Eleven Strategies for Making Reproducible Research and Open Science Training the Norm at Research Institutions
We are pleased to announce our latest paper in eLife that provides a roadmap for integrating reproducible research and open science practices into academic work.
We are excited to announce that our recent paper, "Eleven Strategies for Making Reproducible Research and Open Science Training the Norm at Research Institutions," has been published in eLife, offering a roadmap for integrating reproducible research and open science practices into academic work. Developed in collaboration with the German Reproducibility Network, the paper proposes eleven strategies focusing on modifying research assessment criteria, enhancing training, and fostering community support. This work aims to encourage researchers and institutions to adopt practices that increase the reliability and accessibility of scientific research.
Your Dietary Digitial Twin (DDtwin)
Advancing Precision Nutrition: Workshop Success on Dietary Digital Twin, a workshop in Leiden from 30 October - 3 November 2023.
We are excited to announce our participation in a groundbreaking workshop dedicated to the development of the Dietary Digital Twin (DDtwin) technology platform. This innovative endeavor aims to provide precision nutrition by integrating biology-based and data-driven models with real-life data, harnessing the power of wearable sensors and applications.
The workshop brought together experts and enthusiasts from various fields to tackle the intricate task of extending models of whole-body glucose metabolism. The primary focus was on two technical challenges: the integration of real-time Continuous Glucose Monitoring (CGM) data from wearable sensors, and the creation of a human-centered user interface that connects the DDtwin to dietary intake data collected via apps.
The collaborative efforts made significant strides towards enabling the DDtwin to deliver personalized dietary insights and recommendations. Moreover, the workshop served as a platform for discussing the ethical, legal, and social implications (ELSI) of deploying such advanced technology in everyday practice.
Our participation in this workshop underscores our commitment to advancing health technology and contributing to a future where nutrition is as unique as the individual. Stay tuned for further developments as we continue to navigate the exciting realm of digital twins and precision nutrition. For more information see https://www.lorentzcenter.nl/your-dietary-digital-twin-ddtwin.html.
Humboldt Internship Program 2024 - Computational Modeling of Drug Detoxification - A Systems Medicine Approach
We offer Internships in 2024, academic three-month research stays at Humboldt-Universität zu Berlin. The application for the Summer Term Internships 2024 is open from 03 November to 10 December 2023.
Humboldt Internship Program is an international short-term program for subject-specific, experiential learning. It allows participants to work with teams in research projects and university-related start-ups for three months.
This program is designed for advanced Bachelor as well as Master students and PhD candidates. Choose from projects in various academic areas, receive individual advising, and earn ECTS credit points. Some projects offer online internship opportunities. Please check the individual project descriptions for details.
The Summer Term internships take place from May to August 2023. More information https://hic.hu-berlin.de/en/internship-program .
e:Med Meeting 2023 on Systems Medicine: Quantifying Fat Zonation in Liver Lobules: An Integrated Multiscale In-silico Model
We successfully presented our research on reproducible digital twins for personalized liver function assessment at the e:Med Meeting 2023 on Systems Medicine.
Essential prerequisites for the practical application and translation of computational models include: i) reproducibility of results; ii) model reusability and extensibility; iii) data availability; and iv) strategies for model stratification and individualization. Here, we present a modeling workflow built around these foundational prerequisites, with a focus on liver function tests.
Despite the paramount significance of liver function assessment in hepatology, reliable quantification remains a clinical challenge. Dynamic liver function tests offer a promising method for non-invasive in vivo assessment of liver function and metabolic phenotyping.
By leveraging whole-body physiologically-based pharmacokinetic (PBPK) models, we're simulating these tests and positioning PBPK models as digital twins for metabolic phenotyping and liver function assessment. To develop and validate our models, we established the open pharmacokinetics database, PK-DB, containing curated data from 600+ clinical studies. Our models are individualizable and stratifiable, enabling simulation of lifestyle factors and co-administration effects on drug metabolism. Our models have been instrumental in clinical scenarios: from predicting individual outcomes post-hepatectomy to discerning the impact of CYP2D6 gene variants on liver function tests. These models are constructed hierarchically, describing metabolic and other biological processes in organs like the liver and kidneys, seamlessly integrated with whole-body physiology.
Notably, all models and data are readily available and reproducible for reuse, encoded in the Systems Biology Markup Language (SBML). We will provide an overview of these PBPK models and demonstrate how SBML and FAIR principles can facilitate model development, coupling, and reuse.
Preprint: Quantifying Fat Zonation in Liver Lobules: An Integrated Multiscale In-silico Model
Our latest preprint is now out: An integrated multiscale model for quantifying fat zonation in liver lobules.
We have developed a sophisticated computer model to explore how various factors contribute to the distribution and accumulation of fat in the liver, a phenomenon known as "metabolic zonation." This model will help in understanding how liver health is affected by different conditions and could play a crucial role in studying diseases like MASLD (metabolic dysfunction-associated steatotic liver disease). By simulating liver functions, the model considers interactions between blood flow, oxygen levels, and fat metabolism within liver lobules.
Hannah Menghis from Brown University starts her internship
We welcome Hannah Menghis who will establish a PBPK model of semaglutide.
Hannah will establish a pharmacokinetic dataset of semaglutide and develop an initial version of a physiologically based pharmacokinetic (PBPK) model of the GLP-1 inhibitor semaglutide. The objective is to enhance our understanding of the underlying causes of intraindividual variability in semaglutide treatment, e.g., differences in renal function.
A pathway model of glucose-stimulated insulin secretion in the pancreatic β-cell
Our latest publication is now on Frontiers of Endocrinology: A pathway model of glucose-stimulated insulin secretion in the pancreatic β-cell
The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct a novel data-driven kinetic model of GSIS in the pancreatic β-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and phenomenological equations for insulin secretion coupled to cellular energy state, ATP dynamics and (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and biphasic insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β-cell.
Bachelor Thesis Beatrice Stemmer Mallol
Beatrice Stemmer Mallol submitted her Bachelor thesis developing a PBPK model of talinolol
Congratulations to Bea for successfully submitting her Bachelor thesis on a physiologically based pharmacokinetic (PBPK) model of the probe drug talinolol for the characterization of intestinal P-glycoprotein. Talinolol, a cardioselective beta-blocker applied in treating various cardiovascular diseases and tachyarrhythmias, is widely adopted as a probe drug for the intestinal efflux transporter P-glycoprotein. In the human body, P-gp plays a crucial role, given its distribution across various tissues to protect against potentially toxic substances, while expediting the elimination of xenobiotics. By employing talinolol for P-gp phenotyping, researchers can evaluate factors that influence P-gp-mediated transport, such as P-gp genetic polymorphisms and its distribution throughout the intestine. Bea's thesis presents a comprehensive dataset on talinolol pharmacokinetics, the study of how the body absorbs, distributes, metabolizes, and excretes drugs. This data was used to develop a PBPK model for talinolol, offering detailed insights into its behaviour within the body. The new model investigates the impact of numerous factors on talinolol's pharmacokinetics, including the genetic variants of P-gp, enzymatic activity of transporters OATP2B1 and OATP1B1, and site-specific distribution of P-gp and OATP2B1 proteins in the intestine. It also investigates the impact of diseases like liver cirrhosis and renal dysfunction.
PhD Defense of Jan Grzegorzewski
Jan Grzegorzewski defended his PhD thesis on physiologically based pharmacokinetic modeling (PBPK) for dynamical liver function tests and Cytochrome P450 (CYP) phenotyping.
Congratulations to Jan Grzegorzewski for successfully defending his PhD thesis on physiologically based pharmacokinetic modeling (PBPK) for dynamical liver function tests and Cytochrome P450 (CYP) phenotyping. Jan developed the extensive open pharmacokinetics database, PK-DB, which helped to demonstrate demographic biases and errors in existing pharmacokinetic literature. His research includes a meta-analysis on caffeine pharmacokinetics, discovering multiple factors influencing liver function and CYP1A2 activity. Furthermore, he developed a PBPK model to predict individual plasma concentrations of dextromethorphan based on the CYP2D6 genotype and physiological traits. His work is expected to significantly enhance the precision of dynamic liver function tests and advance personalized medicine.
Xenia Petukhova and Zahra Bahri start Humboldt Internship Program
Humboldt Internship Program 2023 - Computational Modeling of Drug Detoxification - A Systems Medicine Approach
We welcome Xenia and Zhara in our group for the Humboldt Internship Program 2023. Xenia and Zhara will develop a physiologically based pharmacokinetic (PBPK) model for inulin and creatinine to assess the glomerular filtration rate (GFR). Our goal is to gain a deeper understanding of the impact of various inulin protocols on GFR measurement and the influence of physiological factors, such as body composition, on GFR determination using inulin. Furthermore, it should enhance our understanding of how variations in creatinine production, muscle mass, and tubular secretion of creatinine influence eGFR calculations.
Keynote: Advancing Liver Function Assessment: Personalized and Stratified Approaches with Standardized Computational Models and Data
Keynote talk at the Workshop on Computational Models in Biology and Medicine 2023 organized by Nicole Radde and Sebastian Höpfl.
Essential prerequisites for the practical application and translation of computational models include: i) reproducibility of results; ii) reusability and extensibility of models; iii) data availability; and iv) strategies for model stratification and individualization. In this study, we present a modeling workflow tailored to these critical aspects, with a focus on liver function tests. Evaluating liver function is a crucial task in hepatology, yet accurately quantifying hepatic function has persisted as a clinical challenge. Dynamic liver function tests offer a promising method for non-invasive in vivo assessment of liver function and metabolic phenotyping. These clinical tests determine liver function through the elimination of a specific test substance, thus revealing information about the liver's metabolic capacity. We employed whole-body physiologically-based pharmacokinetic (PBPK) models to simulate these tests, which encompass absorption, distribution, metabolism, and elimination processes. PBPK models serve as powerful instruments for investigating drug metabolism and its impact on the human body. In this research, we showcase our efforts in utilizing PBPK models as digital twins for metabolic phenotyping and liver function evaluation. To develop and validate our models, we created the first open pharmacokinetics database, PK-DB, containing curated data from over 600 clinical studies. Our models are individualizable and stratifiable, enabling simulation of lifestyle factors and co-administration effects on drug metabolism. We have applied our models to various clinical inquiries, such as simulating individual outcomes post-hepatectomy using an indocyanine green model and examining the influence of CYP2D6 gene variants through a dextromethorphan model integrated with drug-gene interactions. These models are constructed hierarchically, describing metabolic and other biological processes in organs like the liver and kidneys, connected to whole-body physiology. All models and data are accessible for reuse in a reproducible manner, encoded in the Systems Biology Markup Language (SBML). In this study, we provide an overview of PBPK models and demonstrate how SBML, COMBINE standards, and FAIR principles can facilitate model development, coupling, and reuse.
Preprint: Eleven Strategies for Making Reproducible Research and Open Science Training the Norm at Research Institutions
We present 11 strategies for making #OpenScience & #ReproducibleResearch the norm at research institutions, with tips for implementation & resources. Let's join hands to make these practices standard in the scientific community!
Across disciplines, researchers increasingly recognize that open science and reproducible research practices may accelerate scientific progress by allowing others to reuse research outputs and by promoting rigorous research that is more likely to yield trustworthy results. While initiatives, training programs, and funder policies encourage researchers to adopt reproducible research and open science practices, these practices are uncommon in many fields. Researchers need training to integrate these practices into their daily work. We organized a virtual brainstorming event, in collaboration with the German Reproducibility Network, to discuss strategies for making reproducible research and open science training the norm at research institutions. Here, we outline eleven strategies, concentrated in three areas: (1) offering training, (2) adapting research assessment criteria and program requirements, and (3) building communities. We provide a brief overview of each strategy, offer tips for implementation, and provide links to resources. Our goal is to encourage members of the research community to think creatively about the many ways they can contribute and collaborate to build communities, and make reproducible research and open science training the norm. Researchers may act in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees. Institutional leadership and research administration and support staff can accelerate progress by implementing change across their institutions.
Preprint: A physiologically based pharmacokinetic model for CYP2E1 phenotyping via chlorzoxazone
Our latest preprint is now on bioRxiv: A physiologically based pharmacokinetic model for CYP2E1 phenotyping via chlorzoxazone
We have developed a physiologically based pharmacokinetic (PBPK) model for CYP2E1 phenotyping via chlorzoxazone. This model takes into account various factors that can influence the activity of CYP enzymes, such as genetics, diet, age, environmental factors, and disease, making it a highly effective and reliable tool for assessing the in vivo activity of the CYP2E1 enzyme. What is truly exciting about this research is that it incorporates the effect of ethanol consumption and liver impairment on the results of metabolic phenotyping with chlorzoxazone. This means that the model is not only accurate but also highly practical, as it takes into account factors that are commonly encountered in clinical practice. The extensive pharmacokinetic dataset for chlorzoxazone and the validated PBPK model are expected to have a significant impact on the field of pharmacokinetics, as they provide a reliable and comprehensive tool for assessing the activity of CYP2E1 in a wide range of clinical contexts. This research represents a major step forward in our understanding of the factors that influence the activity of CYP enzymes and promises to have a significant impact on the development of new drugs and the optimization of existing ones.
Michael Stifel Price to promote interdisciplinary, data-driven research
M. Albadry, S. Höpfl, and M. König won the QuaLiPerF Michael Stifel Price for interdisciplinary, data-driven research.
M. Albadry, S. Höpfl, and M. König won the QuaLiPerF Michael Stifel Price for interdisciplinary, data-driven research for the study
Albadry M, Höpfl S, Ehteshamzad N, König M, Böttcher M, Neumann J, Lupp A, Dirsch O, Radde N, Christ B, Christ M, Schwen LO, Laue H, Klopfleisch R, Dahmen U. Periportal steatosis in mice affects distinct parameters of pericentral drug metabolism. Sci Rep. 2022 Dec 17;12(1):21825. doi: 10.1038/s41598-022-26483-6.
The study was conducted as part of a project to model the pharmacokinetics of steatotic mice and explore the impact of periportal steatosis on drug metabolism parameters. With the interdisciplinary approach of QuaLiPerF, we collaborated with three fields within life sciences: Mohamed Albadry for data generation, Matthias König for data analysis, and Sebastian Höpfl for uncertainty quantification. The data generation involved performing animal experiments, pharmacokinetic studies, and analyzing steatosis by quantifying its spatial distribution, zonated expression, and activity of drug metabolism enzymes (CYP).
Publication: Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022
Our overview of specifications in systems and synthetic biology with a brief overview of the COMBINE 2022 is available.
The Journal of Integrative Bioinformatics has published our editorial for a special issue providing updated specifications of the COMBINE standards in systems and synthetic biology. The 2022 issue includes three updates to the standards, namely CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This publication is a valuable resource for researchers looking to keep up with the latest developments in COMBINE standards. Additionally, the editorial section of the issue provides a brief overview of the COMBINE 2022 meeting held in Berlin.
Preprint: A consensus model of glucose-stimulated insulin secretion in the pancreatic beta-cell
Our latest preprint is now on bioRxiv: A consensus model of glucose-stimulated insulin secretion in the pancreatic beta-cell
The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic beta-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). The workflow was applied to construct the first consensus model of GSIS in the pancreatic beta-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and beta-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and equations for insulin secretion coupled to cellular energy state (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the beta-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species.
PBPK modelling of sorafenib and Gd-EOB-DTPA
Frances Okibedi and Narudeem Sojimade will develop models of the anticancer drug sorafenib in HCC and the liver contrast agent Gd-EOB-DTPA.
Within this project, a high quality database of sorafenib pharmacokinetic data used in systemic therapy of hepatocellular carcinoma will be established. Based on the pharmacokinetic data set, a physiologically based pharmacokinetic (PBPK) model will be established for the evaluation of systemic sorafenib therapy in liver cancer. The model will be used to address important clinically relevant questions such as (1) sorafenib is contraindicated in advanced liver cirrhosis, (2) genetic variants of OATP uptake transporters may alter the efficacy of sorafenib, (3) CYP3A4 substrates lead to drug-drug interactions with sorafenib.
Using established data curation pipelines, this project will extend our pharmacokinetics database PK-DB with literature data on Gd-EOB-DTPA. Based on the pharmacokinetic data set, a physiologically based pharmacokinetic (PBPK) model will be established for the evaluation of the pharmacokinetics of Gd-EOB-DTPA and its use as an MRI contrast agent. The model will be used to address important clinically relevant questions such as (1) the effect of renal and hepatic impairment on Gd-EOB-DTPA pharmacokinetics, (2) the effect of perfusion and OATP on Gd-EOB-DTPA pharmacokinetics, (3) the relationship between concentration and MRI attenuation.
X-Research Group - Digital twins for the treatment of hypertension
M. König receives funding of the Berlin University Alliance and the DFG for the X-Student Research Group: Digital twins for the treatment of hypertension
Understanding a drugs pharmacokinetics - how it is absorbed, distributed, metabolised and excreted by the body - and its pharmacodynamics - how it affects the body - is a key challenge in treating people. Angiotensin converting enzyme (ACE) inhibitors and diuretics are two classes of drugs used to treat high blood pressure. Both are among the most commonly prescribed drugs due to the high prevalence of hypertension in an ageing society.
In this X-student research group we will develop physiologically based pharmacokinetic models of the diuretic hydrochlorothiazide and the ACE inhibitor lisinopril. The methodological approach is a combination of lectures, tutorials and practical work by the students. The research project is aimed at students with a STEM (science, technology, engineering, mathematics) background, including biology, computer science and medical students. Basic programming skills in Python are required.
With the X-Student Research Groups, the Berlin University Alliance supports research teams consisting of young researchers and students. The goal is to involve students in current research projects of the alliance partners and to enable them to participate in (cutting-edge) research already during their studies.
Funded under the Excellence Strategy of the Federal Government and the Länder by the Berlin University Alliance.
M. König has been elected as PEtab editor (2023-2026)
Proud to be elected editor for PEtab, a data format for specifying parameter estimation problems in systems biology.
PEtab is a data format for specifying parameter estimation problems in systems biology. PEtab is built around SBML and is based on tab-separated values (TSV) files. It is intended as a standardized way to provide information for parameter estimation that is beyond the current scope of SBML. This includes, for example: (1) specifying and linking measurements to models; (2) defining model outputs; (3) specifying noise models; (4) specifying parameter bounds for optimization; (5) specifying multiple simulation conditions with potentially shared parameters. Matthias König has been elected PEtab Editor from March 2023 to March 2026.
Preprint: Simvastatin therapy in different subtypes of hypercholesterolemia
Our latest preprint is now on medRxiv: Simvastatin therapy in different subtypes of hypercholesterolemia - a physiologically based modelling approach.
Hypercholesterolemia is a multifaceted plasma lipid disorder with heterogeneous causes including lifestyle and genetic factors. A key feature of hypercholesterolemia is elevated plasma levels of low-density lipoprotein cholesterol (LDL-C). Several genetic variants have been reported to be associated with hypercholesterolemia, known as familial hypercholesterolemia (FH). Important variants affect the LDL receptor (LDLR), which mediates the uptake of LDL-C from the plasma, apoliporotein B (APOB), which is involved in the binding of LDL-C to the LDLR, and proprotein convertase subtilisin/kexin type 9 (PCSK9), which modulates the degradation of the LDLR. A typical treatment for hypercholesterolemia is statin medication, with simvastatin being one of the most commonly prescribed statins. In this work, the LDL-C lowering therapy with simvastatin in hypercholesterolemia was investigated using a computational modeling approach. A physiologically based pharmacokinetic model of simvastatin integrated with a pharmacodynamic model of plasma LDL-C (PBPK/PD) was developed based on extensive data curation. A key component of the model is LDL-C turnover by the liver, consisting of: hepatic cholesterol synthesis with the key enzymes HMG-CoA reductase and HMG-CoA synthase; cholesterol export from the liver as VLDL-C; de novo synthesis of LDLR; transport of LDLR to the membrane; binding of LDL-C by LDLR via APOB; endocytosis of the LDLR-LDL-C complex; recycling of LDLR from the complex. The model was applied to study the effects of simvastatin therapy in hypercholesterolemia due to different causes in the LDLR pathway corresponding to different subtypes of hypercholesterolemia. Model predictions of LDL-C lowering therapy were validated with independent clinical data sets. Key findings are: (i) hepatic LDLR turnover is highly heterogeneous among FH classes; (ii) despite this heterogeneity, simvastatin therapy results in a consistent reduction in plasma LDL-C regardless of class; and (iii) simvastatin therapy shows a dose-dependent reduction in LDL-C. Our model suggests that the underlying cause of hypercholesterolemia does not influence simvastatin therapy. Furthermore, our model supports the treatment strategy of stepwise dose adjustment to achieve target LDL-C levels. Both the model and the database are freely available for reuse.
ATLAS - AI and Simulation for Tumor Liver ASsessment
The BMBF is funding our joint project ATLAS in the "Computational Life Sciences - AI Methods for Systems Medicine".
Liver cancer is the second most common cause of cancer-related death. Diagnosis and treatment are time-critical and require highly patient-specific diagnostic and treatment pathways. Medical decision-making is based on a variety of interdependent factors related to different medical disciplines, past experience and clinical guidelines. Taking into account all decision factors in combination with the possible therapeutic approaches is a major challenge for physicians and often cannot be solved optimally even in an interdisciplinary tumor board. In this project, we are developing ATLAS, a decision support tool that will significantly assist clinicians in meeting this challenge. Based on AI methods, ATLAS processes all relevant patient data from databases, systems medicine and continuum biomechanical in silico prognosis models as well as individual patient data. The tool is being developed in a co-design approach by experts in surgical oncology, mathematical modeling and machine learning. The selected technologies will integrate automated understanding of a highly complex patient situation through simulation of liver functions with expert knowledge and ontology-driven learning with knowledge graphs from retrospective liver tumor cases. ATLAS will be based on a detailed historical data cohort of more than 6,000 patients with liver tumors and will be evaluated on case studies at the University Hospital of Jena. The integration of medical expert knowledge, mathematical modeling and artificial intelligence represents a highly original and promising approach for high-quality diagnosis and treatment of liver tumors, resulting in patient-specific improvement of prognosis. The scientific knowledge gained from these projects will provide opportunities for transfer to malignancies in other organs, such as the lung, kidney or brain. The development of tools and demonstrators will provide sustainable exploitation pathways for future commercial applications.
New Bachelor Student - Afruja Hossain
Afruja Hossain started working in our lab on a systematic overview of the protein variability of cytochrome P450 (CYP450) isoforms in the human liver.
Visiting EU parliament and attending STOA AI session
We were selected to participate in an European policy journey on the topic EU research funding and science communication by the Berlin University Alliance (BUA).
Humboldt Internship Program 2023 - Computational Modeling of Drug Detoxification - A Systems Medicine Approachhttps://hic.hu-berlin.de/en/internship-program/projects/201
How does CYP2D6 genotype effect metabolic phenotyping? See our latest paper in Frontiers in Pharmacology for answers.
M. König receives funding of the Berlin University Alliance and the DFG for the X-Student Research Group: Physiologically based modeling of drugs: ACE inhibitors
We organized the Computational Modeling in Biology Network (COMBINE) meeting in Berlin this year with > 100 participants from 06-08 October 2022.