oneGeneDel: Single Gene Deletion Experiment
In sybil: Efficient Constrained Based Modelling

Description Usage Arguments Details Value Author(s) See Also Examples

Predict the metabolic phenotype of single-gene knock out mutants.

  oneGeneDel(model, geneList,
             lb = rep(0, length(geneList)),
             ub = rep(0, length(geneList)),
             checkOptSolObj = FALSE, ...)

`model`	An object of class `modelorg`.
`geneList`	A character vector containing the set of genes to be deleted one by one. Default: `allGenes(model)`.
`lb`	A numeric vector of the same length as `geneList` containing the lower bounds for the reaction rates of reactions (variables) affected by the genes given in argument `geneList`. Default: `0` for all genes in `geneList`, simulating knock-out mutants.
`ub`	A numeric vector of the same length as `geneList` containing the upper bounds for the reaction rates of reactions (variables) affected by the genes given in argument `geneList`. Default: `0` for all genes in `geneList`, simulating knock-out mutants.
`checkOptSolObj`	A single logical value. If set to `TRUE`, a warning will be generated, if not all optimizations ended successful. Default: `FALSE`.
`...`	Further arguments passed to `optimizer`. Important ones are `algorithm` in order to set the algorithm to use or `solverParm` in order to set parameter values for the optimization software.

The function oneGeneDel studies the effect of genetic perturbations by single gene deletions on the phenotype of the metabolic network. The function performs n optimizations with n being the length of the character vector in argument geneList. For each gene deletion j the set of fluxes effected by the deletion of gene given in geneList[j] is constrained to zero flux. If the deletion of a certain gene has an effect, is tested with the function geneDel. Each optimization corresponds to the deletion of one gene.

An object of class optsol_genedel.

Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

modelorg, optsol, optsol_genedel, checkOptSol, optimizer and SYBIL_SETTINGS.

  # load example data set
  data(Ec_core)
  
  # compute phenotypes of genetic perturbations via
  # FBA (default)
  Ec_ogd <- oneGeneDel(Ec_core)
  
  # or MOMA (linearized version)
  Ec_ogd <- oneGeneDel(Ec_core, algorithm = "lmoma")