Homo sapiens

In the CoVIDD project, we aim at unravelling interactions of viral proteins with the cellular factors and the host pathways involved in SARS-CoV-2 infection by a combination of modern technologies, genomics, proteomics, structural biology, chemoinformatics and drug discovery. To effectively search for drugs that could modify viral replication we need to know what human proteins and viral proteins interact. Thus, a comprehensive virus-host protein interaction network will help us to identify the
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Here we share resources and best practices to develop a disease map for COVID-19. The project is progressing as a broad community-driven effort. We aim to establish a knowledge repository on virus-host interaction mechanisms specific to the SARS-CoV-2. The COVID-19 Disease Map is an assembly of molecular interaction diagrams established based on literature evidence.

MESI-STRAT: Systems Medicine of Metabolic-Signaling Networks -A New Concept for Breast Cancer Patient Stratification.
Breast cancer is a complex disease with high prevalence in the European Union and world-wide. 75%-80 of the patients have estrogen receptor-positive (ER)-positive tumors and are treated with endocrine therapies. Endocrine therapies, which block ER-driven tumor growth, show high efficacy. Yet, a significant proportion of the patients will eventually relapse with metastatic breast
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In this project, we integrate COVID19 Disease Maps curated regulatory information in a macrophage logical model.
This allows logical simulations of the effects of acute inflammation caused by the SARS-CoV-2 virus, both in general and in a cell-specific perspective. Moreover, understanding the regulatory network behavior of macrophages following infection opens new ways to test and predict drug and drug combination effects, as a first step towards the development of new treatments.

Data and experimental methods to support the work in the following paper:

Establishing Consensus Annotation for the Hallmarks of Cancer, 2020, Yi Chen, F.J.Verbeek and K.Wolstencroft, in submission

nfdi4health aims to create the most comprehensive inventory of German epidemiological, public health and clinical trial data to date. It builds a centralised data catalogue with elaborate search functionalities, sophisticated data access management, and a data analysis toolbox, while respecting stringent requirements for privacy concerning personal health data. Standardisation services ensure a high degree of interoperability. Use cases covering prototypical study types and areas of research show
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The Integrated Platform for Endocrine Disruptor Risk Assessment (SNAPPER) project will propose solutions based around three core philosophies:
Integrated Science: Integration of knowledge from a complete pipeline of systems biology into a holistic yet mechanistic framework that enhances the understanding both of endocrine biology and of adverse effects due to externally induced disruption of the body’s endocrine system. The pipeline includes in vivo, in vitro, and in silico data resulting both
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This project bundles information about COVID-19 related studies and tools in Germany. It intends to provide an information hub for the different initiatives, their aims, their processes, standards and study protocols, as well as their available data and metadata.
It is initiated by the German National Research Data Infrastructure for Personal Health Data (nfdi4health), a consortium that plans to establish a concept for a federated research data infrastructure for personal health data in Germany.
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We used the Neo4j graph database approach to integrate the content of the COVID-19 Disease Map diagrams to efficiently access, query and manage the content of these diagrams and enable communication with external resources, such as Reactome and Recon, that already provide support via a similar environment.
This work complements the efforts on exploring COVID-19 disease mechanisms within the COVID-19 Disease Map Project.

Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that: (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that
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The project addresses the generation and establishment of programmed pacemaker cells for an in vitro drug testing possibility to perform predictive tests. This may lead to an improved treatment of cardiac arrhythmias or an accurate identification of potential drug molecules at a very early stage of development. Important benefits will arise in verifying the safety of a wide variety of medicines while reducing animal testing. For more information you may visit our project website at
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PoLiMeR is funded through the EU Marie Skłodowska-Curie Innovative Training Network (ITN), which drives scientific excellence and innovation. ITNs bring together universities, research institutes, industry and clinical partners from across the world to train researchers to doctorate level.

Metabolic diseases are a burden on the European population and health care system. It is increasingly recognised that individual differences with respect to history, lifestyle, and genetic make-up affect disease
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We will contribute to the LiSyM Research Network an open source, freely available and reproducible multiscale model of the human liver from single cell metabolism to whole liver function. The model will be available in existing standards of systems biology, provide standardized interfaces for data integration and be fully annotated to available biological, medical and computational ontologies. All data, models and source code will be shared within the LiSyM Research Network and made available to
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