Dr. Matthias König	Group leader 2016-	Data scientist, data analyst, computational modeller, bioinformatician, group leader. Application of computational modelling and machine learning to biological, medical, and clinical questions and datasets. Predictive modeling of physiological processes and systems with focus on metabolic research. Research projects on glucose homeostasis in normal subjects and diabetes, personalization of liver function tests for the improvement of diagnosis, and stratification and individualization of computer models for precision medicine.
	Jan Grzegorzewski	Ph.D. student (mentoring) 2017-	Jan works on the stratification and personalization of computational models. He develops methods and software for semi-automatic data curation of pharmacokinetics information and a pharmacokinetics database for the storage and FAIR access of such data. Jan contributes to an online database for running reproducible simulation experiments in computational biology, i.e. tellurium-web. Jan developed an influenza sub-classification platform based on peptides using data science and machine learning approaches. Part of the work was the development of the data management system required for bioinformatics analysis of peptide-virus binding data FluTypeDB.
	Patrick Pett	Ph.D. student (mentoring) 2017-	Patrick works on the analysis of mammalian circadian clock in the human liver based on the analysis of OMICS datasets.

Alumni

	Roman Schulte	Bachelor thesis 2018	Within his thesis the concept of an Modular Research Environment (MRE) was developed. An MRE is a modular application framework that can be extended with plugins to create a new tool. InSilico is the working proof-of-principle implementation of such an MRE and was developed alongside this work. The created infrastructure of InSilico can be used to create, share and reuse custom research tools with less effort, time, and resources compared to the traditional approach.
	Shalin Shah	Google Summer of Code 2018 2018	The Systems Biology Simulation Core Library (SBSCL) provides an efficient and exhaustive Java™ implementation of methods to interpret the content of models encoded in the Systems Biology Markup Language (SBML) and its numerical solution. This library is based on the JSBML project and can be used on every operating system for which a Java Virtual Machine is available. Within this project SBSCL was extended among others with better support for SED-ML and COMBINE archives. See also https://ssdoesgsoc.wordpress.com/.
	Takahiro Yamada	Google Summer of Code 2017 2017	Within this project as part of Google Summer of Code 2017 a Web App for SBML models was implemented which provides functionality for time course simulation, steady state analysis, and parameter estimation. The Web App provides options for uploading SBML models and experimental data files and run the respective simulations with the models. See also https://gsoc2017developwebservice.blogspot.com/.
	Dimitra Eleftheriadou	Student assistant 2016-2017	Dimitra (Master Toxicology) worked on the analysis and digitalization of literature data for liver function tests.