Biology subjects

Hasenauer, J.

Publications and source records attributed to Hasenauer, J..

At least 19 recordsLinked to original sources

bioRxiv2026

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

Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This makes the choice of optimisation method critical. However, existing empirical studies either consider only a limited number of benchmark problems or only a narrow spectrum of local, global and hybrid optimisation methods. Here, we present a comprehensive benchmark of a broad range of optimisation methods on a curated collection of parameter estimation problems, comprising 990 method-problem-pairs executed on two independent supercomputing infrastructures. Our evaluation quantifies success rates, solution quality and computational cost, revealing characteristic strengths and limitations of each approach. We find that optimisation methods separated into clear performance tiers. Building on these results, we implemented a new hybrid strategy that combines enhanced scatter search with the best-performing local solver, which showed robust performance and improved on the other scatter-search variants we tested. Our results provide practical guidance for selecting optimisation methods and thereby support more accurate and reliable model calibration.

systems biology↗

bioRxiv2026

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

Antibody responses to COVID-19 vaccination vary widely across individuals, but the factors shaping these differences and their relation to underlying host-response processes are still not fully understood. Here, we combined longitudinal serological data from 4,553 healthcare workers with a nonlinear mixed-effects model to quantify determinants of vaccine-induced antibody dynamics. This framework captured both population-level trends and inter-individual variability in anti-spike antibody trajectories after vaccination. We found that age, sex, and pre-vaccine infection significantly influenced response dynamics. Male sex and increasing age were associated with weaker initial antibody production but slower waning, while individuals with a pre-vaccine infection had pre-existing anti-spike antibodies and slower initial antibody production and faster waning. These effects translated into substantial long-term differences in predicted antibody levels, reaching up to a 27% decrease in antibody levels one year after the third vaccination. Our results provide a dynamic view of the determinants of vaccine-induced humoral immunity and establish a framework for analyzing heterogeneous immune responses in large longitudinal cohorts.

Systems Biology↗

bioRxiv2026

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

Time-resolved measurements are central to calibrating mechanistic dynamical models, but current inference frameworks typically assume that reported measurement times are exact. In practice, actual sampling times may deviate from reported times because of sample-handling delays, imper-fect synchronization, or reporting errors. Here, we present a Bayesian framework for parameter inference in ordinary differential equation models that explicitly accounts for uncertainty in measurement times. We formulate latent measurement times as random variables and derive a joint and marginalized posterior. To compute the marginal likelihood efficiently, we augment the original dynamical system with additional state variables that evaluate the required integrals during numerical simulation. This reduces the dimensionality of the estimation problems and allows for efficient and reliable Markov chain Monte Carlo sampling. Across synthetic examples and a published model of carotenoid cleavage in Arabidopsis thaliana, neglecting time uncertainty led to biased estimates and overconfident uncertainty quantification, whereas the proposed marginalized formulation recovered reliable parameter estimates while substantially improving sampling efficiency and scalability. These results identify measurement time uncertainty as an important source of variability in dynamic modeling and establish posterior marginalization as a practical strategy for robust mechanistic inference.

systems biology↗

bioRxiv2026

Mathematical Modeling of the Canonical Aryl Hydrocarbon Receptor Pathway

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in xenobiotic sensing, as well as development, immunity, and tissue homeostasis. AhR signaling can proceed through a canonical and non-canonical pathway; the present study focuses on the canonical pathway. While ligand-dependent differences in binding affinities and direct ligand degradation kinetics are well known, and subtle differences in ligand binding can shape downstream signaling, it is still unclear which biochemical reaction steps within the canonical pathway are responsible for distinct ligand-specific transcriptional responses. Here, we developed a mechanistic ordinary differential equation model of the canonical AhR pathway. We calibrated the model to time-resolved qPCR measurements of Cyp1a1 and Ahrr mRNA in mouse bone-marrow-derived macrophages exposed to structurally diverse, environmentally relevant ligands with known immunomodulatory activity (3-methylcholanthrene, indolo[3,2-b]carbazole, and bisphenol A) using global optimization under a heteroskedastic likelihood. To dissect ligand specificity, we evaluated 528 candidate models that allow one or two ligand-involving reaction rate constants to vary. Akaike-based model selection reveals a dominant dynamical regime governed by promoter occupancy and target-gene mRNA synthesis, indicating that ligand-specific transcriptional responses are primarily encoded at the level of transcriptional regulation rather than upstream signaling events. The resulting model is made available in SBML and PEtab formats for reproducibility, and to enable further research into whether ligand-specific effects are conserved or rewired across cell types.

systems biology↗

bioRxiv2026

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

Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing. In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters. Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.

systems biology↗

bioRxiv2026

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

Reactive neutrophil infiltration can restrain CD8+ T cell expansion in lymph nodes during adoptive T cell therapy (ACT), yet its spatiotemporal regulation remains incompletely understood. Levaraging flow cytometry and multiplex immunofluorescence data, we performed a time-resolved quantitative assessment of immune cell dynamics in tumor-draining lymph node (tdLN) and non-tumor-draining lymph node (non-tdLN) in a melanoma mouse model receiving ACT. Transferred tumor-reactive CD8+ T cells accumulated and expanded early after treatment initiation, showing the highest frequency of a favorable central memory CD8+ T cell phenotype in the tdLN. Enhancing innate immune signaling in melanomas increased neutrophil influx into lymph nodes, particularly the non-tdLN; however, within the tdLN, neutrophils were enriched in the T cell zone, which also contained the largest absolute reservoir of transferred CD8+ T cells. Together, these findings indicate that tdLN and non-tdLN differ in early neutrophil dynamics and compartmentalization during ACT, influenced by the strength of innate immune signaling in the tumor.

immunology↗

bioRxiv2026

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

Protein kinase A (PKA) is a central integrator of nociceptive signaling, yet a quantitative account of how pro- and anti-nociceptive inputs shape its dynamics remains incomplete. Here, we develop a mechanistic model of PKA activity in nociceptive neurons that explicitly links receptor activation to downstream kinase regulation. Using time-course and dose-response measurements, we infer unknown process parameters and quantify parameter and prediction uncertainties to ensure robust conclusions. The model captures the activation of PKA by serotonin and forskolin and its suppression by opioids. We show how the model can be used for the assessment of alternative circuit topologies, and demonstrate that receptor context and stimulation history reconfigure PKA responsiveness, providing testable predictions for opioid modulation under clinically relevant dosing. This framework offers a principled basis for integrating PKA with broader pain-signaling networks, supports rational exploration of combination therapies, and establishes a general strategy for disentangling neuromodulatory control of kinase activity. Author summaryPain perception is modulated by a complex network of signaling pathways activated by different receptors with opposing effects. A key player in this process is protein kinase A (PKA), whose regulation by both serotonin and opioid receptors is not yet fully understood. In this study, we developed a mathematical model to investigate how these opposing signals affect PKA activity in sensory neurons. After estimating the unknown model parameters from a comprehensive dataset, we were able to quantitatively analyze the dynamic behavior of the system and use it for comparison of alternative circuit topologies. Our model provides a valuable tool for integrating diverse molecular interactions involved in pain processing and could help guide future efforts to develop better treatments for chronic pain and reduce opioid tolerance.

systems biology↗

bioRxiv2026

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

A typical neuron receives thousands of inputs and is able to adapt the strength of its synapses to store new information and meet ongoing computational demands. The synaptic response to plasticity induction is stochastic and spatially structured but is traditionally described by deterministic models representing the "average" dynamics. Growing experimental evidence indicates that not only the stimulation protocol determines the plasticity outcome but that the initial synaptic sizes, their fluctuations, and the spatial competition for the plasticity-relevant proteins play a decisive role. This probabilistic perspective makes it hard to predict the fate of a given synapse and requires a conceptual shift from a single synapse view to a probabilistic multi-spine competitive process where the plasticity needs and the available resources are considered together. Here, we propose a data-driven modeling framework able to predict collective plasticity outcomes along a dendrite based on the initial size, the number, and the spatial distance between simultaneously stimulated synapses. Our data analysis reveals a log-normal distribution of protein numbers for many plasticity-mediating proteins and shows that this log-normal protein allocation constrains and controls the collective plasticity outcome across multiple stimulated and non-stimulated synapses while preserving a global size distribution. Our findings highlight how local stochastic processes and global protein allocation rules give rise to synaptic plasticity outcomes, offering a new framework to understand and predict dendritic computation.

neuroscience↗

bioRxiv2025

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

Fine-tuned regulation of RNA polymerase II (Pol II) activity is essential for accurate gene expression. A key layer of this regulation involves phosphorylation of Pol IIs C-terminal domain (CTD), a repetitive heptapeptide tail that coordinates transcription and RNA-processing factors. The kinase P-TEFb plays a major role in this process, yet its precise phosphorylation mechanism remains unclear. Previous in vitro studies have suggested a distributive mode of action based largely on qualitative inspection of mass spectrometry data rather than quantitative analysis. Here, we use mathematical modeling of CTD phosphorylation to explore whether local context, such as neighboring phosphorylations or directional biases, affects PTEFb activity on the CTD. Our results indicate that P-TEFb acts distributively but with pronounced local cooperativity: repeats adjacent to phosphorylated sites are modified at higher rates. We find no evidence for directional bias, although the limited positional resolution of the data precludes a definitive conclusion. These results identify local context as an important factor in P-TEFb-mediated CTD phosphorylation and establish a quantitative modeling framework for dissecting multi-site modification dynamics.

systems biology↗

bioRxiv2025

HDL-associated phosphatidylserine blunts myeloid activation and protects from atherosclerosis

Background and aimsLipids play a critical role in atherosclerosis. Low-density lipoprotein (LDL)-cholesterol and certain lipid classes like sphingomyelins are associated with inflammation and poor cardiovascular outcomes. Phosphatidylserine (PS), on the other hand, is a negatively charged anti-inflammatory phospholipid class involved in efferocytosis. In this study, we sought to investigate its anti-atherosclerotic properties through a combination of complementary human lipidomics analyses, in vitro assays and in vivo experiments in Apoe-/- mice. MethodsHuman lipidomics studies were performed on the 300OB cohort comprising 300 obese and overweight individuals at risk of cardiovascular disease. In vitro assays were carried out using human monocytes and macrophages, and in vivo experiments included histopathological, immunophenotyping and single-cell transcriptomic analyses. ResultsIn humans, we identified PS as an anti-inflammatory and atheroprotective biomarker. Hence, we developed a high-density lipoprotein (HDL)-like formulation enriched in PS to exploit its properties in a targeted fashion in mice. In vitro, this formulation potently inhibited inflammatory cytokine production on human myeloid cells. Our in-depth in vivo experiments provided evidence of the formulations potent plaque-stabilizing and anti-inflammatory actions. These effects were mediated by a shift in the monocyte/macrophage compartment toward homeostatic/repairing phenotypes. ConclusionsCollectively, our results demonstrate that HDL-associated PS potently suppresses inflammation and atheroprogression, and holds promise as a viable approach to improve immunomodulatory therapies.

immunology↗

bioRxiv2025

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

RNA-binding proteins (RBP) are important regulators of RNA metabolism. In neurode-generative disorders such as Huntingtons Disease (HD), disrupted RBP-RNA interactions contribute to neuronal dysfunction. One such RBP, Midline 1 (MID1), has been shown to aberrantly associate with mutant huntingtin (Htt) RNA, enhancing its translation, yet the mechanism driving this effect remains unknown. Here, we develop a computational model to understand the role of MID1. Based on previously published data, our model predicts that MID1 increases the stability of the Htt RNA. We experimentally validate this prediction, showing that overexpression of MID1 significantly prolongs the half-life of mutant Htt RNA. Furthermore, we evaluate model refinements, including clustering of MID1-bound RNA, which allow capturing all key observations in the data. Together, we provide a data-driven framework that underlines the importance of RBP-RNA interaction in post-transcriptional regulation. This framework also shows how individual molecular reactions jointly determine RNA stability and protein levels in HD.

systems biology↗

bioRxiv2025

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

To assess cell migration in complex spatial environments, microfabricated chips, such as mazes and pillar forests, are routinely used to impose spatial and mechanical constraints, and cell trajectories are followed within these structures by advanced imaging techniques. In systems mechanobiology, computational models serve as essential tools to uncover how physical geometry influences intracellular dynamics; however, decoding such complex behaviors requires advanced inference techniques. Here, we integrated experimental observations of dendritic cell migration in a geometrically constrained microenvironment into a Cellular Potts model. We demonstrated that these spatial constraints modulate the motility dynamics, including speed and directional changes. We show that classical summary statistics, such as mean squared displacement and turning angle distributions, can resolve key mechanistic features but fail to extract richer spatiotemporal patterns, limiting accurate parameter inference. To solve this, we applied neural posterior estimation with in-the-loop learning of summary features. This learned summary representation of the data enables robust and flexible parameter inference, providing a data-driven framework for model calibration and advancing quantitative analysis of cell migration in structured microenvironments.

cell biology↗

bioRxiv2025

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

Endothelial cells (ECs) are central regulators of vascular and metabolic homeostasis, yet their organ- and depot-specific diversity remains underexplored. Two major types of adipose tissue (AT) can be distinguished that differ substantially in their physiological function and vascularization: white AT (WAT), which is the major energy storage and brown AT (BAT), which is highly vascularized and dissipates energy [1-5]. While ECs from these depots likely contribute to adipose function, their characterization has been hindered by technical limitations in isolation and culture. Here, we establish a protocol for isolating and expanding ECs from murine BAT and WAT, enabling transcriptomic and functional analyses across depots. We demonstrate that freshly isolated BAT-ECs express depot-specific gene signatures, including Rgcc, Cdkn1c, Tcf15, Meox2, and Efnb1, several of which are dynamically regulated during cold-induced BAT activation. These findings reveal novel BAT-EC markers and highlight specialized endothelial programs that may support thermogenesis. However, we also uncover that culturing BAT-ECs profoundly remodels EC identity. Transcriptomic profiling shows that BAT-ECs rapidly downregulate BAT-specific markers and acquire features resembling WAT- and lymphatic ECs. This dedifferentiation is accompanied by signatures of proliferation, adhesion remodeling, and endothelial-to-mesenchymal transition. While these changes present challenges for maintaining depot-specific identity in culture, they also provide a framework to better interpret experimental outcomes and to investigate EC plasticity. Taken together, our study delivers a novel isolation and culture protocol for adipose ECs, defines BAT-EC markers, and demonstrates how culture conditions reshape their identity. These insights build the foundation for future research of AT vasculature. HighlightsO_LIFast method to purify endothelial cells (ECs) for direct RNA or protein extraction. C_LIO_LIEasy method to isolate and culture ECs specifically from thermogenic adipose tissue. C_LIO_LINovel signature of BAT-ECs regulated upon browning and lost in culture. C_LIO_LIRGC-32, p57kip2, TCF15/MEOX2 and EFNB1 as novel BAT-EC-specific markers. C_LI

cell biology↗

bioRxiv2025

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

Small extracellular vesicles (small EVs) are nanovesicles found in tissues and body fluids that contain regulatory molecules including microRNAs, termed exomiRs. Research in murine models has demonstrated that exercise can trigger the release of small EVs into the circulation. The aim of this study was to study exomiR release in humans pre and post exercise and to characterise the function of these microRNAs especially in relation to thermogenic fat. We found that exercise increased the release of exomiR-196a-5p in endurance athletes, a microRNA that induces UCP1 expression and browning of white adipocytes. We observed that myotubes specifically release miR-196a-5p within small EVs after in vitro exercise-mimicking conditions such as electrical pulse stimulation and cAMP treatment. Likewise, the expression at basal levels of the exercise-induced exomiR-342-3p negatively correlated with BMI and age. EV proteomics revealed a positive correlation between FABP4+ and miR-342-3p, suggesting an adipocyte cell origin. Overexpression of miR-342-3p increased Myogenin levels during skeletal muscle cell differentiation, indicating a positive role in muscle differentiation. Our results suggest that oxidative extreme metabolic capacities in endurance athletes contribute to the enhanced release of circulatory exomiRs after exercise mediating bi-directional crosstalk between skeletal muscle and thermogenic adipose tissue. Graphical abstractO_LISerum-EVs from endurance athletes increase UCP1 expression in white adipocytes. C_LIO_LImiR-196a and miR-342-3p are increased in serum-EVs from endurance athletes. C_LIO_LIMuscle cells release EVs enriched in miR-196a after electrical pulse stimulation. C_LIO_LImiR-196a and miR-342-3p have browning and myogenic potential, respectively. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/656129v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1bd9b25org.highwire.dtl.DTLVardef@114a549org.highwire.dtl.DTLVardef@6f863borg.highwire.dtl.DTLVardef@1d5c528_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

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

A central question in mathematical modeling of biological systems is determining which processes are most relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the specification of model selection problems and the swift evaluation of a broad spectrum of approaches are not available. PEtab Select addresses this challenge by providing a concise, standardized specification of model selection and its associated calibration problems through a new file format standard and software package. The standard facilitates the compact representation of even very large model selection problems; in one example, billions of model alternatives. PEtab Select builds on the PEtab standard for the specification of parameter estimation problems, and enables the use of state-of-the-art modelling and calibration workflows utilizing COPASI, Data2Dynamics, PEtab.jl, and pyPESTO. PEtab Select supports common model selection criteria (e.g., Akaike and Bayesian information criteria) and can be easily extended to use others. To ensure flexibility, PEtab Select implements several model space exploration approaches, including basic brute-force, forward, and backward selection, and also advanced, flexible selection methods. PEtab Select introduces the first standardization of model selection tasks, filling a critical gap in existing computational pipelines. It constitutes an essential contribution to FAIR research software in systems biology by promoting interoperability and reusability in model selection. Author summaryModel selection is a crucial step in mathematical modeling, guiding the choice of components to include in a model. PEtab Select automates this process across diverse modeling frameworks and programming languages via (1) a new interoperable, language-agnostic standard for specifying large-scale model selection problems, and (2) a comprehensive software package that implements these selection methods. Developed through a community effort, PEtab Select has been integrated into multiple modeling frameworks and is accessible to users of COPASI, Julia, MATLAB, and Python.

systems biology↗

bioRxiv2025

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

Dynamic models are useful to study processes ranging from cell signalling to cell differentiation. Common modelling workflows such as model exploration and parameter estimation are computationally demanding. The Julia programming language is a promising tool to address these computational challenges. To evaluate it we developed SBMLImporter.jl and PEtab.jl, a package for model fitting. SBMLImporter.jl was used to evaluate different stochastic simulators against PySB and RoadRunner, overall Julia simulators proved fastest. For Ordinary Differential Equations (ODE) models solvers, gradient methods, and parameter estimation performance were evaluated using PEtab benchmark problems. For the latter two tasks PEtab.jl was compared against pyPESTO, which employs the high-performance AMICI library. Guidelines for choosing ODE solver were produced by evaluating 31 ODE solvers for 29 models. Further, by leveraging automatic differentiation PEtab.jl proved efficient and, for up to medium-sized models, was often at least twice faster than pyPESTO, showcasing how Julias ecosystem can accelerate modelling workflows.

systems biology↗

bioRxiv2025

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

Indirect calorimetry is a cornerstone technique for metabolic phenotyping of animal models in preclinical research, with well-established experimental protocols and platforms. However, a flexible, extensible, and user-friendly software suite that enables standardized integration of data and metadata from diverse metabolic phenotyping platforms--followed by unified statistical analysis and visualization--remains absent. We present Shiny-Calorie, an open-source interactive web application for transparent data and metadata integration, comprehensive statistical data analysis, and visualization of indirect calorimetry datasets. Shiny-Calorie is compatible with data formats from widely used commercial metabolic phenotyping platforms, such as TSE and Sable Systems, and includes functionality for exporting processed data in these formats. Built using GNU R and a Shiny-based reactive interface, Shiny-Calorie enables intuitive exploration of complex, multi-modal longitudinal datasets comprising categorical, continuous, ordinal, and count variables. The platform incorporates state-of-the-art statistical methods for robust hypothesis testing, thereby facilitating biologically meaningful interpretation of energy metabolism phenotypes, including resting metabolic rate and energy expenditure. Overall, Shiny-Calorie streamlines routine analysis workflows and enhances reproducibility and transparency in metabolic phenotyping studies.

bioinformatics↗

bioRxiv2025

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

Oncolytic virus therapy (OVT) is emerging as a potent alternative to conventional cancer treatments by employing engineered viruses that selectively infect and lyse tumor cells while sparing normal tissues. Although mathematical models have been developed to elucidate the dynamics of OVT and inform personalized therapies, they are often specific to certain organisms. Mathematical models tailored to more recently developed animal models of OVT, such as zebrafish, are not yet available. Here, we introduce the first mathematical model of OVT trained on zebrafish data from published studies to bridge the gap. We explore a variety of mathematical model structures and perform parameter estimation and model selection. The selected model effectively captures the observed tumor dynamics, i.e. delayed tumor shrinkage, and provides valuable insights into the underlying mechanisms of OVT in zebrafish. Our work establishes the groundwork for advancing experimental studies in zebrafish, contributing to the design of more effective cancer treatment strategies in the future.

systems biology↗