58 citations · 87 across the 7 of their papers we have counts for
7 papers · 1 filter
PEtab SciML: an exchange format for specifying and training dynamic scientific machine learning models
Sebastian Persson, Branwen Snelling, Maren Philipps +5
Summary: Dynamic scientific machine learning (SciML) models that combine mechanistic ordinary differential equations (ODEs) with machine learning (ML) components have applications…
PEtab-GUI: A graphical user interface to create, edit and inspect PEtab parameter estimation problems
Paul Jonas Jost, Frank T Bergmann, Daniel Weindl +1
Motivation: Parameter estimation is a cornerstone of data-driven modeling in systems biology. Yet, constructing such problems in a reproducible and accessible manner remains challe…
Exploration of methods for computing sensitivities in ODE models at dynamic and steady states
Polina Lakrisenko, Dilan Pathirana, Daniel Weindl +1
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 objec…
pyPESTO: A modular and scalable tool for parameter estimation for dynamic models
Yannik Schälte, Fabian Fröhlich, Paul J. Jost +14
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challengi…
BioSimulators: a central registry of simulation engines and services for recommending specific tools
Bilal Shaikh, Lucian P. Smith, Dan Vasilescu +68
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, becau…
Dynamical models for metabolomics data integration
Polina Lakrisenko, Daniel Weindl
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…
AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models
Fabian Fröhlich, Daniel Weindl, Yannik Schälte +5
Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the…