Independent Projects

Chronic kidney disease (CKD) affects around 9% of the global population and is the 12th leading cause of death (GBD Chronic Kidney Disease Collaboration 2020). Diabetic kidney disease (DKD) is the most common form of CKD, responsible for nearly half of all end-stage renal disease (ESRD) cases (Samsu 2021). Despite its high burden, progress in developing effective therapies has been slow, largely due to limited understanding of the underlying molecular mechanisms.

DKD is driven by complex and ...

Cure-Q demo project to demonstrate how Fairdomseek works

AIRC

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Produktion von Ethylacetat aus Molkerückständen mit Kluyveromyces marxianus und Produktgewinnung mittels Membranverfahren

MS-Proteomics and Lipidomics raw data associate with the present research paper

MS-Proteomics raw data repository

Test project

Komplexe, erweiterte Leberresektionen ((e)PHx) stellen ein hohes Risiko für ein postoperatives Leberversagen dar, insbesondere bei einer bereits bestehenden Lebererkrankung. Das Ziel von QuaLiPerF ist es, bisher unbekannte Wechselwirkungen zwischen den durch die Operation verursachten Veränderungen der Perfusion und der Stoffwechselfunktion aufzuklären. Wir werden einen systemmedizinischen Ansatz anwenden, um ein mehrskaliges Modell der Leberperfusion und -funktion zu erstellen, das die Vorhersage ...

A fundamental and enduring challenge for ecosystem managers developing risk and impact assessment tools is to predict the extent of impact that spilled oil can have on the immediate fish populations and on future generations. Modelling approaches are important tools for these assessments, but they currently suffer from two major shortcomings: 1) the failure to acknowledge that effects can be transferred through several generations and 2) the assumption that a small group of toxic compounds, ...

HubMOL is a Marie Skłodowska-Curie Actions Doctoral Network funded by Horizon Europe.

The hub molecules of life comprise a set of cofactors that are crucial for all cellular functions. They are well known for their central roles in both metabolism and signalling. Still, our limited knowledge of the crosstalk and the reciprocal connectivity between key metabolic and signal transduction pathways and the dynamic interplay of hub molecules is currently hampering their exploitation. Imbalances in hub ...

As a scientists-founded and scientists-driven leading biotechnological company, Creative Biogene focuses on developing high quality products and services, as well as proprietary technology to support the research in the field of basic life sciences research, biomedical development, industrial synthetic application and preclinical drug discovery.

This project provides datasets supporting the analysis of carbon partitioning and metabolite excretion in different Synechocystis sp. PCC 6803 strains. It includes intracellular and extracellular metabolite measurements, glycogen quantification, and sampling metadata, enabling investigation of carbon overflow and glycogen metabolism under different nitrogen conditions.

Isogenic cell lines neuronal activity in cureQ

CureQ demonstration investigation in FairdomHub

MEA data experiments test demo

Patients subjected to extended liver resection ((e)PHx) have a high risk to experience postoperative liver dysfunction or even liver failure, especially in case of preexisting liver disease. Our research group QuaLiPerF aims to enhance the understanding of the quantitative and spatial relationship between hepatic perfusion and function in healthy, steatotic and regenerating livers after surgery using a systems medicine approach. This project will focus on assessing the impact of perfusion ...

Within the overarching goal of the research unit QuaLiPerF to gain a data-integrated, multi-scale understanding of the interplay between hepatic perfusion and function and its recovery after extended hepatectomy (ePHx), this subproject (P6) is devoted to experimental MRI investigations in rats. This subproject aims to establish a comprehensive MR imaging toolbox for small animal imaging at 9.4T and to provide MRI-based spatially resolved, multi-parametric quantitative in vivo information on liver ...

The BENEFIT project is an interdisciplinary eHealth initiative to offer long-term lifestyle support for patients with cardiovascular disease (CVD). Data collected from BENEFIT patients is heterogeneous and encompasses demographics, observational lifestyle data, self-reported motivation for lifestyle change and physical measurements. Analysing these results can provide new insights into the effects of different intervention strategies used in the cardiac rehabilitation programme on maintaining a ...

Genome-wide association studies with ampicillin resistance as phenotype were conducted on based on variant-call data. A linear regression was performed for MICs and logistic regression was performed based on clinical breakpoint concentrations.

Investigation of the regulation of different PGM variants from Synechocystis sp. PCC 6803

Current chemical concept recognition tools have demonstrated significantly lower performance for in full-text articles than in abstracts. Improving automated full-text chemical concept recognition can substantially accelerate manual indexing and curation and advance downstream NLP tasks such as relevant article retrieval. Participating in BioCreative Track NLM-Chem we focus identifying chemicals in full-text articles (i.e. named entity recognition and normalization).

This investigation serves as supplementary material for a SWAT4HCLS publication that describes minimum metadata and provenance requirements for reproducible enrichment analysis results.

Functional enrichment analysis is an essential downstream process in high throughput omics studies, such as transcriptomics and proteomics. By using the Gene Ontology (GO) and its annotations (GOA), underlying functional patterns of over-representation can be identified, leading to ...

Aims: The immune response is important for mediating the benefit of cardiac cell therapies. The role of varied immune responses in influencing the outcome of cardiomyocyte cell transplantation after myocardial infarction was investigated. Methods and Results: Cardiac flow cytometric analysis of C57BL/6J and T- and B cell deficient Rag2del mice revealed varied CD11b, natural killer and dendritic cell responses following sham injection and a disparate macrophage response after myocardial infarction. ...

Members of the genus Aromatoleum are cosmopolitan in diverse habitats and utilize a broad range of recalcitrant organic molecules coupled to denitrification or O2-respiration. To gain a holistic understanding of the model organism A. aromaticum EbN1T, we here studied its catabolic network dynamics in response to 3-(4-hydroxyphenyl)propanoate, phenylalanine, 3-hydroxybenzoate, benzoate and acetate utilized under nitrate-reducing vs. oxic conditions. Multi-OMICS (transcriptome, proteome and metabolome) ...

Huntington MEA data for CureQ

Demo for multi electrode array study in the cureQ project

todo ...

Fat quantification of 5 liver lobes (RML, LML, LLL, RL, CL) During the MRI sesssion at least one PRESS voxel was placed in each of those liver lobes to acquire PRESS data for later fat analysis. Acquisitions were without water suppression, so the fat peaks are relatively small.

The quantification was performed using lcmodel. The original MRI data are part of the MRI session assays (data files uploaded). Here you can find the additional analysis and the output of lcmodel: The "ps" in each exam ...

animals underwent a 60% PVL surgery and this MRI session was performed on post operation day 5 (POD5).

animals underwent a 60% PVL surgery and this MRI session was performed on post operation day 3 (POD3).

animals underwent a 60% PVL surgery and this MRI session was performed on post operation day 2 (POD2).

animals underwent a 60% PVL surgery and this MRI session was performed on post operation day 1 (POD1).

rats under control diet and no surgery to establish a baseline for fucntion, perfusion and fat content using MRI.

Utilization of enzyme assays to investigate PGM variants from Synechocystis

Chemical named entity recognition (NER) is a significant pre-processing task in natural language processing. Identification and extraction of chemical entities from biomedical literature and entities linking to the knowledge base are essential steps for the chemical text-mining pipeline. However, the identification of chemical entities in a biomedical text is a challenging task due to the diverse morphology of chemical entities and the different types of chemical nomenclature. In this work, we ...

RobOKoD algorithm was, designed then implemented as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain. It was shown to perform better than similar methods (OptKnock, and RobustKnock).

OptKnock algorithm was used as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain.

RobustKnock algorithm was used as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain.

Training of the model, parameter estimation using Evolutionary Programming using metabolomics, proteomics and some flux data.

Validation by simulating independent OE, KO mutant and perturbation samples, using sampling of the gausian distribution based on the mean and SD of measurements per sample. A 1000 samples of the gausian distribution of the mean and SD was performed per sample to show error in the measurements and how it propegates in predicted metabolite concentration in SS

Comparison of Kcat values from the model and values from literature.

Protein copy number at 6h, 12h, 24h, 48h, 72h, 96h, average values and SD for the measurements

Metabolomics time series measurements for internal metabolites for 6h, 24h and 48h for multiple experiments. Largely based on MAss spectrometry, bioluminescence kits to measure NAD, NADH at 24h, other time points are infered from relative measurements times the absolute measurements at 24h.

Simulation of OE mutants targetting enzymes in the model, combined with metabolite concentrations and enzyme fold change of from the 40 samples. For each second mutant the enzyme concentrations in case of OE and KO mutants in updated and the metabolite concentrations of the second sample are loaded in the model. Using this approach the model approximately predicts combinatorial effects of OE mutations with other mutations, perturbations and time series concentrations.

Measurements of external metabolites based on growth curve data. Flux estimates for uptake of external metabolites such as glucose and production rates for external metabolites lactate and acetate

Construction and manual curated Genome Scale Metabolitic model of M. hyopneumoniae. Dynamic flux balance analysis was performed for glucose uptake

Contains the analysis of the internal metabolite concentrations of the 40 independend samples Pearson correlation was used to generate heatmaps Pearson correlation with p-value cutof of 0.001 was used and as input for a correlation network (grouping using H-clust) Principal component analysis was performed on samples, F-ion and H-ion data combined and seperately Zip files contains the data (FC.txt), PCA and heatmap plots and the script to re-generate these plots

Metabolic control analysis: Local control coefficients for 40 independent samples based on 100x sampling from the measurement distribution Global control analysis based on 100.000 Latin Hypercube sampling from the parameter search range (0.01-100 for Km values and 0.001-1000 for Vmax values)

The associated zip files contains all input files and a Jupyter notebook to rerun sampled simmulations, combined simmulations, parameter scan for the model with addition of an oxygin inhibiton of LDH, local- and global-sensitivity analysis and plot simmulation output in various formats. In addition the zip file contains the py36.yaml file that can be used to recreate the model simmulation environment using Anaconda making all simmulations completely reproducable. All information on how to use ...

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

The FEM models how metabolic slowdown will induce the age-related changes of weight gain, insulin resistance, basal inflammation, mitochondrial dysfunction, as well as the age-related disease of atherosclerosis, via a series of unavoidable homeostatic shifts.

Underlying R script for the investigation of immune cells. Script contains basic data processing, as well as a DE and monocle analysis.

Stoichiometric model in SBML format using the acetate-aerobic standard scenario.

Please note that SBML was exported using the sbmlwriter class of Metano. This file was not used for the actual analyses.

This stoichiometric model of Aromatoleum aromaticum EbN1 is a genome-scale model and comprises 655 enzyme-catalyzed reactions and 731 distinct metabolites.

The model is in the plain-text reaction format of Metano that is human-readable and can be opened with every text editor. To run this version of the model, please use the Metano Modeling Toolbox (mmtb.brenda-enzymes.org) and the associated scenario files.

A population of turtles have between 1 and 3 genes contributing to the strength of selective destruction, which can either cause ageing or allow for negligible senescence.

Model of selective destruction in a single population of cells with differing sensitivities for growth. Fast growing cells can be epigenetically converted to slower cells rather than simple cell death as in previous models.

Model of unselective destruction in a single population of cells with differing sensitivities for growth

Model of selective destruction in a single population of cells with differing sensitivities for growth

Here, we describe the index file generation of the mm10 genome, the genome alignment with kallisto, and quantification with bustools to obtain the used spliced / unspliced transcript input.

Here is the detailed R script to generate the input needed by scSynO for synthetic cell generation and classification model training.

The code that can be embedded into any other Seurat data processing workflow is:

cell_expression_target_cluster <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = "target_cluster_number")]) cell_expression_all_other_clusters <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = ...

Single nuclei transcriptomics data as .csv files from the Allen Brain atlas data set of mus musculus (https://celltypes.brain-map.org/) have been utilized as an input for scSynO. The underlying analysis is part of the manuscript entitled "Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling". Data anaylsis and visalizations were mainly generated with the Seurat R package (https://satijalab.org/seurat/archive/v3.2/spatial_vignette.html)

Dynamic model of glycolysis, pyruvate metabolism and NoxE. The model is parameterized by selecting the best out of 100 parameter set using Copasi's Genetic algorithm with 1000 itterations and 500 simmulatanious models.

The new GEM of S. coelicolor developed by Tjasa Kumelj, Snorre Sulheim, Alexander Wentzel and Eivind Almaas in 2017/2018

Core model with the addition of a NoxE reaction to regenerate NAD using O2. COPASI’s build in Evolutionary programming algorithm was used to estimate parameters using a maximum of 2000 generations with a population size of 100 models with value scaling as weights to train the 5 parameters of the NoxE reaction.

Butanol producing iNS142, redesigned using RobOKoD.

E.coli Core model, with additional reactions added to generate the beta-oxadation cycle. This is the basic model used in RobOKoD: microbial strain design for (over)production of target compounds (http://fairdomhub.org/publications/236).

SOP for the Control Group animals undergoing MRI

Detailed description of the methods applied for proteomic analyses of P. cordatum heat stress response

Detailed description of the methods applied for metabolomic analyses of P. cordatum heat stress response