"sysBiolAlg_lmoma"The class sysBiolAlg_lmoma holds an object of class
'>optObj which is generated to meet the
requirements of a lineraized versoin of the MOMA algorithm.
Objects can be created by calls of the form
sysBiolAlg(model, algorithm = "lmoma", ...).
Arguments to ... which are passed to method initialize of class
sysBiolAlg_lmoma 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_lmoma" in the signature.
The initialize method has the following arguments:
A numeric vector holding an optimal wild type flux distribution for the
given model. If missing, a default value is computed based on FBA.
If given, arguments solver and method are used, but
solverParm is not.
Boolean, prepare problem object in order to perform minimization of
metabolic adjustment as in COBRA Toolbox.
Default: FALSE.
Only used if argument COBRAflag is set to TRUE:
A single numeric value giving the optimized value of the objective
function of the wild type problem. If missing, a default
value is computed based on FBA. If given, arguments solver and
method are used, but solverParm is not.
Only used if argument COBRAflag is set to TRUE:
Boolean. If set to TRUE, the value of argument wtobj is
treated as lower bound. If set to FALSE, wtobj serves as
an upper bound.
Default: TRUE.
A numeric vector of length two times the number of reactions in the model
containing the non-zero part of the objective function. If set to
NULL, the vector is filled with ones.
Default: NULL.
A single numerical value used as a maximum value for upper variable
and contraint bounds.
Default: SYBIL_SETTINGS("MAXIMUM").
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 a linearized version of
the MOMA algorithm with a given model, which is basically the solution of a
linear programming problem
$$%
\begin{array}{rll}%
\min & \begin{minipage}[b]{5em}
\[
\sum_{i,j=1}^n
\bigl|v_{j,\mathrm{del}} - v_{i,\mathrm{wt}}\bigr|
\]
\end{minipage} \\[2em]
\mathrm{s.\,t.} & \mbox{\boldmath$Sv$\unboldmath}_{\mathrm{del}} = 0
\\[1ex]
& v_i = v_{i,\mathrm{wt}}
& \quad \forall i \in \{1, \ldots, n\} \\[1ex]
& \alpha_j \leq v_{j,\mathrm{del}} \leq \beta_j
& \quad \forall j \in \{1, \ldots, n\} \\[1ex]
\end{array}%
$$
Here,
\(
\mbox{\boldmath$v$\unboldmath}_{\mathrm{wt}}
\)
is the optimal wild type flux distribution. This can be set via the argument
wtflux. If wtflux is NULL (the default), the
wild type flux distribution will be calculated by a standard FBA.
If argument COBRAflag is set to TRUE, the linear programm is
formulated differently. Wild type and knock-out strain will be computed
simultaneously.
$$%
\begin{array}{rll}%
\min & \begin{minipage}[b]{5em}
\[
\sum_{i,j=1}^n
\bigl|v_{j,\mathrm{del}} - v_{i,\mathrm{wt}}\bigr|
\]
\end{minipage} \\[2em]
\mathrm{s.\,t.} & \mbox{\boldmath$Sv$\unboldmath}_{\mathrm{wt}} = 0
\\[1ex]
& \alpha_i \leq v_{i,\mathrm{wt}} \leq \beta_i
& \quad \forall i \in \{1, \ldots, n\} \\[1ex]
& \mbox{\boldmath$Sv$\unboldmath}_{\mathrm{del}} = 0
\\[1ex]
& \alpha_j \leq v_{j,\mathrm{del}} \leq \beta_j
& \quad \forall j \in \{1, \ldots, n\} \\[1ex]
& \mbox{$\mu$}_{\mathrm{wt}} =
\mbox{\boldmath$c$\unboldmath}^{\mathrm{T}}
\mbox{\boldmath$v$\unboldmath}_{\mathrm{wt}} \\[1ex]
\end{array}%
$$
with \(\bold{S}\) being the stoichiometric matrix, \(\alpha_i\)
and \(\beta_i\) being the lower and upper bounds for flux (variable)
\(i\) (\(j\) for the deletion strain).
The total number of variables of the optimization problem is denoted
by \(n\).
Here,
\(
\mu_{\mathrm{wt}}
\)
is the optimal wild type growth rate. This can be set via the argument
wtobj. If wtobj is NULL (the default), the
wild type growth rate will be calculated by a standard FBA.
The optimization can be executed by using optimizeProb.
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.
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.
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.
Segr<U+00E8>, D., Vitkup, D. and Church, G. M. (2002) Analysis or optimality in natural and pertubed metabolic networks. PNAS 99, 15112--15117.
Constructor function sysBiolAlg and
superclass '>sysBiolAlg.
# NOT RUN {
showClass("sysBiolAlg_lmoma")
# }
Run the code above in your browser using DataLab