"sysBiolAlg_moma"sysBiolAlg_moma holds an object of class
optObj which is generated to meet the
requirements of the MOMA algorithm.sysBiolAlg(model, algorithm = "moma", ...).
Arguments to ... which are passed to method initialize of class
sysBiolAlg_moma are described in the Details section."sysBiolAlg ", directly.initialize method has the following arguments:
[object Object],[object Object],[object Object],[object Object] The problem object is built to be capable to perform the MOMA algorithm with
a given model, which is basically the solution of a quadratic programming
problem
$$\begin{array}{rll} \min & \begin{minipage}[b]{5em}
\[
\sum_{i,j=1}^n
\bigl(v_{j,\mathrm{del}} - v_{i,\mathrm{wt}}\bigr)^2
\]
\end{minipage} \[2em]
\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 $\bold{S}$ beeing the stoichiometric matrix, $\alpha_i$
and $\beta_i$ beeing 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,
$\mbox{\boldmath$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.
The optimization can be executed by using optimizeProb.
sysBiolAlg and
superclass sysBiolAlg.