Projects: Kinetics on the move - Workshop 2016, Multi-Scale Models for Personalized Liver Function Tests (LiSyM-MM-PLF), FAIRDOM user meeting, COMBINE Multicellular Modelling, FAIRDOM & LiSyM & de.NBI Data Structuring Trainingorcid.org/0000-0003-1725-179X
We are investigating liver metabolism and function with the help of computational models and methods.
Read more about the LiSyM junior group at: www.livermetabolism.com
Junior Group Leader
Dr. Matthias König
Institute for Theoretical Biology
Invalidenstraße 43, 10117 Berlin, Germany
phone +49 30 2093-8450
The liver is the central metabolic organ of our body playing a crucial role in the clearance of drugs, xenobiotics and numerous metabolites
The workshop focuses on the publication, curation, retrieval, and usage of kinetic data from the reaction kinetics database SABIO-RK and on the use of data in modeling. There will be experience reports from scientists who successfully used experimental data to formulate or verify biological hypotheses with the computer, and you will experience how experimental data can be used with computational models.
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
IMOMESIC - Integrating Modelling of Metabolism and Signalling towards an Application in Liver Cancer
One of the most challenging questions in cancer research is currently the interconnection of metabolism and signalling. An understanding of mechanisms that facilitate the physiological shift towards a proliferative metabolism in cancer cells is considered a major upcoming topic in oncology and is a key activity for future drug development. Due to the complexity of interrelations, a systems biology