TemporalGSSA (version 1.0.1)

calculate_TemporalGSSA: calculate_TemporalGSSA() This is the main function of the package 'TemporalGSSA'.

Description

'TemporalGSSA', may be considered a wrapper for the R-package 'GillespieSSA'. The formatted data generated by consecutive runs of 'GillespieSSA' for molecules of a generic biochemical network will generate a dataset of randomly chosen timesteps along with changes to the corresponding number of molecules. This constitutes a set observations for a specific simulation time.

Usage

calculate_TemporalGSSA(data, type, out)

Value

code A numerically encoded ('0', success; '1', no success) text message to the user indicating the outcome of "TemporalGSSA".

The examples listed below can be run directly from the console as example("calculate_TemporalGSSA")

Arguments

data

Formatted data from the output of consecutive 'GillespieSSA' runs. This is a mandatory argument.

type

A mandatory integer argument that indicates the statistic that will be used to compute the representative timestep for a set of observations. Here: "1" is the MEAN, "2" is the MEDIAN, "3" is a random choice and "4" is ALL

out

An optional logical argument that control the console output with default being "TRUE" Here, TRUE curtails the output while FALSE allows the verbose output of all the coefficents .

Details

The temporal profile of a metabolite is a pre-requisite to comprehend its behaviour in vivo.

This is accomplished by transforming the raw formatted data into a 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. The coefficients of these models (slope, constant) are averaged and incorporated into a metabolite-specific linear regression model. The independent variable is a representative timestep and is user-dependent. The generated data is an imputed number of molecules for each metabolite, is dependent on the simulation time and is defined as a "technical replicate".

The above steps are easily repeated with additional SSA-runs (n >=30) and will generate sets of "technical replicates". These can be statistically evaluated (mean, standard deviation) and will constitute the simulation time-dependent number of molecules for a particular metabolite. For varying simulation times 'TemporalGSSA' will generate a metabolite-specific and simulation time-dependent trajectory.

Examples

# \dontshow{
valid <- matrix(c(4.99592056104662E-19,499995,500003,52076,856074,408144,408147,213587,305440,305446,305445,1039792,9.0895286250952E-19,499992,500000,29946,842290,372229,372231,4384,132154,132160,132167,1097848,5.66871925230001E-19,499989,500008,43979,856199,400171,400175,177259,277086,277096,277095,1055869,9.13431925017368E-19,499997,499999,30174,842560,372728,372730,22234,128214,128213,128214,1097112,6.66856629255775E-19,499997,500007,36194,853340,389533,389529,124479,234948,234950,234950,1074288,9.18748453328027E-19,499998,500001,29951,841897,371842,371845,466464,128159,128156,128159,1098219,6.33923848260308E-19,499996,499997,38414,854111,392517,392523,141620,249102,249102,249106,1069085,6.61025475380139E-19,499995,500004,36842,852757,389594,389595,127161,237566,237572,237572,1073574,6.46404578600321E-19,499997,499993,37546,853998,391541,391543,134830,243289,243287,243290,1070928,8.71251875548091E-19,499995,500002,30238,844659,374894,374894,23273,148382,148384,148386,1094886,6.42456762267738E-19,499994,500001,37659,854377,392030,392032,137247,245215,245219,245221,1070325,6.50000982450828E-19,499997,500002,37403,853794,391193,391195,132931,241735,241744,241741,1071411,6.51703301709049E-19,499996,500000,37294,853141,390431,390434,132095,241663,241666,241664,1072285,9.19733238113478E-19,499997,499998,29641,842522,372160,372162,74747,128165,128167,128168,1098208,8.8945111346424E-19,499999,499998,29956,844208,374158,374163,14760,140602,140598,140602,1095898,6.55496374724788E-19,499994,499999,37078,853792,390867,390867,129538,238669,238673,238673,1072070,6.40478506078292E-19,499997,499993,38083,854148,392221,392220,138163,245947,245945,245954,1069723,8.46044969800135E-19,499996,500004,30517,845908,376418,376424,34899,158483,158480,158477,1093077,9.19232350704206E-19,499994,500002,29541,842618,372151,372156,64364,127850,127852,127852,1098330,9.05290488116942E-19,499999,500002,30116,843048,373157,373161,858885,133256,133260,133258,1096737,9.0739154712218E-19,499990,500005,29854,843198,373043,373049,4709,131663,131673,131675,1097125,9.18051289626637E-19,499995,499999,29866,841910,371768,371769,63232,128229,128235,128241,1098386,9.18014676040227E-19,499996,500000,29956,842427,372378,372384,37733,127620,127621,127618,1097675,8.99677088174691E-19,499993,500003,29947,842729,372672,372673,8741,136069,136075,136076,1097396,9.08400495196285E-19,499992,500009,29926,843132,373051,373055,4843,131792,131799,131796,1097043,9.17671540274208E-19,499996,499999,29855,842155,372001,372007,97878,128919,128919,128921,1098164,9.15629173860131E-19,499995,500001,30161,842024,372181,372183,255,128075,128079,128081,1097681,9.18787494819808E-19,500000,500003,30059,842445,372498,372502,758505,127500,127502,127503,1097454,9.19961013729775E-19,499999,500007,29565,842688,372249,372251,657849,127746,127748,127745,1098192,9.17770058854612E-19,499998,500000,29852,841831,371681,371686,5363,128318,128316,128317,1098480),byrow=TRUE,nrow=30)
# }
# \dontshow{
nvalid <- matrix(c(4.99592056104662E-19,499995,500003,52076,856074,408144,408147,213587,305440,305446,305445,1039792,9.0895286250952E-19,499992,500000,29946,842290,372229,372231,4384,132154,132160,132167,1097848,5.66871925230001E-19,499989,500008,43979,856199,400171,400175,177259,277086,277096,277095,1055869,9.13431925017368E-19,499997,499999,30174,842560,372728,372730,22234,128214,128213,128214,1097112),byrow=TRUE,nrow=4)
# }
calculate_TemporalGSSA(valid,1)
calculate_TemporalGSSA(valid,4)
calculate_TemporalGSSA(nvalid,2)