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>.

Wrapper around various existing tools and command-line interfaces,
  providing a standard interface, simple parallelization, and detailed logging.
  For microarray data, maps probe sets to standard gene IDs, building on
  'GEOquery' Davis and Meltzer (2007) <doi:10.1093/bioinformatics/btm254>,
  'ArrayExpress' Kauffmann et al. (2009) <doi:10.1093/bioinformatics/btp354>,
  Robust multi-array average 'RMA' Irizarry et al. (2003) <doi:10.1093/biostatistics/4.2.249>,
  and 'BrainArray' Dai et al. (2005) <doi:10.1093/nar/gni179>.
  For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology
  Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and
  quality trimming using 'TrimGalore' Krueger <https://github.com/FelixKrueger/TrimGalore>,
  performs quality control checks using 'FastQC' Andrews <https://github.com/s-andrews/FastQC>,
  quantifies transcript abundances using 'salmon' Patro et al. (2017) <doi:10.1038/nmeth.4197> and potentially
  'refgenie' Stolarczyk et al. (2020) <doi:10.1093/gigascience/giz149>,
  aggregates the results using 'MultiQC' Ewels et al. (2016) <doi:10.1093/bioinformatics/btw354>,
  maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) <doi:10.1038/nprot.2009.97>,
  and summarizes transcript-level quantifications for gene-level analyses using
  'tximport' Soneson et al. (2015) <doi:10.12688/f1000research.7563.2>.