"sysBiolAlg_fba"The class sysBiolAlg_fba holds an object of class
'>optObj which is generated to meet the
requirements of the FBA algorithm.
Objects can be created by calls of the form
sysBiolAlg(model, algorithm = "fba", ...).
Arguments to ... which are passed to method initialize of class
sysBiolAlg_fba are described in the Details section.
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.
No methods defined with class "sysBiolAlg_fba" in the signature.
The initialize method has the following arguments:
Single character string containing the direction of optimization.
Can be set to "min" or "max".
Default: "max".
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").
A character vector giving the variable names. If set to NULL,
the reaction id's of model are used.
Default: NULL.
A character vector giving the constraint names. If set to NULL,
the metabolite id's of model are used.
Default: NULL.
A single character string containing a name for the problem object.
Default: NULL.
Scaling options used to scale the constraint matrix. If set to
NULL, no scaling will be performed
(see scaleProb).
Default: NULL.
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 flux balance analysis
(FBA) with a given model, which is basically the solution of a linear
programming problem
$$%
\begin{array}{rll}%
\max & \mbox{\boldmath$c$\unboldmath}^{\mathrm{T}}
\mbox{\boldmath$v$\unboldmath} \\[1ex]
\mathrm{s.\,t.} & \mbox{\boldmath$Sv$\unboldmath} = 0 \\[1ex]
& \alpha_i \leq v_i \leq \beta_i
& \quad \forall i \in \{1, \ldots, n\} \\[1ex]
\end{array}%
$$
with \(\bold{S}\) being the stoichiometric matrix, \(\alpha_i\)
and \(\beta_i\) being the lower and upper bounds for flux (variable)
\(i\) respectively. The total number of variables of the optimization
problem is denoted by \(n\). The solution of the optimization is a flux
distribution maximizing the objective function
\(
\mbox{\boldmath$c$\unboldmath}^{\mathrm{T}}
\mbox{\boldmath$v$\unboldmath}
\) under the a given environment and the assumption of steady state.
The optimization can be executed by using optimizeProb.
Edwards, J. S., Covert, M and Palsson, B. <U+00D8>. (2002) Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 4, 133--140.
Edwards, J. S., Ibarra, R. U. and Palsson, B. <U+00D8>. (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19, 125--130.
Constructor function sysBiolAlg and
superclass '>sysBiolAlg.
# NOT RUN {
showClass("sysBiolAlg_fba")
# }
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