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seeds (version 0.9.1)

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) . To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: .

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Install

install.packages('seeds')

Monthly Downloads

134

Version

0.9.1

License

MIT + file LICENSE

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Repository

https://github.com/Newmi1988/seeds

Maintainer

Tobias Newmiwaka

Last Published

July 14th, 2020

Functions in seeds (0.9.1)

LOGLIKELIHOOD_func

Calculates the Log Likelihood for a new sample given the current state (i.e. log[L(G|x)P(G)])

Get the estimated states

Test dataset for demonstrating the bden algorithm.

Componentwise Adapted Metropolis Hastings Sampler

confidenceBands

Get the estimated confidence bands for the bayesian method

Automatic Calculation of optimal Initial Parameters

Bayesian Dynamic Elastic Net

createCompModel

Create compilable c-code of a model

outputEstimates

Get the estimated outputs

Results from the uvb dataset for examples

Set the inputs of the model.

plot,resultsSeeds,missing-method

Plot method for the S4 class resultsSeeds

UVB signal pathway

Greedy method for estimating a sparse solution

print,resultsSeeds-method

A default printing function for the resultsSeeds class

set measurements of the model

seeds: Estimate Hidden Inputs using the Dynamic Elastic Net

An object of the odeModel Class

Set the vector with the initial (state) values

Create annotated plot

Get the estimated hidden inputs

resultsSeeds-class

Results Class for the Algorithms

Import SBML Models using the Bioconductor package 'rsbml'

optimal_control_gradient_descent

estimating the optimal control using the dynamic elastic net

A class to store the important information of an model.

Set the measurement equation for the model

setModelEquation

Set the model equation

Set the standard deviation of the measurements

odeEquations-class

A S4 class used to handle formatting ODE-Equation and calculate the needed functions for the seeds-algorithm

Calculate the nominal solution of the model

Set the model parameters