A5050239570- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- Single-cell and spatial transcriptomics
- Microbial Metabolic Engineering and Bioproduction
- Computational Drug Discovery Methods
- Gene expression and cancer classification
- Nuclear reactor physics and engineering
- Melanoma and MAPK Pathways
- Cell Image Analysis Techniques
- Protein Structure and Dynamics
- Fault Detection and Control Systems
- Protein Tyrosine Phosphatases
- Process Optimization and Integration
- Viral Infectious Diseases and Gene Expression in Insects
- Drug-Induced Hepatotoxicity and Protection
- Control Systems and Identification
- Metabolomics and Mass Spectrometry Studies
- Advanced Thermodynamics and Statistical Mechanics
- Advanced Control Systems Optimization
- Heat shock proteins research
- Gaussian Processes and Bayesian Inference
- Cancer Immunotherapy and Biomarkers
- Nuclear and radioactivity studies
- Microtubule and mitosis dynamics
- Nuclear Engineering Thermal-Hydraulics
University Hospital Heidelberg
2019-2024
Heidelberg University
2019-2024
RWTH Aachen University
2018-2021
Joint Research Centre
2018-2021
Universidade de Vigo
2014-2016
Consejo Superior de Investigaciones Científicas
2013-2015
Instituto de Investigacións Mariñas
2015
University of Pannonia
2012
Hungarian Academy of Sciences
2009-2011
Institute for Computer Science and Control
2009
Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, intercellular communication scarce, scattered across resources, not linked intracellular processes. To address this gap, we combined over 100 resources covering interactions roles proteins inter- signaling, as well transcriptional post-transcriptional regulation. We added protein...
Abstract The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any omics data, dozens to thousands measured markers. MISTy builds multiple views focusing on different or functional contexts dissect effects. evaluated in silico breast cancer datasets by imaging mass cytometry transcriptomics. estimated structural...
Dynamic modelling provides a systematic framework to understand function in biological systems. Parameter estimation nonlinear dynamic models remains very challenging inverse problem due its nonconvexity and ill-conditioning. Associated issues like overfitting local solutions are usually not properly addressed the systems biology literature despite their importance. Here we present method for robust efficient parameter which uses two main strategies surmount aforementioned difficulties: (i)...
Many problems of interest in dynamic modeling and control biological systems can be posed as non-linear optimization subject to algebraic constraints. In the context modeling, this is case of, e.g. parameter estimation, optimal experimental design flux balance analysis. control, model-based metabolic engineering or drug dose formulated (multi-objective) problems. Finding a solution those very challenging task which requires advanced numerical methods.This work presents AMIGO2 toolbox: first...
Kinetic models of biochemical systems usually consist ordinary differential equations that have many unknown parameters. Some these parameters are often practically unidentifiable, is, their values cannot be uniquely determined from the available data. Possible causes lack influence on measured outputs, interdependence among parameters, and poor data quality. Uncorrelated can seen as key tuning knobs a predictive model. Therefore, before attempting to perform parameter estimation (model...
One goal of precision medicine is to tailor effective treatments patients' specific molecular markers disease. Here, we used mass cytometry characterize the single-cell signaling landscapes 62 breast cancer cell lines and five from healthy tissue. We quantified 34 in each line upon stimulation by growth factor EGF presence or absence kinase inhibitors. These data—on more than 80 million single cells 4,000 conditions—were fit mechanistic network models that provide insight into how process...
Abstract 5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe lead to interruption cancer treatments. However, there limited understanding molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models human colon (SI) were characterize 5-FU transcriptomic metabolomic responses. Clinically relevant concentrations for vitro testing...
Abstract Summary Multiple databases provide valuable information about curated pathways and other resources that can be used to build analyze networks. OmniPath combines 61 (and continuously growing) network into a comprehensive collection, with over 120 000 interactions. We present here the App, Cytoscape plugin flexibly import data from via simple intuitive interface. Thus, it makes possible directly access large body of high-quality knowledge provided by within for inspection further use...
The molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large datasets is increasing steadily. A useful way extract mechanistic insight from data integrating them with a prior knowledge network signalling obtain dynamic models. CellNOpt collection Bioconductor R packages for building logic models perturbation networks. We have recently developed new components refined existing ones keep up...
Transcriptomics is widely used to assess the state of biological systems. There are many tools for different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined impact method choices on expression results, little attention has been paid their effects further downstream functional analysis, which typically provides basis interpretation follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow...
Abstract The advancement of technologies to measure highly multiplexed spatial data requires the development scalable methods that can leverage information. We present MISTy, a flexible, and explainable machine learning framework for extracting interactions from any omics data. MISTy builds multiple views focusing on different or functional contexts dissect effects, such as those direct neighbours versus distant cells. be applied spatially resolved with dozens thousands markers, without need...
Drug-induced liver injury (DILI) is the most prevalent adversity encountered in drug development and clinical settings leading to urgent needs understand underlying mechanisms. In this study, we have systematically investigated dynamics of activation cellular stress response pathways cell death outcomes upon exposure a panel toxicants using live imaging fluorescent reporter lines. We established comprehensive temporal dynamic profile large set BAC-GFP HepG2 lines representing following...
Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of cells tissue origin is lost. Conversely, RNA-seq designed to maintain cell localization have limited throughput and gene coverage. Mapping genes with information increases coverage while providing location. However, methods perform such mapping not yet been benchmarked. To fill this gap, we organized DREAM Single-Cell...
Acquiring a functional comprehension of the deregulation cell signaling networks in disease allows progress development new therapies and drugs. Computational models are becoming increasingly popular as systematic tool to analyze functioning complex biochemical networks, such those involved signaling. CellNOpt is framework build predictive logic-based pathways by training prior knowledge network data obtained from perturbation experiments. This can be formulated an optimization problem that...
Abstract Molecular knowledge of biological processes is a cornerstone in the analysis omics data. Applied to single-cell data, such analyses can provide mechanistic insights into individual cells and their interactions. However, intercellular communication scarce, scattered across different resources, not linked intracellular processes. To address this gap, we combined over 100 resources single database. It covers interactions roles proteins inter- signal transduction, as well...
Recent technological developments allow us to measure the status of dozens proteins in individual cells. This opens way understand heterogeneity complex multi-signaling networks across cells and cell types, with important implications treat diseases such as cancer. These technologies are, however, limited for which antibodies are available fairly costly, making predictions new markers existing under conditions a valuable alternative. To assess our capacity make boost further methodological...
In this paper, a model reduction procedure is proposed for the simplification of biochemical reaction network models. The approach capable reducing ODE models where right hand side equations contains polynomial and/or rational function terms. method based on finite number mixed integer quadratic programming (MIQP) steps objective effectively measures fit between time functions selected concentrations original and reduced models, variables keep track presence individual reactions. also...
Abstract Analysing omics data requires computational methods to effectively handle its complexity and derive meaningful hypotheses about molecular mechanisms. While data-driven statistical machine learning can identify patterns from across multiple samples, they typically require a large number of samples often lack interpretability alignment with existing biological knowledge. In contrast, knowledge-based network integrate prior knowledge provide results that are biologically interpretable,...
This paper presents the modeling and identification procedure for a VVER-type pressurized water reactor. The goal is to produce mathematical description in nonlinear state-space form that suitable control-oriented model analysis preliminary controller design experiments. proposed takes temperature effects xenon poisoning into consideration thus it an extension of formerly published simpler structures. Real transient measurement data from plant has been used based on standard prediction error...