Systems Medicine, Digital Twins & AI

Metabolic Inflammation and Carcinogenesis of the Liver

We build open, FAIR digital twins of human physiology — AI-powered models that predict disease and therapy, patient by patient.

Prof. Dr. Matthias König
Prof. Dr. Matthias König Group Leader

Data scientist, data analyst, computational modeler, bioinformatician, group leader. Application of computational modeling and machine learning to biological, medical and clinical questions and data...

Mariia Myshkina
Mariia Myshkina Ph.D. Student

Mariia works on ATLAS, a decision support tool for hepatocellular carcinoma (HCC). Based on AI methods, ATLAS processes all relevant patient data from databases,...

Shubhankar Palwankar
Shubhankar Palwankar Scientific Researcher

Shubhankar worked on enhancing our understanding of enalapril's pharmacokinetics and dynamics based on a physiologically based modeling approach. The work will build on top...

Michelle Elias
Michelle Elias Master Thesis

Michelle develops a physiologically based pharmacokinetic/ pharmacodynamic (PBPK) model of the sulfonylurea glimepiride. The objective is to enhance our understanding of the underlying causes...

Mariia Babaeva
Mariia Babaeva Student Assistant

Mariia works on a physiologically based model of lisinopril. Key questions are the effect of hepatic and renal disease on lisinopril pharmacokinetics and pharmacodynamics....

Amanda Schwaiger
Amanda Schwaiger Medical Thesis

Amanda develops a physiologically based pharmacokinetic/ pharmacodynamic (PBPK/PD) model of the diuretic hydrochlorothiazide. The objective is to enhance our understanding of how hydrochlorothiazide changes...

Minjun Kim
Minjun Kim Humboldt Internship Project

Kim is developing a physiologically based pharmacokinetic/ pharmacodynamic (PBPK/PD) model of the GLP-1 receptor agonist dulaglutide to better understand its absorption, distribution, metabolism, and...

Jessica Cruz
Jessica Cruz Humboldt Internship Project

Jessica Cruz is developing a physiologically based pharmacokinetic (PBPK) model of the leukotriene receptor antagonist montelukast to better understand its absorption, distribution, metabolism, and...

Shelee Bedón
Shelee Bedón Internship Project

Shelee is developing a physiologically based pharmacokinetic (PBPK) model of the multikinase inhibitor lenvatinib to better understand its absorption, distribution, metabolism, and excretion. The...

Chang Linh Nguyen
Chang Linh Nguyen Bachelor Project

Chang Linh is developing a physiologically based pharmacokinetic (PBPK) model of the β2-adrenergic receptor agonist albuterol (salbutamol) to better understand its absorption, distribution, metabolism,...

Sandeep Konaka Gautamdas
Sandeep Konaka Gautamdas Internship Project

Sandeep is developing a physiologically based pharmacokinetic (PBPK) model of the multi-kinase inhibitor regorafenib to better understand its absorption, distribution, metabolism, and excretion. The...

Antonio Aquiles Alvarez Aguilar
Antonio Aquiles Alvarez Aguilar Internship Project

Antonio will delve into the influence of parameter variability in PBPK models, focusing on the consequential effects for dynamic liver function assessments. The overarching...

What if every medical model served you, not the average — and belonged to everyone?

Digital Twins

Every patient becomes a model. We build physiologically based digital twins — living simulations that mirror an individual from molecule to whole body — so care can be planned before it is given.

AI

Data alone cannot explain a patient — mechanism can. We fuse machine learning with physiological models to turn complex biomedical data into predictions clinicians can trust.

Digital Pathology

A biopsy holds a whole organ's story. We use AI to read whole-slide histology at scale, quantifying liver structure and disease with a precision no eye can match.

Pharmacometrics

The right dose is never one-size-fits-all. Our PBPK/PD models simulate how each body absorbs, distributes, and clears a drug, turning population averages into individual precision.

Open & FAIR

Science that cannot be reproduced cannot be trusted. Every model, dataset, and line of code we publish is open, FAIR, and built for the community to verify, reuse, and extend.