Network biology, knowledge formalisation, cancer, data analysis
The Disease Maps Project is designed as a large-scale community effort. It is a network of groups that work together in order to better understand disease mechanisms. The project exchanges best practices, share information, develop tools to make it easier for all the involved groups to achieve their goals.
Projects: COVID-19 Disease Map
Web page: https://disease-maps.org
Projects that do not fall under current programmes.
Projects: Manchester Institute for Biotechnology, ICYSB 2015 - International Practical Course in Systems Biology, iRhythmics, INBioPharm, EmPowerPutida, Systo models, MycoSynVac - Engineering Mycoplasma pneumoniae as a broad-spectrum animal vaccine, Multiscale modelling of state transitions in the host-microbiome-brain network, Extremophiles metabolsim, NAD COMPARTMENTATION, Agro-ecological modelling, Bergen(Ziegler lab) project AF-NADase, NAMPT affinity, Stress granules, Modelling COVID-19 epidemics, Bio-crop, ORHIZON, Coastal Data, SASKit: Senescence-Associated Systems diagnostics Kit for cancer and stroke, hybrid sequencing, HOST-PAR, BioCreative VII, Boolean modeling of Parkinson disease map, Orphan cytochrome P450 20a1 CRISPR/Cas9 mutants and neurobehavioral phenotypes in zebrafish, Selective Destruction in Ageing, Viral Metagenomic, Synthetic biology in Synechococcus for bioeconomy applications (SynEco), testproject, SDBV ephemeral data exchanges, Test project, The BeeProject, PHENET, LiceVault, EbN1 Systems Biology
Web page: Not specified
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.
The COLOSYS project aims to develop a deeper understanding of colon cancer networks and convert them into computer models with which it will be better to predict response to treatment. The combination of computational, experimental and clinical testing will provide understanding of drug resistance mechanisms, and allow personalised treatment of colon cancer.
Systems Biology studies the properties and phenotypes that emerge from the interaction of biomolecules where such properties are not obvious from those of the individual molecules. By connecting fields such as genomics, proteomics, bioinformatics, mathematics, cell biology, genetics, mathematics, engineering and computer sciences, Systems Biology enables discovery of yet unknown principles underlying the functioning of living cells. At the same time, testable and predictive models of complex ...