Biology subjects

Grein, S.

Publications and source records attributed to Grein, S..

6 recordsLinked to original sources

bioRxiv2024

cOmicsArt - a customizable Omics Analysis and reporting tool

MotivationThe availability of bulk-omic data is steadily increasing, necessitating collaborative efforts between experimental and computational researchers. While software tools with graphical user interfaces (GUIs) enable rapid and interactive data assessment, they are limited to pre-implemented methods, often requiring transitions to custom code for further adjustments. However, most available tools lack GUI-independent reproducibility such as direct integration with R, resulting in very limited support for transition. ResultsWe introduce the customizable Omics Analysis and reporting tool - cOmicsArt. cOmicsArt aims to enhance collaboration through integration of GUI-based analysis with R. The GUI allows researchers to perform user-friendly exploratory and statistical analyses with interactive visualizations and automatic documentation. Downloadable R scripts and results ensure reproducibility and seamless integration with R, supporting both novice and experienced programmers by enabling easy customizations and serving as a foundation for more advanced analyses. This versatility also allows for usage in educational settings guiding students from GUI-based analysis to R Code. AvailabilitycOmicsArt is freely available at https://shiny.iaas.uni-bonn.de/cOmicsArt/ User documentation is available at https://icb-dcm.github.io/cOmicsArt/ Source code is available on GitHub https://github.com/ICB-DCM/cOmicsArt A docker image can be retrieved from https://hub.docker.com/r/pauljonasjost/comicsart/tags A snapshot upon publication can be found on Zenodo: https://zenodo.org/records/13740904 A screen recording of cOmicsArt is available at: https://www.youtube.com/watch?v=pTGjtIYQOakp Contactjan.hasenauer@uni-bonn.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2024

Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a non-linear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. Here, we propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO).

systems biology↗

bioRxiv2024

From Planning Stage To FAIR Data: A Practical Metadatasheet For Biomedical Scientists

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.

bioinformatics↗

bioRxiv2022

IntestLine: a Shiny-based application to map the rolled intestinal tissue onto a line

To allow the comprehensive histological analysis of the whole intestine in one image, the tissue is often rolled to a spiral before imaging. This Swiss-rolling technique facilitates robust experimental procedures, but it limits the possibilities to comprehend changes along the intestine. Here, we present IntestLine, a Shiny-based open-source application to map imaging data of intestinal tissues in spiral shape onto a line. The mapping of intestinal tissues improves the visualization of the whole intestine in both proximal-distal and serosa-luminal axis, and facilitates the observation of location-specific cell types and markers. In summary, IntestLine serves as a tool to visualize and characterize intestine in future imaging studies.

bioinformatics↗

bioRxiv2022

Secreted long non-coding RNAs Gadlor1 and Gadlor2 affect multiple cardiac cell types and aggravate cardiac remodeling during pressure overload

BackgroundPathological overload triggers maladaptive myocardial remodeling that leads to heart failure. Recent studies have shown that long non-coding RNAs (lncRNAs) regulate cardiac remodeling. This study investigates two recently discovered, secreted lncRNAs, Gadlor1 and Gadlor2 (Gadlor 1/2). MethodsWe generated compound Gadlor1/2 knock-out (KO) mice and compared their response to pressure overload by transverse aortic constriction (TAC) to that of wild-type (WT) littermates. Endothelial cells, fibroblasts and cardiomyocytes were isolated from the hearts of both genotypes after TAC and their transcriptome was investigated by RNA sequencing. Gadlor target proteins were identified by RNA antisense purification coupled with mass spectrometry (RAP-MS) in cardiomyocytes. In addition, we investigated the effects of cardiac overexpression of Gadlor1/2. ResultsGadlor1/2 are jointly upregulated in failing mouse hearts as well as in the myocardium of heart failure patients. Cardiac overexpression of Gadlor1 and Gadlor2 aggravated myocardial dysfunction and enhanced hypertrophic and fibrotic remodeling in mice exposed to pressure overload. Compound Gadlor1/2 KO mice, in turn, exerted markedly reduced myocardial hypertrophy, fibrosis and dysfunction, but more angiogenesis during short and long-standing pressure overload. Paradoxically, Gadlor1/2 KO mice suffered from sudden death during prolonged overload, possibly due to cardiac arrhythmia. Gadlor1 and Gadlor2, which are mainly expressed in endothelial cells (ECs) in the heart, where they inhibit pro-angiogenic gene-expression, are strongly secreted within extracellular vesicles (EVs). These EVs transfer Gadlor lncRNAs to cardiomyocytes, where they bind and activate calmodulin-dependent kinase II, induce pro-hypertrophic gene-expression and enhance calcium re-uptake into the sarcoplasmic reticulum. ConclusionGadlor1 and Gadlor2 are lncRNAs that are mainly enriched in EC-derived EVs and are jointly upregulated in mouse and human hearts during pathological overload. We reveal a crucial endothelial cell-cardiomyocyte crosstalk, which aims at restoring calcium homeostasis in cardiomyocytes during overload at the cost of aggravated hypertrophy and fibrosis.

systems biology↗

bioRxiv2022

Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks

Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical. Author summaryLarge-scale dynamical models are nowadays widely used for the analysis of complex processes and the integration of large-scale data sets. However, computational cost is often a bottleneck. Here, we propose a new gradient computation method that facilitates the parameterization of large-scale models based on steady-state measurements. The method can be combined with existing gradient computation methods for time-course measurements. Accordingly, it is an essential contribution to the environment of computationally efficient approaches for the study of large-scale screening and omics data, but not tailored to biological applications, and, therefore, also useful beyond the field of computational biology.

systems biology↗