"sysBiolAlg_room"sysBiolAlg_room holds an object of class
optObj which is generated to meet the
requirements of the ROOM algorithm.sysBiolAlg(model, algorithm = "room", ...).
Arguments to ... which are passed to method initialize of class
sysBiolAlg_room are described in the Details section."sysBiolAlg ", directly.initialize method has the following arguments:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] The problem object is built to be capable to perform the ROOM algorithm with
a given model, which is basically the solution of a mixed integer programming
problem
$$\begin{array}{rll} \min & \begin{minipage}[b]{5em}
\[
\sum_{i=1}^n y_i
\]
\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]
& v_i - y(\beta_i - w_i^u) \leq w_i^u \[1ex]
& v_i - y(\alpha_i - w_i^l) \geq w_i^l \[1ex]
& y_i \in {0, 1} \[1ex]
& w_i^u = w_i + \delta |w_i| + \epsilon \[1ex]
& w_i^l = w_i - \delta |w_i| - \epsilon \[1ex]
\end{array}$$
with $\bold{S}$ beeing the stoichiometric matrix, $\alpha_i$
and $\beta_i$ beeing the lower and upper bounds for flux (variable)
$i$.
The total number of fluxes of the optimization problem is denoted by $n$.
Here, $w$ 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.
All variables $y_i$ are binary, with $y_i = 1$ for a significant flux
change in $v_i$ and $y_i = 0$ otherwise. Thresholds determining the
significance of a flux change are given in $w^u$ and $w^l$, with
$\delta$ and $\epsilon$ specifying absolute and
relative ranges in tolerance [Shlomi et al. 2005].
The boolean argument LPvariant relax the binary contraints to
$0 \leq y_i \leq 1$ so that the problem becomes a linear
program.
The optimization can be executed by using optimizeProb.
sysBiolAlg and
superclass sysBiolAlg.