Identifiability analysis for stochastic differential equation models in systems biology
Mathematical models are routinely calibrated to experimental data, with goals ranging from
building predictive models to quantifying parameters that cannot be measured. Whether or …
building predictive models to quantifying parameters that cannot be measured. Whether or …
Information geometry for multiparameter models: New perspectives on the origin of simplicity
Complex models in physics, biology, economics, and engineering are often ill-determined or
sloppy: their multiple parameters can vary over wide ranges without significant changes in …
sloppy: their multiple parameters can vary over wide ranges without significant changes in …
Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data
This work introduces a comprehensive approach to assess the sensitivity of model outputs to
changes in parameter values, constrained by the combination of prior beliefs and data. This …
changes in parameter values, constrained by the combination of prior beliefs and data. This …
PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model
Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help
formulate battery aging models. However, it is computationally intensive and cannot be …
formulate battery aging models. However, it is computationally intensive and cannot be …
[图书][B] Parameter redundancy and identifiability
D Cole - 2020 - taylorfrancis.com
Statistical and mathematical models are defined by parameters that describe different
characteristics of those models. Ideally it would be possible to find parameter estimates for …
characteristics of those models. Ideally it would be possible to find parameter estimates for …
Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates
AP Browning, MJ Simpson - PLoS Computational Biology, 2023 - journals.plos.org
An enduring challenge in computational biology is to balance data quality and quantity with
model complexity. Tools such as identifiability analysis and information criterion have been …
model complexity. Tools such as identifiability analysis and information criterion have been …
Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds
MPH Stumpf - Journal of the Royal Society Interface, 2020 - royalsocietypublishing.org
Recent progress in theoretical systems biology, applied mathematics and computational
statistics allows us to compare the performance of different candidate models at describing a …
statistics allows us to compare the performance of different candidate models at describing a …
The underlying connections between identifiability, active subspaces, and parameter space dimension reduction
AF Brouwer, MC Eisenberg - arXiv preprint arXiv:1802.05641, 2018 - arxiv.org
The interactions between parameters, model structure, and outputs can determine what
inferences, predictions, and control strategies are possible for a given system. Parameter …
inferences, predictions, and control strategies are possible for a given system. Parameter …
Algebra, geometry and topology of ERK kinetics
The MEK/ERK signalling pathway is involved in cell division, cell specialisation, survival and
cell death (Shaul and Seger in Biochim Biophys Acta (BBA)-Mol Cell Res 1773 (8): 1213 …
cell death (Shaul and Seger in Biochim Biophys Acta (BBA)-Mol Cell Res 1773 (8): 1213 …