TemporalGSSA: Outputs Temporal Profile of Molecules from Stochastic Simulation
Algorithm Generated Datasets
The data that is generated from independent and consecutive 'GillespieSSA' runs for a generic biochemical network
is formatted as rows and constitutes an observation. The first column of each row is the computed timestep for each run. Subsequent
columns are used for the number of molecules of each participating molecular species or "metabolite" of a generic biochemical network.
In this way 'TemporalGSSA', is a wrapper for the R-package 'GillespieSSA'. The number of observations must be at least 30.
This will generate data that is statistically significant. 'TemporalGSSA', transforms this raw data into a simulation time-dependent and metabolite-specific
trial. Each such trial is defined as a set of linear models (n >= 30) between a timestep and number of molecules for a metabolite. Each linear model
is characterized by coefficients such as the slope, arbitrary constant, etc. The user must 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 coefficients of each linear model are averaged (mean slope, mean constant)
and are incorporated into a metabolite-specific linear regression model as the dependent variable. 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. The generated "technical
replicates" can be statistically evaluated (mean, standard deviation) and will constitute simulation time-dependent molecules for each metabolite.
For SSA-generated datasets with varying simulation times 'TemporalGSSA' will generate a simulation time-dependent trajectory for each metabolite
of the biochemical network under study. The relevant publication with the mathematical derivation of the algorithm is
(2022, Journal of Bioinformatics and Computational Biology) <doi:10.1142/S0219720022500184>. The algorithm has been deployed in the following publications
(2021, Heliyon) <doi:10.1016/j.heliyon.2021.e07466> and (2016, Journal of Theoretical Biology) <doi:10.1016/j.jtbi.2016.07.002>.
||testthat (≥ 3.0.0)
||Siddhartha Kundu <siddhartha_kundu at aiims.edu>
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