regulation of action potentials in excitable tissues, such as the heart and brain. Despite extensive electrophysiological and structural data, the atomistic mechanisms regulating the conductance of these protein channels remain unknown. By combining explicit molecular dynamics simulations and statistical physics principles, the group aims at elucidating the molecular basis of these mechanisms in the potassium channels and various transporters. A multi-scale stochastic simulation framework is also developed to reproduce measurable data and to ultimately bridge atomistic simulations of membrane systems with macroscopic simulations of excitable tissues.
 

LipidX

Considering their substantial presence in everything from cell and organelle membranes to complex structural aggregates (micro/nano domains), lipids have enjoyed little attention in the field of biological research. These micro/nano domains assemble into biologically functional networks collectively referred to as biomembranes. Although the “-omics’” era has provided large amounts of data results for those interested in proteins, transcription, genes, and metabolism, lipidomics lags behind. The richness of the lipid repertoire exceeds 100’000 species and has recently received more attention due to information implicating them in all kinds of biological processes via genomic, proteomic, and other “–omics” analyses. Consequently lipid researchers can also benefit from existing tools developed for other fields. The researchers involved in this project aim to answer several fundamental questions surrounding lipids in living systems in order to begin building up a comprehensive understanding of these molecules as a whole.

LiverX

According to the World Health Organization, the type two diabetes epidemic will continue well into the 21st century and affect people in all age groups. A major metabolic consequence of obesity is insulin resistance, which can lead to uncontrolled glucose production in the liver. This significantly contributes to the onset of type 2 diabetes. The etiology of insulin resistance is still poorly understood.
It is therefore the aim of ‘LiverX’ to systematically collect and analyse quantitative and dynamic data of the important metabolic networks at the levels of single cells, organs and whole organisms. Together with clinical data of patients this knowledge will be used to develop mathematical models of insulin resistance. Alterations in metabolic control are not unique to type two diabetes but underlie many other metabolism-related disorders including cancer. The data generated in this project should therefore have applications far beyond the project and provide novel perspectives for diagnostics and therapeutics.

SystemsX.ch Plant Growth

Plants serve as the basis for all food, fuel, and fiber. Plants have evolved a very sophisticated way to build cells, tissues, and organs. They also have a highly diverse metabolism and a life long ability to adapt their growth and development at their disposal. All of these individual mechanisms have been studied extensively, and many models created based on data collected in these studies. However, until recently, these models relied on fairly limited and rather qualitative data from various plant species. In this project the researchers aim to create a new generation of data describing the mechanical, metabolic and environmental properties of plant growth. By limiting their investigations to a single species, Arabidopsis thaliana, they aim to produce a set of models which can ultimately be unified into one model spanning all the data which can be used to comprehensively understand plant growth in all its complexity.

WingX

As a model organism the fruit fly, Drosophila melanogaster has been instrumental in the discovery of basic principles in the development from a fertilized egg cell to a multicellular organism. In spite of the detailed understanding of the genes and the regulatory networks that control the development of even a simple structure such as the fly wing, it is still not known how this structure attains its specific size and shape. The goal of this project is to obtain quantitative datasets of the developing wing as a basis to generate accurate and predictive computer models of this simple developing organ. To attain this goal scientist from computer science, engineering, physics and biology are collaborating to develop new imaging techniques, molecular read-outs, in vitro culture systems, and multiscale modeling tools. The development of the Drosophila wing is undoubtedly one of the simplest and best understood systems. Progress towards understanding this organ at the systems level will pave the way for an understanding of more complex organs.

YeastX

Although scientists are in the position now to generate large data sets on all sorts of cellular components, they are not able to keep up with their ability to integrate, analyze, and most importantly, interpret these vast amounts of information. This project aims at addressing the issues in conceptual theory challenges for the evolving cellular systems biology field. To achieve this, the researchers plan to tightly integrate computational and experimental approaches in an effort to solve how signals are dynamically translated within complex information networks, which ultimately lead to a specific cellular responses. The researchers will focus on two cellular signaling molecules, glucose and nitrogen. They want to see how nitrogen and glucose signals are translated quantitatively via a highly complex and interconnected information and regulatory network that essentially elicits its effects on cellular metabolism. By performing their initial experiments in the yeast model Saccharomyces cerevisiae, they aim to lay the groundwork for such studies in more complex systems such as mammals.

Integrated regulatory map of the genome of Mycobacterium tuberculosis

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

Development of a gene-centered, proteomics platform for the systematic identification of DNA-binding proteins or complexes

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

Quantifying robustness of biochemical modules to parametric and structural perturbations

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

Analysis and classification of conserved DNA elements through whole-genome multiple alignment

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

Analysis and simulation of the physical rules of lumen formation in organogenesis

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

Systems biology on ecosystems: exploring the mechanism of synchronized flowering by integrating molecular and modeling approaches

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

From single cells to single pores: Mathematical modeling and experimental testing of ionic imbalances induced by pore formation of bacterial toxin

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

Mechanisms of single cell injury repair in migrating cells

To support interdisciplinary research and education and to promote the future generation of Systems Biologists, SystemsX.ch funds PhD positions for students pursuing research projects that integrate at least two disciplines relevant to Systems Biology. The students are mentored jointly by investigators from two different disciplines such as computer science, engineering, nanotechnology, physics, mathematics, chemistry, biology, medicine, etc. SystemsX.ch grants the same amount to each IPhD.

Laboratory evolution of the lac systems in S. aureus

As an emerging field of research, Systems Biology is highly dependant on new innovative impulses, many of which are expected to come from the interfaces of traditional scientific disciplines. SystemsX.ch supports IPPs which catalyze the exploration of new research directions and ideas. These projects will bring together research teams from the different disciplines to address “seed” or “high -risk” topics critical for Systems Biology. IPPs will be supported for a maximum of one year and are non-renewable.

Decoding mechanisms of polarity establishment in C. elegens embryos

As an emerging field of research, Systems Biology is highly dependant on new innovative impulses, many of which are expected to come from the interfaces of traditional scientific disciplines. SystemsX.ch supports IPPs which catalyze the exploration of new research directions and ideas. These projects will bring together research teams from the different disciplines to address “seed” or “high -risk” topics critical for Systems Biology. IPPs will be supported for a maximum of one year and are non-renewable.

Development of techniques for analyzing the Trypsin-Resistant Proteome (TReP)

As an emerging field of research, Systems Biology is highly dependant on new innovative impulses, many of which are expected to come from the interfaces of traditional scientific disciplines. SystemsX.ch supports IPPs which catalyze the exploration of new research directions and ideas. These projects will bring together research teams from the different disciplines to address “seed” or “high -risk” topics critical for Systems Biology. IPPs will be supported for a maximum of one year and are non-renewable.