Jan Hasenauer

PEtab Select: specification standard and supporting software for automated model selection

PEtab Select provides a standardized format and software for automated model selection in systems biology, supporting diverse modeling frameworks and enabling efficient evaluation of large model spaces.

Large-scale analysis of optimisation methods for parameter estimation problems in the life sciences

The paper benchmarks optimization methods for parameter estimation in life sciences, identifying performance tiers and proposing a hybrid strategy that improves solution quality and robustness.

Linking Host Covariates to COVID-19 Vaccine-Induced Antibody Dynamics in a Large Healthcare Worker Cohort

The paper models antibody dynamics after COVID-19 vaccination in healthcare workers, finding that age, sex, and prior infection significantly affect antibody levels over time.

Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding

The paper develops a computational model to show that MID1 binding increases mutant huntingtin RNA stability, validated experimentally.

Efficient Bayesian inference for ordinary differential equation models from experimental data with uncertain measurement times

This paper presents a Bayesian framework for parameter inference in ODE models that accounts for uncertain measurement times, improving reliability and efficiency in dynamic modeling.

Mathematical Modeling of the Canonical Aryl Hydrocarbon Receptor Pathway

The paper develops a mathematical model of the canonical AhR pathway to identify which biochemical steps drive ligand-specific transcriptional responses using qPCR data from mouse macrophages.

Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data

This paper presents a framework for integrating single-cell and bulk data using nonlinear mixed-effect models and tailored parameter estimation, improving parameter identifiability and prediction accuracy.

From Brown to White: Brown Adipose Tissue Endothelial Cells whiten in Culture Conditions

This paper isolates and cultures endothelial cells from brown and white adipose tissue, showing that brown adipose tissue endothelial cells lose their identity in culture and adopt features of white adipose and lymphatic endothelial cells.

Endothelial deletion of ADGRF5 (GPR116) promotes fibro-inflammatory EndMT and impairs adaptive thermogenesis in brown adipose tissue

This study shows that deleting Adgrf5 in endothelial cells of brown adipose tissue impairs thermogenic capacity during prolonged cold exposure by inducing EndMT and fibro-inflammatory changes.

Longitudinal spatial profiling of neutrophils during adoptive T cell therapy in murine melanoma reveals distinct lymph node infiltration patterns across anatomical sites

This study examines neutrophil infiltration patterns in lymph nodes during adoptive T cell therapy in mice, revealing differences between tumor-draining and non-tumor-draining lymph nodes.

A mechanistic model of protein kinase A dynamics under pro- and anti-nociceptive inputs

The paper develops a mechanistic model of PKA dynamics in nociceptive neurons to quantify how pro- and anti-nociceptive inputs shape its activity and provides testable predictions for opioid modulation.

Push-and-pull protein dynamics leads to log-normal synaptic sizes and probabilistic multi-spine plasticity

The paper introduces a data-driven framework to predict collective synaptic plasticity outcomes by considering protein allocation and spatial competition among synapses.

Simulation-Based Inference of Cell Migration Dynamics in Complex Spatial Environments

The paper uses a Cellular Potts model and neural posterior estimation to infer cell migration dynamics in microfabricated environments, showing improved parameter inference over classical methods.

Quantitative modeling of P-TEFb mediated CTD phosphorylation identifies local cooperativity

The paper uses mathematical modeling to show P-TEFb phosphorylates CTD repeats with local cooperativity but no directional bias.

HDL-associated phosphatidylserine blunts myeloid activation and protects from atherosclerosis

This study identifies phosphatidylserine as an anti-inflammatory biomarker and shows that HDL-associated PS reduces atherosclerosis through plaque stabilization and immune modulation in mice.

Shiny-Calorie: A context-aware application for indirect calorimetry data analysis and visualization using R

Shiny-Calorie provides a flexible, open-source tool for analyzing and visualizing indirect calorimetry data, supporting standardized integration and statistical analysis of metabolic phenotyping datasets.

PEtab.jl: Advancing the Efficiency and Utility of Dynamic Modelling

PEtab.jl improves dynamic modelling efficiency by comparing ODE solvers and parameter estimation methods, showing faster performance than pyPESTO for medium-sized models.

Universal differential equations for systems biology: Current state and open problems

This paper evaluates Universal Differential Equations (UDEs) for systems biology, showing their versatility but highlighting challenges with noise, data scarcity, and interpretability.

NKG7 is a stable marker of cytotoxicity across immune contexts and within the tumor microenvironment

The paper identifies NKG7 as a reliable and consistent marker of cytotoxicity across immune subsets and tissues, outperforming traditional markers like Granzyme B and Perforin.

miR-196a-5p and miR-342-3p mediate skeletal muscle and thermogenic adipose tissue crosstalk through extracellular vesicles

This study identifies miR-196a-5p and miR-342-3p as exomiRs released during exercise that mediate crosstalk between skeletal muscle and thermogenic adipose tissue.

A Stochastic Modelling Framework for Cancer Patient Trajectories: Combining Tumour Growth, Metastasis, and Survival

This paper introduces a stochastic modelling framework for cancer progression that integrates tumour growth, metastasis, and survival, using clinical data to analyze patient trajectories.

Pseudo-time trajectory of single-cell lipidomics: Suggestion for experimental setup and computational analysis

The paper proposes an experimental setup and computational framework to infer longitudinal single-cell lipid metabolism data from a single destructive measurement.

Parameter Estimation and Model Selection for the Quantitative Analysis of Oncolytic Virus Therapy in Zebrafish

This paper develops a mathematical model of oncolytic virus therapy in zebrafish to capture tumor dynamics and inform future cancer treatments.

cOmicsArt - a customizable Omics Analysis and reporting tool

cOmicsArt provides a customizable tool for omics analysis and reporting, integrating GUI-based analysis with R for reproducibility and customization.

Hawkes process modelling for chemical reaction networks in a random environment

The paper models chemical reaction networks in a random environment using Hawkes processes to approximate marginal descriptions of the system.

C-COMPASS: A Neural Network Tool for Multi-Omic Classification of Cell Compartments

C-COMPASS predicts protein and lipid localization across cellular compartments using neural networks, enabling multi-omic analysis of organelle dynamics in humanized liver mice.

Missing data in amortized simulation-based neural posterior estimation

This paper addresses missing data in amortized simulation-based neural posterior estimation by proposing methods to encode and handle missing values, showing improved robustness in inference.

Anabolic lipid metabolism regulates adipose type 2 innate lymphoid cell differentiation to maintain metabolic health

This study shows that ACC1 regulates adipose ILC2 differentiation and function in obesity, linking lipid metabolism to metabolic health.

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

This paper introduces Metadatasheet, a metadata standard designed to improve data management by aligning with the data-lifecycle and enabling structured metadata recording in Microsoft Excel.

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

This paper introduces a spline-based method for parameter estimation in ODE models using semi-quantitative data, improving estimation efficiency and enabling discovery of unknown measurement transformations.