sybil (version 2.2.0)

sysBiolAlg_fv-class: Class "sysBiolAlg_fv"

Description

The class sysBiolAlg_fv holds an object of class '>optObj which is generated to meet the requirements of the flux variance algorithm.

Objects from the Class

Objects can be created by calls of the form

sysBiolAlg(model, algorithm = "fv", ...).

Arguments to ... which are passed to method initialize of class sysBiolAlg_fv are described in the Details section.

Slots

problem:

Object of class "optObj" containing the problem object.

algorithm:

Object of class "character" containing the name of the algorithm.

nr:

Object of class "integer" containing the number of rows of the problem object.

nc:

Object of class "integer" containing the number of columns of the problem object

fldind:

Object of class "integer" pointers to columns (variables) representing a flux (reaction) in the original network. The variable fldind[i] in the problem object represents reaction i in the original network.

alg_par:

Object of class "list" containing a named list containing algorithm specific parameters.

Extends

Class "'>sysBiolAlg", directly.

Methods

No methods defined with class "sysBiolAlg_fv" in the signature.

Details

The initialize method has the following arguments:

model

An object of class '>modelorg.

percentage

Consider solutions with x percent of the optimal solution. Default: 100.

Zopt

A single numeric value giving the optimal value to be fixed during all other optimizations (see argument fixObjVal). If Zopt is set to NULL and model has an objective function, a default value is computed based on FBA. If given, arguments solver, method and solverParm are used during FBA. Default: NULL.

fixObjVal

A single Boolean value. If set to TRUE and if the model contains an objective function, an optimal value of this objective function will be fixed during all other optimizations. The optimal value can be controlled by argument Zopt. Default: TRUE.

tol

Single numeric value giving the tolerance value. Default: SYBIL_SETTINGS("TOLERANCE").

lpdir

Single character string containing the direction of optimization. Can be set to "min" or "max". Default: SYBIL_SETTINGS("OPT_DIRECTION").

useNames

A single boolean value. If set to TRUE, variables and constraints will be named according to cnames and rnames. If set to NULL, no specific variable or constraint names are set. Default: SYBIL_SETTINGS("USE_NAMES").

cnames

A character vector giving the variable names. If set to NULL, the reaction id's of model are used. Default: NULL.

rnames

A character vector giving the constraint names. If set to NULL, the metabolite id's of model are used. If an objective value has to be fixed (see argument fixObjVal), the corresponding constrained is named "Z". Default: NULL.

pname

A single character string containing a name for the problem object. Default: NULL.

scaling

Scaling options used to scale the constraint matrix. If set to NULL, no scaling will be performed (see scaleProb). Default: NULL.

writeProbToFileName

A single character string containing a file name to which the problem object will be written in LP file format. Default: NULL.

...

Further arguments passed to the initialize method of '>sysBiolAlg. They are solver, method and solverParm.

The problem object is built to be capable to perform the flux variance algorithm with a given model, which is basically the solution of a linear program $$% \begin{array}{rll}% \max \textrm{ or } \min & v_i \\[1ex] \mathrm{s.\,t.} & Z = Z_{\mathrm{opt}} \\[1ex] & \mbox{\boldmath$Sv$\unboldmath} = 0 \\[1ex] & \alpha_i \leq v_i \leq \beta_i & \quad \forall i \in \{1, \ldots, n\} \\[1ex] \end{array}% $$ with \(\bold{S}\) being the stoichiometric matrix, \(\alpha_i\) and \(\beta_i\) being the lower and upper bounds for flux (variable) \(i\). The total number of variables of the optimization problem is denoted by \(n\). The optimization can be executed by using optimizeProb.

References

Becker, S. A., Feist, A. M., Mo, M. L., Hannum, G., Palsson, B. <U+00D8>. and Herrgard, M. J. (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2, 727--738.

Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., Kang, J., Hyduke, D. R. and Palsson, B. <U+00D8>. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6, 1290--1307.

Bernhard <U+00D8>. Palsson (2006). Systems Biology: Properties of Reconstructed Networks. Cambridge University Press.

See Also

Constructor function sysBiolAlg and superclass '>sysBiolAlg.

Examples

# NOT RUN {
  showClass("sysBiolAlg_fv")
# }