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König, M.

Publications and source records attributed to König, M..

18 recordsLinked to original sources

Nanoscale PI(4,5)P2 partitioning driven by FGF2 oligomerization triggers membrane pore formation

Fibroblast Growth Factor 2 (FGF2) is a potent tumor cell survival factor. Unlike most secreted proteins, FGF2 lacks a signal peptide and is exported by an unconventional secretory pathway, bypassing the ER/Golgi system. A key event of this unusual secretory mechanism is FGF2 recruitment at the inner plasma membrane leaflet mediated by the phosphoinositide PI(4,5)P2. This interaction induces FGF2 oligomerization which, in turn triggers the formation of a transient lipidic membrane pore with a toroidal structure. FGF2 oligomers populating such membrane pores are captured and disassembled at the outer plasma membrane leaflet through interactions with heparan sulfate chains of the cell surface proteoglycan GPC1, resulting in full translocation of FGF2 across the plasma membrane. Once FGF2 appears on cell surfaces, it engages in signaling complexes initiating autocrine or paracrine signaling. Here, using a multidisciplinary and multiscale approach, we show that FGF2 self-assembles into oligomers that induce lateral PI(4,5)P2 partitioning and membrane remodeling. This process depends on PI(4,5)P2 reaching a critical local threshold concentration that, due to its non-bilayer properties destabilizes the bilayer, facilitating pore formation and FGF2 translocation across the plasma membrane. Our findings highlight a fundamental principle of how protein oligomerization can induce lateral partitioning of membrane lipids that in turn leads to a drastic membrane remodeling event: the transformation of the lipid bilayer into a toroidal membrane pore. The underlying mechanism is likely to be applicable to a wide range of membrane lipid interacting proteins that induce membrane remodeling in other cellular processes.

biochemistry↗

Automated Segmentation of Hepatic Vessels and Lobules in Whole-Slide Images Using U-Net Models

Automated analysis of hepatic vascular structures and lobules within whole-slide histological images is critical for ensuring accurate and timely morphometric evaluations and facilitating advancements in computational liver histology. Nonetheless, the intricate morphology of the tissue, variability in staining techniques, and the requirements for high-resolution imaging present substantial challenges to the precision of segmentation processes. We present a robust deep-learning pipeline using adaptive patch extraction and specialized U-Net architectures for segmenting vessels, bile ducts, and lobules in Glutamine Synthetase and Picro-Sirius-Red stained porcine liver sections. Our architecture incorporates a weight-boosted nnU-Net framework to effectively manage class imbalances and improve the detection of smaller vascular structures. Geometric data transformations enhanced the robustness and generalizability of the segmentation models. Evaluations conducted through five-fold cross-validation, as well as assessments utilizing independent test datasets, resulted in Dice similarity scores: 0.960 for lobules, 0.801 for central veins, 0.909 for hepatic arteries, 0.609 for portal veins, and 0.710 for bile ducts. The developed segmentation pipeline additionally supports comprehensive morphometric analyses of structural parameters, including number and size (diameter, area) of vascular structures, bile ducts, and lobules. For e.g., the diameter of hepatic arteries ranges between 30-90 {micro}m. These findings underscore the practical relevance of adaptable segmentation frameworks in advancing computational histological analysis of liver tissue.

bioinformatics↗

Mass cytometry data integration methods reveal rural-urban gradient of immune profiles across geography

The human immune system strongly varies across populations and is shaped by a wide range of host and environmental factors. As such, a rural compared to urban lifestyle has previously been associated with baseline differences in immune profiles, which can impact outcome of vaccination. The question whether there is a immune signature that associates with rural living irrespective of ancestry or geography has been addressed here along with which analytical methods are most suitable. Using three mass cytometry datasets, we studied shared and population-specific immune characteristics of healthy rural- or urban-living Indonesian, Senegalese and Tanzanian adults and urban-living Europeans. After harmonized preprocessing and quality control, 75.4 million cells were integrated using four different data-integration methods. Among these, CytoNorm with in silico references performed best, revealing shared differences in the differentiation state within the lymphocyte compartment that distinguishes rural from urban-living individuals. Differentiated CD4+ T cells expressing CTLA-4, PD-1 and ICOS characterized rural living, as did CD4+ T cells with high CD161 expression in combination with CRTH2 and GATA3. In the innate compartment, CD56- CD16 NK cells with low CD38 and CD11c expression were expanded in rural living subjects. Together, our results demonstrate that, using a harmonized analytical pipeline and machine learning-based integration, large-scale mass cytometry data can be combined across diverse cohorts to identify shared immunological hallmarks of rural living. These signatures may provide targets for modulating immune responses in future vaccine studies.

immunology↗

Gut microbial metabolite imidazole propionate impairs endothelial cell function and promotes the development of atherosclerosis

BackgroundThe microbially generated amino acid-derived metabolite imidazole propionate (ImP) contributes to the pathogenesis of type 2 diabetes. However, the effect of ImP on endothelial cell physiology and its role in atherosclerotic coronary artery disease (CAD) is unknown. Using both human and animal model studies, we investigated the potential contributory role of ImP in the development of atherosclerosis. MethodsPlasma levels of ImP were measured in patients undergoing elective cardiac angiography (n = 831) by means of ultra high-performance liquid chromatography coupled to tandem mass spectrometry. Odds ratios (ORs) and corresponding 95% confidence intervals for CAD were calculated based on the ImP quartiles using both univariable and multivariable logistic regression models. Atheroprone apolipoprotein E-/- (Apoe-/-) mice fed a high-fat diet were additionally treated with ImP (800 {micro}g) or vehicle and aortic atherosclerotic lesion area was evaluated after 12 weeks. In a mouse model of carotid artery injury, the effect of ImP on vascular regeneration was examined. Using human aortic endothelial cells (HAECs) the effect of ImP on functional properties of endothelial cells were assessed. Next-generation sequencing, western blot analysis, siRNA-based gene knockdown and tamoxifen-inducible Cre-loxP experiments were performed to investigate ImP-mediated molecular mechanisms. ResultsPlasma ImP levels in subjects undergoing cardiac evaluation were associated with increased risk for prevalent CAD. In atheroprone Apoe-/- mice ImP increased atherosclerotic lesion size. We found that ImP dose-dependently impaired migratory and angiogenic properties of human endothelial cells, and promoted an increased inflammatory response. Long-term exposure to ImP impaired the repair potential of the endothelium after an arterial insult. Mechanistically, ImP attenuated insulin receptor signaling by suppressing PI3K/AKT pathway leading to the sustained activation of the forkhead box protein O1 (FOXO1) transcription factor. Genetic inactivation of endothelial FOXO1 signaling in ImP-treated mice enhanced the angiogenic activity and preserved the vascular repair capacity of endothelial cells after carotid injury. ConclusionsOur findings reveal a hitherto unknown role of the microbially produced histidine-derived metabolite ImP in endothelial dysfunction and atherosclerosis, suggesting that ImP metabolism is a potential therapeutic target in atherosclerotic cardiovascular disease.

systems biology↗

Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury

Multiscale modeling requires the coupling of models on different scales, often based on different mathematical approaches and developed by different research teams. This poses many challenges, such as defining interfaces for coupling, reproducible exchange of submodels, efficient simulation of the models, or reproducibility of results. Here, we present a multiscale digital twin of the liver that couples a partial differential equation (PDE)-based porous media approach for the hepatic lobule with cellular-scale ordinary differential equation (ODE)-based models. The models based on the theory of porous media describe transport, tissue mechanical properties, and deformations at the lobular scale, while the cellular models describe hepatic metabolism in terms of drug metabolism and damage in terms of necrosis. The resulting multiscale model of the liver was used to simulate perfusion-zonation-function relationships in the liver spanning scales from single cell to the lobulus. The model was applied to study the effects of (i) protein zonation patterns (metabolic zonation) and (ii) drug concentration dependence on spatially heterogeneous liver damage in the form of necrosis. Depending on the zonation pattern, different liver damage patterns could be reproduced, including periportal and pericentral necrosis as seen in drug-induced liver injury (DILI). Increasing the drug concentration led to an increase in the observed damage pattern. A key point for the success was the integration of domain-specific simulators based on standard exchange formats, i.e., libroadrunner for the high-performance simulation of ODE-based systems and FEBio for the simulation of the continuum-biomechanical part. This allows a standardized and reproducible exchange of cellular scale models in the Systems Biology Markup Language (SBML) between research groups.

pharmacology and toxicology↗

Cross-Species Variability in Lobular Geometry and Cytochrome P450 Hepatic Zonation: Insights into CYP1A2, CYP2E1, CYP2D6 and CYP3A4

This study explores the critical interplay between lobular geometry and the zonated distribution of cytochrome P450 (CYP) enzymes across species. We present an innovative approach to assess lobular geometry and zonation patterns using whole slide imaging (WSI). This method allows a detailed, systematic comparison of lobular structures and spatial distribution of key CYP450 enzymes and glutamine synthetase in four different species (mouse, rat, pig, and human). Our results shed light on species differences in lobular geometry and enzymatic zonation, providing critical insights for drug metabolism research. Based on our approach we could determine the minimum number of lobules required for a statistically representative analysis, an important piece of information when evaluating liver biopsies and deriving information from WSI.

pathology↗

Insights into Intestinal P-glycoprotein Function using Talinolol: A PBPK Modeling Approach

Talinolol is a cardioselective beta-blocker that was previously used to treat heart failure and myocardial infarction. Following the development of new, more effective beta-blockers with better study results, talinolol is now only used clinically for the treatment of arterial hypertension. In basic science, talinolol continues to be used as a test substance due to its pharmacokinetics. Its intestinal absorption is determined by uptake by the organic anion transporting polypeptide 2B1 (OATP2B1) and efflux via P-glycoprotein (P-gp). Talinolol can be taken up via OATP1B1 in the liver, where it enters the enterohepatic circulation. Talinolol is excreted unchanged in the urine and feces. Talinolol is widely used as a probe drug for the intestinal efflux transporter P-gp, which plays a critical role in protecting against potentially toxic substances and facilitating the elimination of xenobiotics. In this work, an extensive database of talinolol pharmacokinetics was established and used to develop and validate a physiologically based pharmacokinetic (PBPK) model of talinolol for P-gp phenotyping. The model was used to investigate the influence of several factors on talinolol pharmacokinetics: (i) inhibition of P-gp via drug-drug interaction; (i) genetic polymorphisms of P-gp; (iii) activity of OATP2B1 and OATP1B1; (iv) effect of comorbidity, namely hepatic and renal impairment; and (v) site-specific distribution of P-gp and OATP2B1 in the intestine. The model accurately predicts the concentration-time profile of talinolol after oral or intravenous administration of single and multiple dosing. Furthermore, the model accurately describes the effect of genetic variants of P-gp on the pharmacokinetics of talinolol, the effect of inhibition of P-gp, the effect of renal impairment, as well as site-specific infusion of talinolol in the intestine. The detailed description of the intestinal absorption of talinolol and the predictions of talinolol pharmacokinetics as a function of hepatorenal impairment provide valuable clinical insights for metabolic phenotyping with talinolol. Both the model and the database are freely available for reuse.

systems biology↗

A physiologically based pharmacokinetic model for CYP2E1 phenotyping via chlorzoxazone

The cytochrome P450 (CYP) superfamily of enzymes plays a critical role in the metabolism of drugs, toxins, and endogenous and exogenous compounds. The activity of CYP enzymes can be influenced by a variety of factors, including genetics, diet, age, environmental factors, and disease. Among the major isoforms, CYP2E1 is of particular interest due to its involvement in the metabolism of various low molecular weight chemicals, including alcohols, pharmaceuticals, industrial solvents, and halogenated anesthetics. Metabolic phenotyping of CYPs based on the elimination of test compounds is a useful method for assessing in vivo activity, with chlorzoxazone being the primary probe drug for phenotyping of CYP2E1. The aim of this work was to investigate the effect of changes in CYP2E1 level and activity, ethanol consumption, ethanol abstinence, and liver impairment on the results of metabolic phenotyping with chlorzoxazone. To accomplish this, an extensive pharmacokinetic dataset for chlorzoxazone was established and a physiologically based pharmacokinetic (PBPK) model of chlorzoxazone and its metabolites, 6-hydroxychlorzoxazone and chlorzoxazone-O-glucuronide, was developed and validated. The model incorporates the effect of ethanol consumption on CYP2E1 levels and activity by extending the model with a core ethanol pharmacokinetic model and a CYP2E1 turnover model. The model accurately predicts pharmacokinetic data from several clinical studies and is able to estimate the effect of changes in CYP2E1 levels and activity on chlorzoxazone pharmacokinetics. Regular ethanol consumption induces CYP2E1 over two to three weeks, resulting in increased conversion of chlorzoxazone to 6-hydroxychlorzoxazone and a higher 6-hydroxychlorzoxazone/chlorzoxazone metabolic ratio. After ethanol withdrawal, CYP2E1 levels return to baseline within one week. Importantly, liver impairment has an opposite effect, resulting in reduced liver function via CYP2E1. In alcoholics with liver impairment who also consume ethanol, these factors will have opposite confounding effects on metabolic phenotyping with chlorzoxazone.

systems biology↗

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

The cytochrome P450 2D6 (CYP2D6) is a key xenobiotic-metabolizing enzyme involved in the clearance of many drugs. Genetic polymorphisms in CYP2D6 contribute to the large inter-individual variability in drug metabolism and could affect metabolic phenotyping of CYP2D6 probe substances such as dextromethorphan (DXM). To study this question, we (i) established an extensive pharmacokinetics dataset for DXM; and (ii) developed and validated a physiologically based pharmacokinetic (PBPK) model of DXM and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) based on the data. Drug-gene interactions (DGI) were introduced by accounting for changes in CYP2D6 enzyme kinetics depending on activity score (AS), which in combination with AS for individual polymorphisms allowed us to model CYP2D6 gene variants. Variability in CYP3A4 and CYP2D6 activity was modeled based on in vitro data from human liver microsomes. Model predictions are in very good agreement with pharmacokinetics data for CYP2D6 polymorphisms, CYP2D6 activity as described by the AS system, and CYP2D6 metabolic phenotypes (UM, EM, IM, PM). The model was applied to investigate the genotype-phenotype association and the role of CYP2D6 polymorphisms for metabolic phenotyping using the urinary cumulative metabolic ratio (UCMR), DXM/(DXO+DXO-Glu). The effect of parameters on UCMR was studied via sensitivity analysis. Model predictions indicate very good robustness against the intervention protocol (i.e. application form, dosing amount, dissolution rate, and sampling time) and good robustness against physiological variation. The model is capable of estimating the UCMR dispersion within and across populations depending on activity scores. Moreover, the distribution of UCMR and the risk of genotype-phenotype mismatch could be estimated for populations with known CYP2D6 genotype frequencies. The model can be applied for individual prediction of UCMR and metabolic phenotype based on CYP2D6 genotype. Both, model and database are freely available for reuse.

pharmacology and toxicology↗

Physiologically based modeling of the effect of physiological and anthropometric variability on indocyanine green based liver function tests

Accurate evaluation of liver function is a central task in hepatology. Dynamic liver function tests (DLFT) based on the time-dependent elimination of a test substance provide an important tool for such a functional assessment. These tests are used in the diagnosis and monitoring of liver disease as well as in the planning of hepatobiliary surgery. A key challenge in the evaluation of liver function with DLFTs is the large inter-individual variability. Indocyanine green (ICG) is a widely applied test compound used for the evaluation of liver function. After an intravenous administration, pharmacokinetic (PK) parameters are calculated from the plasma disappearance curve of ICG which provide an estimate of liver function. The hepatic elimination of ICG is affected by physiological factors such as hepatic blood flow or binding of ICG to plasma proteins, anthropometric factors such as body weight, age, and sex, or the protein amount of the organic anion-transporting polypeptide 1B3 (OATP1B3) mediating the hepatic uptake of ICG. Being able to account for and better understand these various sources of inter-individual variability would allow to improve the power of ICG based DLFTs and move towards an individualized evaluation of liver function. Within this work we systematically analyzed the effect of various factors on ICG elimination by the means of computational modeling. For the analysis, a recently developed and validated physiologically based pharmacokinetics (PBPK) model of ICG distribution and hepatic elimination was utilized. Key results are (i) a systematic analysis of the variability in ICG elimination due to hepatic blood flow, cardiac output, OATP1B3 abundance, liver volume, body weight and plasma bilirubin level; (ii) the evaluation of the inter-individual variability in ICG elimination via a large in silico cohort of n=100000 subjects based on the NHANES cohort with special focus on stratification by age, sex, and body weight; (iii) the evaluation of the effect of various degrees of cirrhosis on variability in ICG elimination. The presented results are an important step towards individualizing liver function tests by elucidating the effects of confounding physiological and anthropometric parameters in the evaluation of liver function via ICG.

systems biology↗

Pharmacokinetics of caffeine: A systematic analysis of reported data for application in metabolic phenotyping and liver function testing

Caffeine is by far the most ubiquitous psychostimulant worldwide found in tea, coffee, cocoa, energy drinks, and many other beverages and food. Caffeine is almost exclusively metabolized in the liver by the cytochrome P-450 enzyme system to the main product paraxanthine and the additional products theobromine and theophylline. Besides its stimulating properties, two important applications of caffeine are metabolic phenotyping of cytochrome P450 1A2 (CYP1A2) and liver function testing. An open challenge in this context is to identify underlying causes of the large inter-individual variability in caffeine pharmacokinetics. Data is urgently needed to understand and quantify confounding factors such as lifestyle (e.g. smoking), the effects of drug-caffeine interactions (e.g. medication metabolized via CYP1A2), and the effect of disease. Here we report the first integrative and systematic analysis of data on caffeine pharmacokinetics from 148 publications and provide a comprehensive high-quality data set on the pharmacokinetics of caffeine, caffeine metabolites, and their metabolic ratios in human adults. The data set is enriched by meta-data on the characteristics of studied patient cohorts and subjects (e.g. age, body weight, smoking status, health status), the applied interventions (e.g. dosing, substance, route of application), measured pharmacokinetic time-courses, and pharmacokinetic parameters (e.g. clearance, half-life, area under the curve). We demonstrate via multiple applications how the data set can be used to solidify existing knowledge and gain new insights relevant for metabolic phenotyping and liver function testing based on caffeine. Specifically, we analyzed (i) the alteration of caffeine pharmacokinetics with smoking and use of oral contraceptives; (ii) drug-drug interactions with caffeine as possible confounding factors of caffeine pharmacokinetics or source of adverse effects; (iii) alteration of caffeine pharmacokinetics in disease; and (iv) the applicability of caffeine as a salivary test substance by comparison of plasma and saliva data. In conclusion, our data set and analyses provide important resources which could enable more accurate caffeine-based metabolic phenotyping and liver function testing.

pharmacology and toxicology↗

Prediction of survival after hepatectomy using a physiologically based pharmacokinetic model of indocyanine green liver function tests

The evaluation of hepatic function and functional capacity of the liver are essential tasks in hepatology as well as in hepatobiliary surgery. Indocyanine green (ICG) is a widely applied test compound that is used in clinical routine to evaluate hepatic function. Important questions for the functional evaluation with ICG in the context of hepatectomy are how liver disease such as cirrhosis alters ICG elimination, and if postoperative survival can be predicted from preoperative ICG measurements. Within this work a physiologically based pharmacokinetic (PBPK) model of ICG was developed and applied to the prediction of the effects of a liver resection under various degrees of cirrhosis. For the parametrization of the computational model and validation of model predictions a database of ICG pharmacokinetic data was established. The model was applied (i) to study the effect of liver cirrhosis and liver resection on ICG pharmacokinetics; and (ii) to evaluate the model-based prediction of postoperative ICG-R15 as a measure for postoperative outcome. Key results are the accurate prediction of changes in ICG pharmacokinetics caused by liver cirrhosis and postoperative changes of ICG-elimination after liver resection, as validated with a wide range of data sets. Based on the PBPK model, individual survival after liver resection could be classified, demonstrating its potential value as a clinical tool.

physiology↗

Polymerization of misfolded protein lowers CX3CR1 expression in human PBMCs

The CX3CR1 (chemokine (C-X3-C motif) receptor 1) expression levels on immune cells have significant importance in maintaining tissue homeostasis under physiological and pathological conditions. The factors implicated in the regulation of CX3CR1 and its specific ligand CX3CL1 (fractalkine) expression remain largely unknown. Recent studies provide evidence that host s misfolded proteins occurring in the forms of polymers or amyloid fibrils can regulate CX3CR1 expression. Herein, we present a novel example that polymers of human ZZ alpha1-antitrypsin (Z-AAT) protein, resulting from its conformational misfolding due to the Z (Glu342Lys) mutation in SERPINA1 gene, strongly lower CX3CR1 expression in human PBMCs. We also show that extracellular polymers of Z-AAT are internalized by PBMCs, which parallels with increased intracellular levels of CX3CR1 protein. Our findings support the role of extracellular misfolded proteins in CX3CR1 regulation and encourage conducting further studies on this issue.

cell biology↗

Executable Simulation Model of the Liver

To address the issue of reproducibility in computational modeling we developed the concept of an executable simulation model (EXSIMO). An EXSIMO combines model, data and code with the execution environment to run the computational analysis in an automated manner using tools from software engineering. Key components are i) models, data and code for the computational analysis; ii) tests for models, data and code; and iii) an automation layer to run tests and execute the analysis. An EXSIMO combines version control, model, data, units, annotations, analysis, reports, execution environment, testing, continuous integration and release. We applied the concept to perform a replication study of a computational analysis of hepatic glucose metabolism in the liver. The corresponding EXSIMO is available from https://github.com/matthiaskoenig/exsimo.

systems biology↗

PK-DB: PharmacoKinetics DataBase for Individualized and Stratified Computational Modeling

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials including pre-clinical research. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) measured pharmacokinetic time-courses; (iv) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve). Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/DB), or population pharmacokinetic (pop PK) modeling.

bioinformatics↗

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗

Dynamic Flux Balance Analysis Models in SBML

Computational models in systems biology and systems medicine are typically simulated using a single formalism such as ordinary differential equations (ODE). However, more complex models require the coupling of multiple formalisms since different biological phenomena are better described by different methods. For example, metabolism in steady state is often modeled using flux-balance analysis (FBA) whereas dynamic changes of model components are better described via ODEs. The coupling of FBA and ODE modeling formalisms results in dynamic FBA models. A major challenge is how to describe such hybrid models that couple multiple formalisms in a standardized way so that they can be exchanged between tools and simulated consistently in a reproducible manner. This paper presents a scheme for encoding and implementation of dynamic FBA models in the Systems Biology Markup Language (SBML), thereby enabling the exchange of multi-framework computational models between software tools. We demonstrate the feasibility of the approach using various example models and show that different tools are able to simulate the hybrid models and agree on the results. As part of this work, two independent implementations of a multi-framework simulation method for dynamic FBA have been developed supporting such models: iBioSim and sbmiutils.

bioinformatics↗