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oneGeneDel

Single Gene Deletion Experiment

Description

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

Usage

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

Arguments

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.

Details

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.

Value

An object of class optsol_genedel.

Author(s)

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

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

See Also

Examples

# 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")
sybil

Efficient Constrained Based Modelling

v2.1.5
GPL-3 | file LICENSE
Authors
Mayo Roettger [cre], Gabriel Gelius-Dietrich [aut], C. Jonathan Fritzemeier [ctb], Rajen Piernikarczyk [ctb], Marc Andre Daxer [ctb], Benjamin Braasch [ctb], Abdelmoneim Desouki [ctb], Martin J. Lercher [ctb]
Initial release
2019-03-07

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