sybil (version 2.2.0)

robAna: Robustness Analysis

Description

Performs robustness analysis for a given metabolic model.

Usage

robAna(model, ctrlreact, rng = NULL,
         numP = 20, verboseMode = 1, ...)

Arguments

model

An object of class '>modelorg.

ctrlreact

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

rng

A numeric vector of length two, giving the lower and upper bound of the control reaction. If set to NULL (the default), the range will be computed by flux variability analysis for the reaction given in ctrlreact. Default: NULL

numP

The number of points to analyse. Default: 20

verboseMode

An integer value indicating the amount of output to stdout, see optimizer for details. Default: 1.

…

Further arguments passed to optimizer.

Value

An object of class '>optsol_robAna.

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 \(\bold{S}\) being the stoichiometric matrix, \(\alpha_i\) and \(\beta_i\) being 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. <U+00D8>. 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. <U+00D8>. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6, 1290--1307.

Bernhard <U+00D8>. Palsson (2006). Systems Biology: Properties of Reconstructed Networks. Cambridge University Press.

Examples

# NOT RUN {
  data(Ec_core)
  rb <- robAna(Ec_core, ctrlreact = "EX_o2(e)")
  plot(rb)
# }