The data that is generated from consecutive 'GillespieSSA' runs for a generic biochemical network is formatted as "rows". The first column of each row constitutes the computed timestep. Subsequent columns are used for the participating molecules of a generic biochemical network. In this way 'TemporalGSSA', may be considered a wrapper for the R-package 'GillespieSSA'. The number of observations must be at least 30. This will generate data that is statistically significant. The user must also enter an integer from 1-4. These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all). These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability, "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The temporal profile of a molecule is necessary to comprehend its' behaviour within the cell. This is accomplished by selecting a representative timestep for a set of observations or consecutive runs (n >= 30). A linear model of the numbers of each molecule is created with the associated timestep from these observations. The coefficients of this model (slope, constant) are then incorporated into a second linear regression model. Here, the independent variable is the representative timestep chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) observations. These steps can be replicated with multiple set of observations or runs. The generated "technical replicates" can be averaged and will constitute the time-dependent data point of each molecule for a particular simulation time. For varying simulation times these data will generate time-dependent trajectories for each molecule of the biochemical network under study. The algorithm has been deployed effectively in previous publications Kundu, S (2021, Heliyon) <doi:10.1016/j.heliyon.2021.e07466> and (2016, Journal of Theoretical Biology) <doi:10.1016/j.jtbi.2016.07.002>.

Version: | 1.0.0 |

Depends: | stats |

Suggests: | testthat (≥ 3.0.0) |

Published: | 2022-03-02 |

Author: | Siddhartha Kundu |

Maintainer: | Siddhartha Kundu <siddhartha_kundu at aiims.edu> |

License: | GPL-3 |

NeedsCompilation: | no |

CRAN checks: | TemporalGSSA results |

Reference manual: | TemporalGSSA.pdf |

Package source: | TemporalGSSA_1.0.0.tar.gz |

Windows binaries: | r-devel: TemporalGSSA_1.0.0.zip, r-release: TemporalGSSA_1.0.0.zip, r-oldrel: TemporalGSSA_1.0.0.zip |

macOS binaries: | r-release (arm64): TemporalGSSA_1.0.0.tgz, r-oldrel (arm64): TemporalGSSA_1.0.0.tgz, r-release (x86_64): TemporalGSSA_1.0.0.tgz, r-oldrel (x86_64): TemporalGSSA_1.0.0.tgz |

Please use the canonical form
`https://CRAN.R-project.org/package=TemporalGSSA`
to link to this page.