Daniel Weindl

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.

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.

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.

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

This paper develops a rule-based cell cycle model for RPE-1 cells that explains 16 observables and improves model reusability through PEtab specification.

The paper introduces a new adjoint method for computing gradients in ODE models of biochemical reaction networks using steady-state data, achieving up to 4.4x speedup in simulation time for large-scale models.

The paper introduces SBML annotations for compact spline representations, improving model import performance in AMICI and enabling easier model editing.

This paper applies mini-batch optimization to ODE models, improving efficiency and results on a cancer signaling dataset.

This paper introduces a framework for efficiently estimating parameters of dynamic models from qualitative data using gradient-based optimization, achieving significant speedup and improved results.

The paper develops an efficient optimal scaling approach for parameter inference in dynamical models using qualitative data, improving robustness and reducing computation time.

The paper introduces a hierarchical method for efficiently parameterizing large-scale dynamic models using relative measurements, improving optimizer performance and enabling unbiased weighting of heterogeneous data.

The paper presents a computational framework to parameterize large-scale mechanistic models for predicting drug response in cancer cell lines using exome and transcriptome data, achieving higher accuracy than statistical approaches.

The paper benchmarks optimization methods for parameter estimation in large kinetic models and finds that a hybrid metaheuristic outperforms multi-start local searches.