"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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