König Lab
Systems Medicine of the Liver
The König group works on computational modeling, data science, bioinformatics methods and machine learning on biological, medical and clinical data. Main research interest is the predictive modeling of physiological processes and systems with focus on metabolic research in the liver.
Key projects are the personalized evaluation of liver function tests and multi-scale modeling of the liver. For more information have a look at our publications and projects.
Dr. Matthias König More
Junior Group Leader
LiSyM - Systems Medicine of the Liver
Institute for Biology, Institute for Theoretical Biology
Humboldt Universität zu Berlin
Invalidenstraße 110, 10115 Berlin, Germany
+49 30 2093-98435
koenigmx@hu-berlin.de
Our projects More
Here you can find information about our latest projects. Most of them are hosted on GitHub and Open Source. Core interests are the computational modeling of liver metabolism and multi-scale modes of liver function.
Our publications More
Scientific publications and preprints can be found below. We are trying to publish as much as possible of our scientific work as Open Access.
Open Positions More
Bachelor and Master students, and research interns are always welcome. Just send a mail to Matthias König.
COMBINE coordinator (2018 - )
Our lab works actively on the standardization and reproducibility of computational models in biology.
The 'COmputational Modeling in BIology' NEtwork (COMBINE) is an initiative to coordinate the development
of the various community standards and formats for computational models.
SBML editor (2018 - 2020)
Systems Biology Markup Language (SBML) is a free and open interchange format for computer models of biological processes.
SBML is useful for models of metabolism, cell signaling, and more. It continues to be evolved and expanded by an international community.
SED-ML editor (2017 - 2019)
The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format
for encoding simulation setups, to ensure exchangeability and reproducibility of simulation experiments.