sybil (version 1.1.2)

sysBiolAlg_lmoma-class: Class "sysBiolAlg_lmoma"

Description

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.

encoding

utf8

Objects from the Class

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.

Extends

Class "sysBiolAlg", directly.

Methods

No methods defined with class "sysBiolAlg_lmoma" in the signature.

Details

The 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 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 $\bold{S}$ 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, $\mu_{\mathrm{wt}}$ 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.

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.

Edwards, J. S., Covert, M and Palsson, B. Ø. (2002) Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 4, 133--140. Edwards, J. S., Ibarra, R. U. and Palsson, B. Ø. (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. Ø. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6, 1290--1307.

Segrè, D., Vitkup, D. and Church, G. M. (2002) Analysis or optimality in natural and pertubed metabolic networks. PNAS 99, 15112--15117.

See Also

Constructor function sysBiolAlg and superclass sysBiolAlg.

Examples

showClass("sysBiolAlg_lmoma")