Technology - Systems biology
In order to understand complex biological systems, such as the development and progression of human disease, the relationship between elements within biomedical pathways must be ascertained with respect to one another and other interfering networks. The biological information gathered here must be integrated in order to obtain a view of how the biological system works.
MicroDiscovery is working on the key technologies for systems biology:
- Data integration: The complexity of major diseases cannot be adequately explained by a single experimental technique. MicroDiscovery has established techniques for integrating data from different biomedical experiments.
- Automation: Large data sets in the form of array data, proteomics data etc. must be analysed and integrated. MicroDiscovery develops tools and technologies that enable a high level of automation which allows the efficient analysis of large data sets.
- Quality control: In order to use high-throughput data in systems biology (as well as in diagnostics and therapeutic approaches), demanding standards have to be established for data quality. MicroDiscovery implements quality control processes for all stages of data analysis and integration.
- Prediction: The analysis of integrated data can identify and predict relevant biomedical mechanisms. Based on pathways thus derived, relevant biomedical questions can be modelled and analysed in silico.
One example project involving MicroDiscovery systems biology approaches is `PhysioSim - an in silico model for diagnosing obesity induced type 2 diabetes`.
PhysioSim - an in silico model for diagnosing obesity induced type 2 diabetes
The primary aim of this project that is being carried out jointly with MPI-MG and DifE (German Institute of Human Nutrition) is to develop a modelling and data integration platform for the diagnostic screening of obesity induced type 2 diabetes mellitus.
Initially, data generation will be based on DNA chips, however, the platform will be designed to integrate complex and heterogeneous data. One key feature of the platform will be the generation of hypotheses and models for disease progression based on in silico predictions. This requires completely new methods of modelling, network analyses and data integration.
Analysing complex, polygenic diseases, such as obesity induced type 2 diabetes, is a task that can only be achieved if the data from the different functional genomics experiments (gene and protein expression, sequence data, etc.) can be integrated with the physiological data and environmental factors.
For more information about this project, please contact:
Dr. Arif Malik
MicroDiscovery GmbH
Marienburger Straße 1
D - 10405 Berlin
Germany
Phone: +49-30-4435090-0
Fax: +49-30-4435090-10
E-Mail: [email protected]