Daniel Weindl
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View article: PEtab Select: specification standard and supporting software for automated model selection
PEtab Select: specification standard and supporting software for automated model selection Open
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…
View article: Pseudo-time trajectory of single-cell lipidomics: Suggestion for experimental setup and computational analysis
Pseudo-time trajectory of single-cell lipidomics: Suggestion for experimental setup and computational analysis Open
Summary Cellular heterogeneity is a fundamental facet of cell biology, influencing cellular signaling, metabolism, and gene regulation. Its accurate quantification requires measurements at the single-cell level. Most high-throughput single…
View article: Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states
Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states Open
Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based opti…
View article: Exploration of methods for computing sensitivities in ODE models at dynamic and steady states
Exploration of methods for computing sensitivities in ODE models at dynamic and steady states Open
Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based opti…
View article: Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells
Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells Open
The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By c…
View article: pyPESTO: a modular and scalable tool for parameter estimation for dynamic models
pyPESTO: a modular and scalable tool for parameter estimation for dynamic models Open
Summary Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modu…
View article: A more expressive spline representation for SBML models improves code generation performance in AMICI
A more expressive spline representation for SBML models improves code generation performance in AMICI Open
s Spline interpolants are commonly used for discretizing and estimating functions in mathematical models. While splines can be encoded in the Systems Biology Markup Language (SBML) using piecewise functions, the resulting formulas are very…
View article: Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells
Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells Open
The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By c…
View article: pyPESTO: A modular and scalable tool for parameter estimation for dynamic models
pyPESTO: A modular and scalable tool for parameter estimation for dynamic models Open
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. We present pyPESTO, a mod…
View article: Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks Open
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 op…
View article: Epidemiological and Serological Analysis of a SARS-CoV-2 Outbreak in a Nursing Home: Impact of SARS-CoV-2 Vaccination and Enhanced Neutralizing Immunity Following Breakthrough Infection
Epidemiological and Serological Analysis of a SARS-CoV-2 Outbreak in a Nursing Home: Impact of SARS-CoV-2 Vaccination and Enhanced Neutralizing Immunity Following Breakthrough Infection Open
Background: Despite a vaccination rate of 82.0% (n = 123/150), a SARS-CoV-2 (Alpha) outbreak with 64.7% (n = 97/150) confirmed infections occurred in a nursing home in Bavaria, Germany. Objective: the aim of this retrospective cohort study…
View article: Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks Open
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 op…
View article: BioSimulators: a central registry of simulation engines and services for recommending specific tools
BioSimulators: a central registry of simulation engines and services for recommending specific tools Open
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various …
View article: BioSimulators: a central registry of simulation engines and services for\n recommending specific tools
BioSimulators: a central registry of simulation engines and services for\n recommending specific tools Open
Computational models have great potential to accelerate bioscience,\nbioengineering, and medicine. However, it remains challenging to reproduce and\nreuse simulations, in part, because the numerous formats and methods for\nsimulating vario…
View article: Dynamic models for metabolomics data integration
Dynamic models for metabolomics data integration Open
As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamic models of metabolism allow for the integration of heterogene…
View article: Efficient gradient-based parameter estimation for dynamic models using qualitative data
Efficient gradient-based parameter estimation for dynamic models using qualitative data Open
Motivation Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitati…
View article: Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets*
Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets* Open
This archive contains supplementary material to the revised version of the manuscript Mini-batch optimization enables training of ODE models on large-scale datasets This upload contains: Code for parameter estimation which we used to find …
View article: Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets*
Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets* Open
This archive contains supplementary material to the revised version of the manuscript Mini-batch optimization enables training of ODE models on large-scale datasets This upload contains: Code for parameter estimation which we used to find …
View article: Dynamical models for metabolomics data integration
Dynamical models for metabolomics data integration Open
As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamical models of metabolism allow for the integration of heteroge…
View article: AMICI: high-performance sensitivity analysis for large ordinary differential equation models
AMICI: high-performance sensitivity analysis for large ordinary differential equation models Open
Summary Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can…
View article: Efficient gradient-based parameter estimation for dynamic models using qualitative data
Efficient gradient-based parameter estimation for dynamic models using qualitative data Open
Motivation Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitati…
View article: PEtab—Interoperable specification of parameter estimation problems in systems biology
PEtab—Interoperable specification of parameter estimation problems in systems biology Open
Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introdu…
View article: Mini-batch optimization enables training of ODE models on large-scale datasets
Mini-batch optimization enables training of ODE models on large-scale datasets Open
Quantitative dynamical models are widely used to study cellular signal processing. A critical step in modeling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, establis…
View article: Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets*
Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets* Open
This archive contains supplementary material to the manuscript Mini-batch optimization enables training of ODE models on large-scale datasets This upload contains: Code for parameter estimation which we used to find our results The biologi…
View article: Statistical inference of mechanistic models from qualitative data using an efficient optimal scaling approach
Statistical inference of mechanistic models from qualitative data using an efficient optimal scaling approach Open
Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative e…
View article: Efficient parameterization of large-scale dynamic models based on relative measurements
Efficient parameterization of large-scale dynamic models based on relative measurements Open
Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of…
View article: Supplementary data to Schmiester et al. *Efficient parameterization of large-scale dynamic models based on relative measurements*
Supplementary data to Schmiester et al. *Efficient parameterization of large-scale dynamic models based on relative measurements* Open
This archive contains Supplementary data to the manuscript Efficient parameterization of large-scale dynamic models based on relative measurements by Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer and Daniel Weindl.