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47 Publications visible to you, out of a total of 55
Review of Key Elements in Developing a Common Data Model for Rare Diseases: Identifying Common Success Factors.

Abstract (Expand)

This paper explores key success factors for the development and implementation of a Common Data Model (CDM) for Rare Diseases (RDs) focusing on the European context. Several challenges hinder RD care … and research in diagnosis, treatment, and research, including data fragmentation, lack of standardisation, and Interoperability (IOP) issues within healthcare information systems. We identify key issues and recommendations for an RD-CDM, drawing on international guidelines and existing infrastructure, to address organisational, consensus, interoperability, usage, and secondary use challenges. Based on these, we analyse the importance of balancing the scope and IOP of a CDM to cater to the unique requirements of RDs while ensuring effective data exchange and usage across systems. In conclusion, a well-designed RD-CDM can bridge gaps in RD care and research, enhance patient care and facilitate international collaborations.

Authors: A. S. L. Graefe, F. Rehburg, M. Hubner, S. Thun, O. Beyan

Date Published: 22nd Aug 2024

Publication Type: Proceedings

A liver digital twin for in silico testing of cellular and inter-cellular mechanisms in regeneration after drug-induced damage

Abstract

Not specified

Authors: Jieling Zhao, Ahmed Ghallab, Reham Hassan, Steven Dooley, Jan Georg Hengstler, Dirk Drasdo

Date Published: 1st Feb 2024

Publication Type: Journal

Gene regulatory network inference in the era of single-cell multi-omics

Abstract

Not specified

Authors: Pau Badia-i-Mompel, Lorna Wessels, Sophia Müller-Dott, Rémi Trimbour, Ricardo O. Ramirez Flores, Ricard Argelaguet, Julio Saez-Rodriguez

Date Published: 1st Nov 2023

Publication Type: Journal

Metabolome plasticity in 241 Arabidopsis thaliana accessions reveals evolutionary cold adaptation processes.

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Acclimation and adaptation of metabolism to a changing environment are key processes for plant survival and reproductive success. In the present study, 241 natural accessions of Arabidopsis (Arabidopsis … thaliana) were grown under two different temperature regimes, 16 degrees C and 6 degrees C, and growth parameters were recorded, together with metabolite profiles, to investigate the natural genome x environment effects on metabolome variation. The plasticity of metabolism, which was captured by metabolic distance measures, varied considerably between accessions. Both relative growth rates and metabolic distances were predictable by the underlying natural genetic variation of accessions. Applying machine learning methods, climatic variables of the original growth habitats were tested for their predictive power of natural metabolic variation among accessions. We found specifically habitat temperature during the first quarter of the year to be the best predictor of the plasticity of primary metabolism, indicating habitat temperature as the causal driver of evolutionary cold adaptation processes. Analyses of epigenome- and genome-wide associations revealed accession-specific differential DNA-methylation levels as potentially linked to the metabolome and identified FUMARASE2 as strongly associated with cold adaptation in Arabidopsis accessions. These findings were supported by calculations of the biochemical Jacobian matrix based on variance and covariance of metabolomics data, which revealed that growth under low temperatures most substantially affects the accession-specific plasticity of fumarate and sugar metabolism. Our findings indicate that the plasticity of metabolic regulation is predictable from the genome and epigenome and driven evolutionarily by Arabidopsis growth habitats.

Authors: J. Weiszmann, D. Walther, P. Clauw, G. Back, J. Gunis, I. Reichardt, S. Koemeda, J. Jez, M. Nordborg, J. Schwarzerova, I. Pierides, T. Nagele, W. Weckwerth

Date Published: 22nd Sep 2023

Publication Type: Journal

EnzymeML: seamless data flow and modeling of enzymatic data.

Abstract (Expand)

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, … reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .

Authors: S. Lauterbach, H. Dienhart, J. Range, S. Malzacher, J. D. Sporing, D. Rother, M. F. Pinto, P. Martins, C. E. Lagerman, A. S. Bommarius, A. V. Host, J. M. Woodley, S. Ngubane, T. Kudanga, F. T. Bergmann, J. M. Rohwer, D. Iglezakis, A. Weidemann, U. Wittig, C. Kettner, N. Swainston, S. Schnell, J. Pleiss

Date Published: 10th Feb 2023

Publication Type: Journal

Evaluation of Pharmacokinetic and Toxicological Parameters of Arnica Tincture after Dermal Application In Vivo

Abstract (Expand)

Cutaneous leishmaniasis (CL) is classified as a neglected tropical disease by the World Health Organization. As the standard drugs for the treatment of this disease suffer from severe unwanted effects, …anted effects, new effective and safe therapeutic options are required. In our previous work, Arnica tincture showed promising antileishmanial effects in vitro and in vivo. For the potential treatment of human CL patients with Arnica tincture, data on the pharmacokinetic properties of the bioactive, antileishmanial compounds (the sesquiterpene lactone (STL) helenalin and its derivatives) are needed. Therefore, we studied the in vivo absorption of the bioactive compounds after the dermal application of Arnica tincture in rats. Moreover, we analyzed the blood plasma, urine, and feces of the animals by ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). Although the majority (84%) of the applied STLs (1.0 mg) were absorbed, the concentrations in the plasma, urine, and feces were below the limit of detection (0.3 ng/mL) in the samples for UHPLC-HRMS analysis. This result may be explained by extensive metabolism and slow permeation accompanied by the accumulation of STLs in the skin, as described in our previous work. Accordingly, the plasma concentration of STLs after the topical application of Arnica tincture was very far from a dose where toxicity could be expected. Additionally, tests for corrosive or irritant activity as well as acute and repeated-dose dermal toxicity did not show any positive results after the administration of the amounts of Arnica tincture that would be needed for the treatment of CL. Consequently, in the treatment of CL patients with Arnica tincture, no toxic effects are expected, other than the known sensitization potential of the STLs.

Authors: Franziska M. Jürgens, Sara M. Robledo, Thomas J. Schmidt

Date Published: 1st Nov 2022

Publication Type: Journal

Guided interactive image segmentation using machine learning and color-based image set clustering

Abstract (Expand)

Abstract Motivation Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic …The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.e. the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only a few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited. Results We present a novel approach that combines machine learning-based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general. Availability and implementation The presented methods are implemented in our image processing software TiQuant which is freely available at tiquant.hoehme.com. Supplementary information Supplementary data are available at Bioinformatics online.

Authors: Adrian Friebel, Tim Johann, Dirk Drasdo, Stefan Hoehme

Date Published: 1st Oct 2022

Publication Type: Journal

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