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.
ICSB 2022 - Presentation and Posters
We could present our work on physiologically based pharmacokinetics (PBPK) modeling at ICSB2022.