| seeds-package {seeds} | R Documentation |
seeds: Estimate Hidden Inputs using the Dynamic Elastic Net
Description
Algorithms to calculate the hidden inputs of systems of differential equations. These hidden inputs can be interpreted as a control that tries to minimize the discrepancies between a given model and taken measurements. The idea is also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>. To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.
Details
Details
The first algorithm (DEN) calculates the needed equations using the Deriv
function of the Deriv package. The process is implemented through the use
of the S4 class odeEquations-class.
The conjugate gradient based algorithm uses a greedy algorithm to estimate a
sparse control that tries to minimize the discrepancies between a given
'nominal model given the measurements (e.g from an experiment). The algorithm
the ode uses deSolve to calculate the hidden inputs w
based on the adjoint equations of the ODE-System.
The adjoint equations are calculated using the ode function of the
deSolve package. For the usage of the algorithm please look into the
examples and documentation given for the functions.
The second algorithm is called Bayesian Dynamic Elastic Net (BDEN). The BDEN as a new and fully probabilistic approach, supports the modeler in an algorithmic manner to identify possible sources of errors in ODE based models on the basis of experimental data. THE BDEN does not require pre-specified hyper-parameters. BDEN thus provides a systematic Bayesian computational method to identify target nodes and reconstruct the corresponding error signal including detection of missing and wrong molecular interactions within the assumed model. The method works for ODE based systems even with uncertain knowledge and noisy data.
Author(s)
Maintainer: Tobias Newmiwaka tobias.newmiwaka@gmail.com
Authors:
Benjamin Engelhardt engelhar@bit.uni-bonn.de
References
Benjamin Engelhardt, Holger Froehlich, Maik Kschischo Learning (from) the errors of a systems biology model, Nature Scientific Reports, 6, 20772, 2016 https://www.nature.com/articles/srep20772
See Also
Useful links: