sybil (version 1.1.2)

robAna: Robustness Analysis

Description

Performs robustness analysis for a given model.

Usage

robAna(model,
         ctrlreact,
         numP = 20,
         verboseMode = 2, fld = FALSE, ...)

Arguments

model

An object of class modelorg.

ctrlreact

An object of class reactId, character or integer. Specifies the control reaction -- the parameter to vary.

numP

The number of points to analyse.

fld

Boolean. Save the resulting flux distribution. Default: FALSE

verboseMode

An integer value indicating the amount of output to stdout: 0: nothing, 1: status messages, 2: like 1 plus a progress indicator, 3: a table containing the control reaction id and the corresponding flux values. Default: 2.

...

Further arguments passed to sysBiolAlg.

Value

• An object of class optsol_robAna.

encoding

utf8

Details

The function robAna performs a robustness analysis with a given model. The flux of ctrlreact will be varied in numP steps between the maximum and minimum value the flux of ctrlreact can reach. For each of the numP datapoints the followong lp problem is solved $$\begin{array}{rll} \max & \mbox{\boldmath$c$\unboldmath}^{\mathrm{T}} \mbox{\boldmath$v$\unboldmath} \[1ex] \mathrm{s.\,t.} & \mbox{\boldmath$Sv$\unboldmath} = 0 \[1ex] & v_j = c_k \[1ex] & \alpha_i \leq v_i \leq \beta_i & \quad \forall i \in {1, \ldots, n}, i \neq j\[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 variables of the optimization problem is denoted by $n$. The parameter $c_k$ is varied numP times in the range of $v_{j,\mathrm{min}}$ to $v_{j,\mathrm{max}}$. The result of the optimization is returned as object of class optsol_robAna containing the objective value for each datapoint. The extreme points of the range for ctrlreact are calculated via flux balance analysis (see also sysBiolAlg_fba) with the objective function being minimization and maximization of the flux through ctrlreact.

References

Becker, S. A., Feist, A. M., Mo, M. L., Hannum, G., Palsson, B. Ø. and Herrgard, M. J. (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2, 727--738.

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. Ø. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6, 1290--1307.

Bernhard Ø. Palsson (2006). Systems Biology: Properties of Reconstructed Networks. Cambridge University Press.

Examples

data(Ec_core)
  rb <- robAna(Ec_core, ctrlreact = "EX_o2")
  plot(rb)