Error(s) in re-building vignettes:
--- re-building ‘sybil.Rnw’ using Sweave
Loading required package: Matrix
Loading required package: lattice

		Information on package ‘sybil’

Description:

Package:            sybil
Type:               Package
Title:              Efficient Constrained Based Modelling
Version:            2.1.5
Date:               2019-03-07
Authors@R:          c( person("Mayo", "Roettger", role = c("cre"),
                    email = "mayo.roettger@hhu.de"), person("Gabriel",
                    "Gelius-Dietrich", role = "aut"), person(c("C.",
                    "Jonathan"), "Fritzemeier", role = "ctb", email =
                    "clausjonathan.fritzemeier@uni-duesseldorf.de"),
                    person("Rajen", "Piernikarczyk", role = "ctb"),
                    person(c("Marc", "Andre"), "Daxer", role = "ctb"),
                    person("Benjamin", "Braasch", role = "ctb"),
                    person("Abdelmoneim", "Desouki", role = "ctb"),
                    person(c("Martin", "J."), "Lercher", role = "ctb"))
Maintainer:         Mayo Roettger <mayo.roettger@hhu.de>
Depends:            R (>= 3.2.0), Matrix, lattice
Imports:            methods
Suggests:           glpkAPI (>= 1.2.8), cplexAPI (>= 1.2.4), clpAPI (>=
                    1.2.4), lpSolveAPI (>= 5.5.2.0), parallel, grid
URL:                http://www.cs.hhu.de/lehrstuehle-und-arbeitsgruppen/computational-cell-biology/software/sybil.html
Description:        This Systems Biology Package (Gelius-Dietrich et.
                    al. (2012) <doi:10.1186/1752-0509-7-125>)
                    implements algorithms for constraint based analyses
                    of metabolic networks, e.g. flux-balance analysis
                    (FBA), minimization of metabolic adjustment (MOMA),
                    regulatory on/off minimization (ROOM), robustness
                    analysis and flux variability analysis. The package
                    is easily extendable for additional algorithms.
                    Most of the current LP/MILP solvers are supported
                    via additional packages.
LazyLoad:           yes
License:            GPL-3 | file LICENSE
Collate:            generics.R validmodelorg.R validoptsol.R
                    validreactId.R validreact.R reactClass.R
                    validreactId_Exch.R validsysBiolAlg.R
                    addAlgorithm.R addExchReact.R addReact.R
                    addSolver.R blockedReact.R bracket_pairs.R
                    ceilValues.R changeBounds.R changeGPR.R
                    changeObjFunc.R checkAlgorithm.R
                    checkDefaultMethod.R checkEmptyField.R
                    checkReactId.R check_brackets.R
                    createReactionString.R deadEndMetabolite.R
                    doInRound.R doubleFluxDel.R doubleGeneDel.R
                    doubleReact.R editEnvir.R findExchReact.R
                    floorValues.R fluxVar.R geneDel.R geneDeletion.R
                    generateFluxdels.R generateModKey.R generateWT.R
                    getsybilenv.R makeLPcompatible.R mod2irrev.R
                    modelorg2ExPA.R modelorg2text.R modelorg2tsv.R
                    multiDel.R oneFluxDel.R oneGeneDel.R
                    onlyChangeGPR.R onlyCheckGPR.R optObj_basicfunc.R
                    optObj_lpSolveAPIcompat.R optimizer.R
                    parseBoolean.R phpp.R ppProcessing.R
                    prepareSubSysMatrix.R printLogComment.R
                    printNamedList.R progress.R promptSysBiolAlg.R
                    recodeMatrix.R readTEXTmod.R readTSVmod.R
                    reassignFwBwMatch.R rmReact.R robAna.R settings.R
                    singletonMetabolite.R sybilStack.R ypd.R zzz.R
                    modelorgClass.R modelorg_irrevClass.R
                    optObj_pointer.R optObjClass.R optObj_clpAPIClass.R
                    optObj_cplexAPIClass.R optObj_glpkAPIClass.R
                    optObj_lpSolveAPIClass.R sybilErrorClass.R
                    ppProcClass.R netFluxClass.R
                    fluxDistributionClass.R reactIdClass.R
                    reactId_ExchClass.R optsolClass.R
                    optsol_blockedReactClass.R
                    optsol_optimizeProbClass.R optsol_fluxVarClass.R
                    optsol_fluxdelClass.R optsol_robAnaClass.R
                    optsol_phppClass.R optsol_genedelClass.R
                    checksolClass.R summaryOptsolClass.R
                    sysBiolAlgClass.R sysBiolAlg_fbaClass.R
                    sysBiolAlg_fbaEasyConstraintClass.R
                    sysBiolAlg_fvClass.R sysBiolAlg_lmomaClass.R
                    sysBiolAlg_momaClass.R sysBiolAlg_mtfClass.R
                    sysBiolAlg_mtfEasyConstraintClass.R
                    sysBiolAlg_roomClass.R sybilLogClass.R
                    upgradeModelorg.R mergeReact2Modelorg.R
Packaged:           2019-03-07 10:11:26 UTC; root
Author:             Mayo Roettger [cre], Gabriel Gelius-Dietrich [aut],
                    C. Jonathan Fritzemeier [ctb], Rajen Piernikarczyk
                    [ctb], Marc Andre Daxer [ctb], Benjamin Braasch
                    [ctb], Abdelmoneim Desouki [ctb], Martin J. Lercher
                    [ctb]
NeedsCompilation:   no
Repository:         CRAN
Date/Publication:   2019-03-07 14:10:03 UTC
Built:              R 4.0.0; ; 2019-10-06 00:50:39 UTC; unix

Index:

Ec_core                 Escherichia coli Core Metabolic Model
SYBIL_SETTINGS          Set and Get sybil Parameters
addAlgorithm            Add a New Algorithm Name to sybil
addCols-methods         Add Columns to an Optimization Problem
addColsToProb-methods   Add New Columns (Variables) to an Optimization
                        Problem
addExchReact            Add Exchange Reactions to a Model
addReact,modelorg-method
                        Add/Change Reactions in a Model
addRows-methods         Add Rows to an Optimization Problem
addRowsCols-methods     Add Rows and Columns to an Optimization Problem
addRowsToProb-methods   Add New Rows (Constraints) to an Optimization
                        Problem
addSolver               Add a New Mathematical Programming Solver to
                        sybil
applyChanges-methods    Generic Function to Apply Changes to Objects of
                        Class sysBiolAlg
backupProb-methods      Copies a Problem Object to a New Problem Object
blockedReact            Find Blocked Reactions in a Metabolic Network
changeBounds            Change Variable Bounds in a Metabolic Network
changeColsBnds-methods
                        Change Column (Variable) Bounds in the
                        Optimization Problem
changeColsBndsObjCoefs-methods
                        Change Column (Variable) Bounds and Objective
                        Coefficients in the Optimization Problem
changeGPR               Check and Change the GPR Rules
changeMatrixRow-methods
                        Change a Row in the Constraint Matrix of the
                        Optimization Problem
changeObjCoefs-methods
                        Change Column (Variable) Objective Coefficients
                        in the Optimization Problem
changeObjFunc           Sets/changes the Objective Function
changeRowsBnds-methods
                        Change Row Bounds in the Optimization Problem
changeUptake-methods    Change Uptake Reactions
checkAlgorithm          Check Algorithm
checkDefaultMethod      Validate Solver and Method
checkOptSol-methods     Summarized Information About an Object of Class
                        Optsol
checkReactId            Check if a Reaction Id is Valid
checkVersion-methods    checks Version of modelorg
checksol-class          Structure of the Class "checksol"
deadEndMetabolites-methods
                        Identify Dead End Metabolites
delProb-methods         Free Memory Associated to the Pointer to the
                        Problem Object
doubleFluxDel           Double Flux Deletion Experiment
doubleGeneDel           Double Gene Deletion Experiment
doubleReact             Identifies Identical Reactions
editEnvir               Environment Editor for Metabolic Networks
findExchReact           Find Exchange Reactions
fluxDistribution-class
                        Class '"fluxDistribution"'
fluxVar                 Flux Variability Analysis
geneDel                 Get Gene-Reaction Association
geneDeletion            Gene Deletion Experiments
getColPrim-methods      Get Primal Value of Variables After
                        Optimization
getColsLowBnds-methods
                        Get Lower Bounds of the Columns (Variables) of
                        the Optimization Problem
getColsNames-methods    Retrieve Variable Names
getColsUppBnds-methods
                        Get Upper Bounds of the Columns (Variables) of
                        the Optimization Problem
getFluxDist-methods     Retrieve Flux Distribution
getNumCols-methods      Get Number of Columns (Variables) of the
                        Optimization Problem
getNumNnz-methods       Retrieve the Number of Non-Zero Elements of the
                        Constraint Matrix
getNumRows-methods      Get Number of Rows (Constraints) of the
                        Optimization Problem
getObjCoefs-methods     Get Objective Coefficients of the Optimization
                        Problem
getObjDir-methods       Get Direction of Optimization.
getObjVal-methods       Get Value of the Objective Function After
                        Optimization
getRedCosts-methods     Get Reduced Costs of all Variables After
                        Optimization
getRowsLowBnds-methods
                        Get Lower Bounds of the Rows (Constraints) of
                        the Optimization Problem
getRowsNames-methods    Retrieve Constraint Names
getRowsUppBnds-methods
                        Get Upper Bounds of the Rows (Constraints) of
                        the Optimization Problem
getSolStat-methods      Get Solution Status After Optimization
getSolverParm-methods   Retrieve Current Parameter Settings Used By The
                        Optimization Software
getsybilenv             Print sybil Environment
initProb-methods        Initialize Problem Object
loadLPprob-methods      Load Data to Optimization Problem
loadQobj-methods        Load Quadratic Part of the Objective Function
                        to the Optimization Problem
makeOptsolMO            Constructor Function for Objects of Class
                        'optsol_optimizeProb'.
mergeReact2Modelorg     Functions to subset and merge modelorg objects.
mod2irrev               Produces a Model in Irreversible Format
modelorg-class          Structure of Class "modelorg"
modelorg2ExPA           Write an Instance of Class modelorg to File in
                        ExPA Format
modelorg2tsv            Write an Instance of Class modelorg to File
modelorg_irrev-class    Class for Metabolic Networks in Irreversible
                        Format.
multiDel                Parallel Support for sybil
netFlux-class           Class '"netFlux"'
oneFluxDel              Single Flux Deletion Experiment
oneGeneDel              Single Gene Deletion Experiment
onlyChangeGPR           Change the GPR Rules
onlyCheckGPR            Check the GPR Rules
optObj                  General Constructor Function For Objects of
                        Class 'optObj'
optObj-class            Class '"optObj"'
optObj_clpAPI-class     Class '"optObj_clpAPI"'
optObj_cplexAPI-class   Class '"optObj_cplexAPI"'
optObj_glpkAPI-class    Class '"optObj_glpkAPI"'
optObj_lpSolveAPI-class
                        Class '"optObj_lpSolveAPI"'
optimizeProb-methods    Optimize Problem Object
optimizer               Performs Series of Optimizations
optsol-class            Class optsol
optsol_blockedReact-class
                        Class '"optsol_blockedReact"'
optsol_fluxVar-class    Class '"optsol_fluxVar"'
optsol_fluxdel-class    Class '"optsol_fluxdel"'
optsol_genedel-class    Class '"optsol_genedel"'
optsol_optimizeProb-class
                        Class '"optsol_optimizeProb"'
optsol_phpp-class       Class '"optsol_phpp"'
optsol_robAna-class     Class '"optsol_robAna"'
phpp                    Phenotypic Phase Plane Analysis
ppProc-class            Class '"ppProc"'
printMetabolite-methods
                        Print Rows of the Stoichiometric Matrix
printReaction-methods   Print Columns of the Stoichiometric Matrix
promptSysBiolAlg        Generate A Skeletal Structure of Subclasses of
                        sysBiolAlg
reactId-class           Structure of Class "reactId"
reactId_Exch-class      Class '"reactId_Exch"'
readProb-methods        Read Problem Object From File
readTSVmod              Read a Metabolic Network in a TSV (CSV) Format
resetChanges-methods    Generic Function to Reset Temporary Changes in
                        Objects of Class sysBiolAlg
rmReact                 Remove Reactions From a Model
robAna                  Robustness Analysis
scaleProb-methods       Scaling of the Constraint Matrix of an
                        Optimization Problem
sensitivityAnalysis-methods
                        Sensitivity Analysis
setColsNames-methods    Set/Change Variable Names
setObjDir-methods       Set Direction of Optimization
setRhsZero-methods      Set Right Hand Side of the Optimization Problem
                        To Zero
setRowsNames-methods    Set/Change Constraint Names
setSolverParm-methods   Set Parameters Used By The Optimization
                        Software
shrinkMatrix-methods    Get a Subset of Matrix Like Objects
singletonMetabolites-methods
                        Identify Singleton Metabolites
solveLp-methods         Optimize Problem Object
summaryOptsol           Summarize Objects of Class Optsol
summaryOptsol-class     Class '"summaryOptsol"'
sybil-deprecated        Deprecated Functions and Methods in Package
                        'sybil'
sybil-package           sybil - Efficient Constrained Based Modelling
                        in R
sybilError-class        Class '"sybilError"'
sybilLog-class          Class '"sybilLog"'
sybilStack              A Data Type Providing Stack (LIFO) And Queue
                        (FIFO) Functionality
sysBiolAlg              General Constructor Function For Objects of
                        Class 'sysBiolAlg'
sysBiolAlg-class        Class '"sysBiolAlg"'
sysBiolAlg_fba-class    Class '"sysBiolAlg_fba"'
sysBiolAlg_fbaEasyConstraint-class
                        Class '"sysBiolAlg_fbaEasyConstraint"' and
                        Class '"sysBiolAlg_mtfEasyConstraint"'
sysBiolAlg_fv-class     Class '"sysBiolAlg_fv"'
sysBiolAlg_lmoma-class
                        Class '"sysBiolAlg_lmoma"'
sysBiolAlg_moma-class   Class '"sysBiolAlg_moma"'
sysBiolAlg_mtf-class    Class '"sysBiolAlg_mtf"'
sysBiolAlg_room-class   Class '"sysBiolAlg_room"'
upgradeModelorg         Upgrade modelorg to newer version.
writeProb-methods       Write Problem Object to File
ypd                     In Sillico YPD Medium

Further information is available in the following vignettes in
directory
‘/data/gannet/ripley/R/packages/tests-clang/sybil.Rcheck/sybil/doc’:

sybil: sybil -- Efficient Constrained Based Modelling in R (source,
        pdf)

doubleGeneDel              package:sybil               R Documentation

_D_o_u_b_l_e _G_e_n_e _D_e_l_e_t_i_o_n _E_x_p_e_r_i_m_e_n_t

_D_e_s_c_r_i_p_t_i_o_n:

     Predict the metabolic phenotype of of double-gene knock out
     mutants.

_U_s_a_g_e:

       doubleGeneDel(model, geneList1, geneList2, lb = NULL, ub = NULL,
                     allComb = FALSE, exLethal = TRUE,
                     tol = SYBIL_SETTINGS("TOLERANCE"),
                     checkOptSolObj = FALSE, ...)
     
_A_r_g_u_m_e_n_t_s:

   model: An object of class ‘modelorg’.

geneList1: A character vector containing the set of genes to be
          deleted.
          Default: ‘allGenes(model)’.

geneList2: A character vector containing the set of genes to be
          deleted.
          Default: ‘allGenes(model)’.

      lb: A numeric vector containing the lower bounds for the reaction
          rates of reactions (variables) affected by the genes given in
          arguments ‘geneList1’ and ‘geneList2’. If set to ‘NULL’, all
          reactions affected will be constrained to zero.
          Default: ‘NULL’.

      ub: A numeric vector containing the upper bounds for the reaction
          rates of reactions (variables) affected by the genes given in
          arguments ‘geneList1’ and ‘geneList2’. If set to ‘NULL’, all
          reactions affected will be constrained to zero.
          Default: ‘NULL’.

 allComb: A single Boolean value. If set to ‘TRUE’, every possible
          pairwise combination of genes given in arguments ‘geneList1’
          and ‘geneList2’ will be knocked-out. If set to ‘FALSE’,
          arguments ‘geneList1’ and ‘geneList2’ must have the same
          length. The knock-outs will be computed pair-wise: first
          ‘geneList1[1]’ and ‘geneList2[1]’, second ‘geneList1[2]’ and
          ‘geneList2[2]’ and so on.
          Default: ‘FALSE’.

exLethal: A single Boolean value. If set to ‘TRUE’, lethal genes are
          removed from the analysis. A unique set of genes in
          ‘geneList1’ and ‘geneList2’ will be scanned for lethal genes.
          A particular gene i is considered as lethal, if the deletion
          of this gene results in a zero flux rate in the objective
          function given in ‘model’.  Default: ‘TRUE’.

     tol: A single numeric value, containing an absolute threshold
          value for a gene being lethal or not.
          Default: ‘SYBIL_SETTINGS("TOLERANCE")’.

checkOptSolObj: A single logical value. If set to ‘TRUE’, a warning
          will be generated, if not all optimizations ended successful.
          Default: ‘FALSE’.

     ...: Further arguments passed to ‘optimizer’.  Important ones are
          ‘algorithm’ in order to set the algorithm to use or
          ‘solverParm’ in order to set parameter values for the
          optimization software.

_D_e_t_a_i_l_s:

     The function ‘doubleGeneDel’ studies the effect of genetic
     perturbations by double gene deletions on the phenotype of the
     metabolic network. The function performs n optimizations with n
     being either the length of the character vector in argument
     ‘geneList1’ times the length of the character vector in argument
     ‘geneList2’, if argument ‘allComb’ is set to ‘TRUE’, or the length
     of one of these vectors if argument ‘allComb’ is set to ‘FALSE’.
     For each gene deletion i,j the set of fluxes effected by the
     simultaneous deletion of genes i and j is constrained to zero
     flux. If the deletion of a certain pair of genes has an effect, is
     tested with the function ‘geneDel’. Each optimization corresponds
     to the simultaneous deletion of two genes.

_V_a_l_u_e:

     An object of class ‘optsol_genedel’.

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_S_e_e _A_l_s_o:

     ‘modelorg’, ‘optsol’, ‘optsol_genedel’, ‘checkOptSol’, ‘optimizer’
     and ‘SYBIL_SETTINGS’.

_E_x_a_m_p_l_e_s:

     ## Not run:
     
       ## compute all possible pairwise gene deletions
       # load example data set
       data(Ec_core)
       
       # compute all possible pairwise gene deletions via
       # FBA (default)
       Ec_dgd <- doubleGeneDel(Ec_core, allComb = TRUE)
       
       # or MOMA (linearized version)
       Ec_dgd <- doubleGeneDel(Ec_core,
                               allComb = TRUE,
                               algorithm = "lmoma")
     ## End(Not run)
     

Help files with alias or concept or title matching ‘flux variability
analysis’ using fuzzy matching:


BacArena::fluxVarSim    Function to compute flux variability analysis
                        on an simulation object to get min/max of
                        substance usage
abcdeFBA::Flux Variability Analysis
                        FLUX_VAR_ANALYSIS, a function to perform a flux
                        variability analysis.
  Aliases: Flux Variability Analysis
sybil::fluxVar          Flux Variability Analysis


Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.




reading model description, ... OK
reading metabolite list ... OK
parsing reaction list ... OK
GPR mapping ... OK
sub systems ... OK
prepare modelorg object ... OK
validating object ... OK
Loading required package: glpkAPI
using GLPK version 4.65
sysBiolAlg_fba-class           package:sybil           R Documentation

_C_l_a_s_s '"_s_y_s_B_i_o_l_A_l_g__f_b_a"'

_D_e_s_c_r_i_p_t_i_o_n:

     The class ‘sysBiolAlg_fba’ holds an object of class ‘optObj’ which
     is generated to meet the requirements of the FBA algorithm.

_D_e_t_a_i_l_s:

     The ‘initialize’ method has the following arguments:

     model An object of class ‘modelorg’.

     lpdir Single character string containing the direction of
          optimization.  Can be set to ‘"min"’ or ‘"max"’.
          Default: ‘"max"’.

     useNames A single boolean value. If set to ‘TRUE’, variables and
          constraints will be named according to ‘cnames’ and ‘rnames’.
          If set to ‘NULL’, no specific variable or constraint names
          are set.
          Default: ‘SYBIL_SETTINGS("USE_NAMES")’.

     cnames A character vector giving the variable names. If set to
          ‘NULL’, the reaction id's of ‘model’ are used.
          Default: ‘NULL’.

     rnames A character vector giving the constraint names. If set to
          ‘NULL’, the metabolite id's of ‘model’ are used.
          Default: ‘NULL’.

     pname A single character string containing a name for the problem
          object.
          Default: ‘NULL’.

     scaling Scaling options used to scale the constraint matrix. If
          set to ‘NULL’, no scaling will be performed (see
          ‘scaleProb’).
          Default: ‘NULL’.

     writeProbToFileName A single character string containing a file
          name to which the problem object will be written in LP file
          format.
          Default: ‘NULL’.

     ...  Further arguments passed to the initialize method of
          ‘sysBiolAlg’. They are ‘solver’, ‘method’ and ‘solverParm’.

     The problem object is built to be capable to perform flux balance
     analysis (FBA) with a given model, which is basically the solution
     of a linear programming problem

           max  c^T v
           s.t. Sv = 0
                a_i <= v_i <= b_i  for i = 1, ..., n
     
     with S being the stoichiometric matrix, a_i and b_i being the
     lower and upper bounds for flux (variable) i respectively.  The
     total number of variables of the optimization problem is denoted
     by n.  The solution of the optimization is a flux distribution
     maximizing the objective function c^T v under the a given
     environment and the assumption of steady state.  The optimization
     can be executed by using ‘optimizeProb’.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form

     ‘sysBiolAlg(model, algorithm = "fba", ...)’.

     Arguments to ‘...’ which are passed to method ‘initialize’ of
     class ‘sysBiolAlg_fba’ are described in the Details section.

_S_l_o_t_s:

     ‘problem’: Object of class ‘"optObj"’ containing the problem
          object.

     ‘algorithm’: Object of class ‘"character"’ containing the name of
          the algorithm.

     ‘nr’: Object of class ‘"integer"’ containing the number of rows of
          the problem object.

     ‘nc’: Object of class ‘"integer"’ containing the number of columns
          of the problem object

     ‘fldind’: Object of class ‘"integer"’ pointers to columns
          (variables) representing a flux (reaction) in the original
          network. The variable ‘fldind[i]’ in the problem object
          represents reaction ‘i’ in the original network.

     ‘alg_par’: Object of class ‘"list"’ containing a named list
          containing algorithm specific parameters.

_E_x_t_e_n_d_s:

     Class ‘"sysBiolAlg"’, directly.

_M_e_t_h_o_d_s:

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

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     Constructor function ‘sysBiolAlg’ and superclass ‘sysBiolAlg’.

_E_x_a_m_p_l_e_s:

       showClass("sysBiolAlg_fba")
     

sysBiolAlg_mtf-class           package:sybil           R Documentation

_C_l_a_s_s '"_s_y_s_B_i_o_l_A_l_g__m_t_f"'

_D_e_s_c_r_i_p_t_i_o_n:

     The class ‘sysBiolAlg_mtf’ holds an object of class ‘optObj’ which
     is generated to meet the requirements of the minimize total flux
     algorithm: minimize the absolute sum of all fluxes given a
     previously calculated objective value.

_D_e_t_a_i_l_s:

     The ‘initialize’ method has the following arguments:

     model An object of class ‘modelorg’.

     wtobj A single numeric value giving the optimal value. If missing,
          a default value is computed based on FBA. If given, arguments
          ‘solver’ and ‘method’ are used, but ‘solverParm’ is not.
          Default: ‘NULL’.

     react Arguments ‘react’, ‘lb’ and ‘ub’ are used, if argument
          ‘wtobj’ is ‘NULL’, meaning: no previous objective value is
          given. Objective values will be calculated via ‘fba’ using
          the parameters given in ‘react’, ‘lb’ and ‘ub’.
          Default: ‘NULL’.

     lb See argument ‘react’.
          Default: ‘NULL’.

     ub See argument ‘react’.
          Default: ‘NULL’.

     costcoeffw A numeric vector containing cost coefficients for all
          variables (forward direction). If set to ‘NULL’, all cost
          coefficients are set to ‘1’, so that all variables have the
          same impact on the objective function.
          Default: ‘NULL’.

     costcoefbw A numeric vector containing cost coefficients for all
          variables (backward direction). If set to ‘NULL’, all cost
          coefficients are set to the values given in ‘costcoeffw’.
          Default: ‘NULL’.

     absMAX A single numerical value used as a maximum value for upper
          variable and contraint bounds.
          Default: ‘SYBIL_SETTINGS("MAXIMUM")’.

     useNames A single boolean value. If set to ‘TRUE’, variables and
          constraints will be named according to ‘cnames’ and ‘rnames’.
          If set to ‘NULL’, no specific variable or constraint names
          are set.
          Default: ‘SYBIL_SETTINGS("USE_NAMES")’.

     cnames A character vector giving the variable names. If set to
          ‘NULL’, the reaction id's of ‘model’ are used.
          Default: ‘NULL’.

     rnames A character vector giving the constraint names. If set to
          ‘NULL’, the metabolite id's of ‘model’ are used.
          Default: ‘NULL’.

     pname A single character string containing a name for the problem
          object.
          Default: ‘NULL’.

     scaling Scaling options used to scale the constraint matrix. If
          set to ‘NULL’, no scaling will be performed (see
          ‘scaleProb’).
          Default: ‘NULL’.

     writeProbToFileName A single character string containing a file
          name to which the problem object will be written in LP file
          format.
          Default: ‘NULL’.

     ...  Further arguments passed to the initialize method of
          ‘sysBiolAlg’. They are ‘solver’, ‘method’ and ‘solverParm’.

     The problem object is built to be capable to perform minimize
     total flux with a given model, which is basically the solution of
     a linear programming problem

           min  sum cost_i abs(v_i)  for i = 1, ..., n
           s.t. Sv = 0
                a_i <= v_i <= b_i  for i = 1, ..., n
                c_wt >= c^T v_wt
     
     with c^T v_wt being the previously computed optimized value of the
     objective function (argument ‘wtobj’).  The variable S denotes the
     stoichiometric matrix, a_i and b_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 optimization can be
     executed by using ‘optimizeProb’.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form

     ‘sysBiolAlg(model, algorithm = "mtf", ...)’.

     Arguments to ‘...’ which are passed to method ‘initialize’ of
     class ‘sysBiolAlg_mtf’ are described in the Details section.

_S_l_o_t_s:

     ‘maxobj’: Object of class ‘"numeric"’ containing optimized
          objective values.

     ‘problem’: Object of class ‘"optObj"’ containing the problem
          object.

     ‘algorithm’: Object of class ‘"character"’ containing the name of
          the algorithm.

     ‘nr’: Object of class ‘"integer"’ containing the number of rows of
          the problem object.

     ‘nc’: Object of class ‘"integer"’ containing the number of columns
          of the problem object

     ‘fldind’: Object of class ‘"integer"’ pointers to columns
          (variables) representing a flux (reaction) in the original
          network. The variable ‘fldind[i]’ in the problem object
          represents reaction ‘i’ in the original network.

     ‘alg_par’: Object of class ‘"list"’ containing a named list
          containing algorithm specific parameters.

_E_x_t_e_n_d_s:

     Class ‘"sysBiolAlg"’, directly.

_M_e_t_h_o_d_s:

     changeMaxObj ‘signature(object = "sysBiolAlg_mtf")’: change
          current objective value to the jth value given in slot
          ‘maxobj’. Argument ‘j’ must be in ‘[1:length(maxobj)]’.

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     Constructor function ‘sysBiolAlg’ and superclass ‘sysBiolAlg’.

_E_x_a_m_p_l_e_s:

       showClass("sysBiolAlg_mtf")
     

sysBiolAlg_fba-class           package:sybil           R Documentation

_C_l_a_s_s '"_s_y_s_B_i_o_l_A_l_g__f_b_a"'

_D_e_s_c_r_i_p_t_i_o_n:

     The class ‘sysBiolAlg_fba’ holds an object of class ‘optObj’ which
     is generated to meet the requirements of the FBA algorithm.

_D_e_t_a_i_l_s:

     The ‘initialize’ method has the following arguments:

     model An object of class ‘modelorg’.

     lpdir Single character string containing the direction of
          optimization.  Can be set to ‘"min"’ or ‘"max"’.
          Default: ‘"max"’.

     useNames A single boolean value. If set to ‘TRUE’, variables and
          constraints will be named according to ‘cnames’ and ‘rnames’.
          If set to ‘NULL’, no specific variable or constraint names
          are set.
          Default: ‘SYBIL_SETTINGS("USE_NAMES")’.

     cnames A character vector giving the variable names. If set to
          ‘NULL’, the reaction id's of ‘model’ are used.
          Default: ‘NULL’.

     rnames A character vector giving the constraint names. If set to
          ‘NULL’, the metabolite id's of ‘model’ are used.
          Default: ‘NULL’.

     pname A single character string containing a name for the problem
          object.
          Default: ‘NULL’.

     scaling Scaling options used to scale the constraint matrix. If
          set to ‘NULL’, no scaling will be performed (see
          ‘scaleProb’).
          Default: ‘NULL’.

     writeProbToFileName A single character string containing a file
          name to which the problem object will be written in LP file
          format.
          Default: ‘NULL’.

     ...  Further arguments passed to the initialize method of
          ‘sysBiolAlg’. They are ‘solver’, ‘method’ and ‘solverParm’.

     The problem object is built to be capable to perform flux balance
     analysis (FBA) with a given model, which is basically the solution
     of a linear programming problem

           max  c^T v
           s.t. Sv = 0
                a_i <= v_i <= b_i  for i = 1, ..., n
     
     with S being the stoichiometric matrix, a_i and b_i being the
     lower and upper bounds for flux (variable) i respectively.  The
     total number of variables of the optimization problem is denoted
     by n.  The solution of the optimization is a flux distribution
     maximizing the objective function c^T v under the a given
     environment and the assumption of steady state.  The optimization
     can be executed by using ‘optimizeProb’.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form

     ‘sysBiolAlg(model, algorithm = "fba", ...)’.

     Arguments to ‘...’ which are passed to method ‘initialize’ of
     class ‘sysBiolAlg_fba’ are described in the Details section.

_S_l_o_t_s:

     ‘problem’: Object of class ‘"optObj"’ containing the problem
          object.

     ‘algorithm’: Object of class ‘"character"’ containing the name of
          the algorithm.

     ‘nr’: Object of class ‘"integer"’ containing the number of rows of
          the problem object.

     ‘nc’: Object of class ‘"integer"’ containing the number of columns
          of the problem object

     ‘fldind’: Object of class ‘"integer"’ pointers to columns
          (variables) representing a flux (reaction) in the original
          network. The variable ‘fldind[i]’ in the problem object
          represents reaction ‘i’ in the original network.

     ‘alg_par’: Object of class ‘"list"’ containing a named list
          containing algorithm specific parameters.

_E_x_t_e_n_d_s:

     Class ‘"sysBiolAlg"’, directly.

_M_e_t_h_o_d_s:

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

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     Constructor function ‘sysBiolAlg’ and superclass ‘sysBiolAlg’.

_E_x_a_m_p_l_e_s:

       showClass("sysBiolAlg_fba")
     

sysBiolAlg_mtf-class           package:sybil           R Documentation

_C_l_a_s_s '"_s_y_s_B_i_o_l_A_l_g__m_t_f"'

_D_e_s_c_r_i_p_t_i_o_n:

     The class ‘sysBiolAlg_mtf’ holds an object of class ‘optObj’ which
     is generated to meet the requirements of the minimize total flux
     algorithm: minimize the absolute sum of all fluxes given a
     previously calculated objective value.

_D_e_t_a_i_l_s:

     The ‘initialize’ method has the following arguments:

     model An object of class ‘modelorg’.

     wtobj A single numeric value giving the optimal value. If missing,
          a default value is computed based on FBA. If given, arguments
          ‘solver’ and ‘method’ are used, but ‘solverParm’ is not.
          Default: ‘NULL’.

     react Arguments ‘react’, ‘lb’ and ‘ub’ are used, if argument
          ‘wtobj’ is ‘NULL’, meaning: no previous objective value is
          given. Objective values will be calculated via ‘fba’ using
          the parameters given in ‘react’, ‘lb’ and ‘ub’.
          Default: ‘NULL’.

     lb See argument ‘react’.
          Default: ‘NULL’.

     ub See argument ‘react’.
          Default: ‘NULL’.

     costcoeffw A numeric vector containing cost coefficients for all
          variables (forward direction). If set to ‘NULL’, all cost
          coefficients are set to ‘1’, so that all variables have the
          same impact on the objective function.
          Default: ‘NULL’.

     costcoefbw A numeric vector containing cost coefficients for all
          variables (backward direction). If set to ‘NULL’, all cost
          coefficients are set to the values given in ‘costcoeffw’.
          Default: ‘NULL’.

     absMAX A single numerical value used as a maximum value for upper
          variable and contraint bounds.
          Default: ‘SYBIL_SETTINGS("MAXIMUM")’.

     useNames A single boolean value. If set to ‘TRUE’, variables and
          constraints will be named according to ‘cnames’ and ‘rnames’.
          If set to ‘NULL’, no specific variable or constraint names
          are set.
          Default: ‘SYBIL_SETTINGS("USE_NAMES")’.

     cnames A character vector giving the variable names. If set to
          ‘NULL’, the reaction id's of ‘model’ are used.
          Default: ‘NULL’.

     rnames A character vector giving the constraint names. If set to
          ‘NULL’, the metabolite id's of ‘model’ are used.
          Default: ‘NULL’.

     pname A single character string containing a name for the problem
          object.
          Default: ‘NULL’.

     scaling Scaling options used to scale the constraint matrix. If
          set to ‘NULL’, no scaling will be performed (see
          ‘scaleProb’).
          Default: ‘NULL’.

     writeProbToFileName A single character string containing a file
          name to which the problem object will be written in LP file
          format.
          Default: ‘NULL’.

     ...  Further arguments passed to the initialize method of
          ‘sysBiolAlg’. They are ‘solver’, ‘method’ and ‘solverParm’.

     The problem object is built to be capable to perform minimize
     total flux with a given model, which is basically the solution of
     a linear programming problem

           min  sum cost_i abs(v_i)  for i = 1, ..., n
           s.t. Sv = 0
                a_i <= v_i <= b_i  for i = 1, ..., n
                c_wt >= c^T v_wt
     
     with c^T v_wt being the previously computed optimized value of the
     objective function (argument ‘wtobj’).  The variable S denotes the
     stoichiometric matrix, a_i and b_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 optimization can be
     executed by using ‘optimizeProb’.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form

     ‘sysBiolAlg(model, algorithm = "mtf", ...)’.

     Arguments to ‘...’ which are passed to method ‘initialize’ of
     class ‘sysBiolAlg_mtf’ are described in the Details section.

_S_l_o_t_s:

     ‘maxobj’: Object of class ‘"numeric"’ containing optimized
          objective values.

     ‘problem’: Object of class ‘"optObj"’ containing the problem
          object.

     ‘algorithm’: Object of class ‘"character"’ containing the name of
          the algorithm.

     ‘nr’: Object of class ‘"integer"’ containing the number of rows of
          the problem object.

     ‘nc’: Object of class ‘"integer"’ containing the number of columns
          of the problem object

     ‘fldind’: Object of class ‘"integer"’ pointers to columns
          (variables) representing a flux (reaction) in the original
          network. The variable ‘fldind[i]’ in the problem object
          represents reaction ‘i’ in the original network.

     ‘alg_par’: Object of class ‘"list"’ containing a named list
          containing algorithm specific parameters.

_E_x_t_e_n_d_s:

     Class ‘"sysBiolAlg"’, directly.

_M_e_t_h_o_d_s:

     changeMaxObj ‘signature(object = "sysBiolAlg_mtf")’: change
          current objective value to the jth value given in slot
          ‘maxobj’. Argument ‘j’ must be in ‘[1:length(maxobj)]’.

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     Constructor function ‘sysBiolAlg’ and superclass ‘sysBiolAlg’.

_E_x_a_m_p_l_e_s:

       showClass("sysBiolAlg_mtf")
     

sysBiolAlg_fbaEasyConstraint-class    package:sybil    R Documentation

_C_l_a_s_s '"_s_y_s_B_i_o_l_A_l_g__f_b_a_E_a_s_y_C_o_n_s_t_r_a_i_n_t"' _a_n_d _C_l_a_s_s
'"_s_y_s_B_i_o_l_A_l_g__m_t_f_E_a_s_y_C_o_n_s_t_r_a_i_n_t"'

_D_e_s_c_r_i_p_t_i_o_n:

     The classes ‘sysBiolAlg_fbaEasyConstraint’
     ‘sysBiolAlg_mtfEasyConstraint’ hold an object of class ‘optObj’
     which is generated to meet the requirements of the FBA/MTF
     algorithm.
     In Addition to this, it is very easy to add additional linear
     constraints to that linear problem. Each constraints is defined by
     the affected reaction, the coefficient, lower and upper bounds,
     and the constraint type.

_D_e_t_a_i_l_s:

     The problem object is built to be capable to perform flux balance
     analysis (FBA) with a given model, which is basically the solution
     of a linear programming problem

           max  c^T v
           s.t. Sv = 0
                a_i <= v_i <= b_i  for i = 1, ..., n
     
     with S being the stoichiometric matrix, a_i and b_i being the
     lower and upper bounds for flux (variable) i respectively.  The
     total number of variables of the optimization problem is denoted
     by n.  The solution of the optimization is a flux distribution
     maximizing the objective function c^T v under the a given
     environment and the assumption of steady state.  The optimization
     can be executed by using ‘optimizeProb’.

     The additional i-th EasyConstraint will be added as follows to the
     problem: _to be checked._

                      gamma_i <= v_{r^i} * (x_i)^T <= delta_i           
     
     Here r_i (= ‘easyConstraint$react[[i]]’) is a set of reaction
     indices and x_i (= ‘easyConstraint$x[[i]]’) is the corresponding
     set of coefficients. gamma and delta are the vectors of lower and
     upper bounds for the constraints, respectively.  For the type of
     (in)equality (<=, ...) see the text above for parameter ‘rtype’.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form

     ‘sysBiolAlg(model, algorithm = "fbaEasyConstraint", ...)’.

     Arguments to ‘...’ which are passed to method ‘initialize’ of
     class ‘sysBiolAlg_fba’ are described in the Details section.

_S_l_o_t_s:

     Slots are the same as in the original MTF/FBA classes. In
     addition, this slot is implemented:

     ‘easyConstraint’ Named list holding the information for the
          constraints (see details):

            • ‘react’ List of numeric vectors. Values indicate, to
              which reaction the constraint applys.

            • ‘x’ List of numeric vectors. Values indicate coefficients
              of the constraint. Lengths have to be equal to
              ‘react’-field.

            • ‘lb’ Numeric vector of lower bounds for constraints. If
              not given, a default bound of 0 will be used.

            • ‘ub’ Numeric vector of lower bounds for constraints. If
              not given, a default bound of 0 will be used. Only needed
              for constraints, that need two bounds.

            • ‘rtype’ Character vector defining the type of constraint.

                ‘"F"’:  free constraint (GLPK only)         -INF < x < INF 
                ‘"L"’:  constraint with lower bound         lb <= x < INF  
                ‘"U"’:  constraint with upper bound         -INF < x <= ub 
                ‘"D"’:  double-bounded (ranged) constraint  lb <= x <= ub  
                ‘"E"’:  fixed (equality) constraint         lb = x = ub    
               
              If ‘rtype[i]’ is not one of ‘"F"’, ‘"L"’, ‘"U"’, ‘"D"’ or
              ‘"E"’, the value of ‘rtype[i]’ will be set to ‘"E"’. See
              Details of ‘loadLPprob’.

_E_x_t_e_n_d_s:

     Class ‘"sysBiolAlg"’, directly.

_M_e_t_h_o_d_s:

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

_A_u_t_h_o_r(_s):

     Claus Jonathan Fritzemeier
     <clausjonathan.fritzemeier@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     Constructor function ‘sysBiolAlg’ and superclass ‘sysBiolAlg’.

_E_x_a_m_p_l_e_s:

       showClass("sysBiolAlg_fbaEasyConstraint")
       
       # see package vignette for second example with more comments:
       #vignette("sybil")
       
       #load model
       data(Ec_core)
       
       # allow influx of Fumarate and restrict outflux of Fumarate and Glucose
       lowbnd(Ec_core)[react_id(Ec_core) %in% c("EX_fum(e)")] <- -1000
       uppbnd(Ec_core)[react_id(Ec_core) %in% c("EX_glc(e)", "EX_fum(e)")] <- 0
       
       # see result
       findExchReact(Ec_core)
       optimizeProb(Ec_core)
       
       # define easyConstraint to have the same influx for Glucose and Fumarate:
       # EX_glc(e) = EX_fum(e)
       # here we omit the upper and lower bound, hence they are set to zero.
       ec <- list(
             react=list(which(react_id(Ec_core) %in% c("EX_glc(e)", "EX_fum(e)"))),
             x=list(c(1, -1)),
             rtype="E")
       
       # optimize
       opt <- optimizeProb(Ec_core, algorithm=("fbaEasyConstraint"), easyConstraint=ec)
       
       # check if fluxes are really the same:
       fluxes(opt)[react_id(Ec_core) %in% c("EX_glc(e)", "EX_fum(e)")]
     

compute affected fluxes ... OK
calculating 137 optimizations ... OK
Done.
compute affected fluxes ... OK
calculating 137 optimizations ... OK
Done.
compute affected fluxes ... OK
calculating 137 optimizations ... OK
Done.
calculating 190 optimizations ... OK
calculating 20 optimizations ... OK
calculating 625 optimizations ... OK
setting 272 objective values to zero
compute affected fluxes ... OK
calculating 137 optimizations ... OK
Done.
compute affected fluxes ... OK
calculating 137 optimizations ... OK
Done.
SYBIL_SETTINGS              package:sybil              R Documentation

_S_e_t _a_n_d _G_e_t _s_y_b_i_l _P_a_r_a_m_e_t_e_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     Manage a set of default parameter settings for sybil.

_U_s_a_g_e:

       SYBIL_SETTINGS(parm, value, ...)
     
_A_r_g_u_m_e_n_t_s:

    parm: A character string giving the name of the parameter to set.

   value: The corresponding value.

     ...: Further arguments passed to ‘checkDefaultMethod’. Only used
          if parameters ‘"SOLVER"’ or ‘"METHOD"’ are set.

_D_e_t_a_i_l_s:

     Typical usages are

         SYBIL_SETTINGS(parm, value)
         SYBIL_SETTINGS(parm)
         SYBIL_SETTINGS()
     
     Possible parameters are:

     ‘"SOLVER"’ The default solver for lp problems. Possible values are
          depend on your installed API package.

          ‘glpkAPI’: ‘"glpkAPI"’,

          ‘cplexAPI’: ‘"cplexAPI"’,

          ‘clpAPI’: ‘"clpAPI"’,

          ‘lpSolveAPI’: ‘"lpSolveAPI"’.

          Default: ‘"glpkAPI"’.

     ‘"METHOD"’ The default method to solve lp problems. Possible
          values are

          ‘glpkAPI’: ‘"simplex"’, ‘"interior"’, ‘"exact"’ or ‘mip’.

          ‘cplexAPI’: ‘"lpopt"’, ‘"primopt"’ ‘"dualopt"’, ‘"baropt"’,
              ‘"hybbaropt"’, ‘"hybnetopt"’, ‘"siftopt"’, ‘mipopt’ or
              ‘qpopt’.

          ‘clpAPI’: ‘"general_solve"’, ‘"inidual"’ ‘"iniprimal"’,
              ‘"inibarrier"’, ‘"inibarriernoc"’, ‘"idiot"’, ‘"dual"’ or
              ‘"primal"’.

          ‘lpSolveAPI’: ‘"lp_solve"’.

          Default: ‘"simplex"’.
          If the parameter ‘"SOLVER"’ is changed, the corrsponding
          default ‘"METHOD"’ is the first one mentioned, e.g. for
          ‘"cplexAPI"’, it will be ‘"lpopt"’. This change is done
          automatically when changing the solver. It is not possible,
          to set a not existing ‘"METHOD"’ for a particular ‘"SOLVER"’,
          the corresponding default value will be used in such a case.

     ‘"MAXIMUM"’ Absolute maximum value.
          Default: ‘1000’.

     ‘"MODELORG_VERSION"’ Currtent version of ‘modelorg’-Class.
          Value: ‘"2.0"’.
          This value must not be changed.

     ‘"ALGORITHM"’ Algorithm to use in order to analyze metabolic
          networks.  Possible values are:

          ‘"fba"’ flux-balance analysis,

          ‘"fv"’ flux-variance analysis,

          ‘"mtf"’ minimize total flux,

          ‘"moma"’ minimization of metabolic adjustment (MOMA),

          ‘"lmoma"’ linear version of MOMA,

          ‘"room"’ regulatory on/off minimization (ROOM).

          Default: ‘"fba"’.

     ‘"OPT_DIRECTION"’ Direction of optimization. Can be ‘"max"’ or
          ‘"min"’.
          Default: ‘"max"’.

     ‘"USE_NAMES"’ A logical value indicating if reaction id's and
          metabolite id's (or other names) should be used as names for
          variables and constraints in objects of class ‘sysBiolAlg’.
          Default: ‘FALSE’.

     ‘"PATH_TO_MODEL"’ Path to a directory to read or write files.
          Default: ‘"."’.

     ‘"SOLVER_CTRL_PARM"’ A ‘data.frame’ giving parameters to the
          optimizer software (e.g. GLPK).
          Default: ‘as.data.frame(NA)’.

     ‘"TOLERANCE"’ Tolerance value.
          Default: ‘1E-6’.

_V_a_l_u_e:

     If successful, a set of parameters to sybil will be returned.

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_S_e_e _A_l_s_o:

     ‘checkDefaultMethod’

_E_x_a_m_p_l_e_s:

       ## show all current parameters
       SYBIL_SETTINGS()
       
       ## show current setting for "SOLVER"
       SYBIL_SETTINGS("SOLVER")
     
       ## change current solver to glpkAPI
       SYBIL_SETTINGS("SOLVER", "glpkAPI")
     ## Not run:
     
       ## this needs cplexAPI installed
       ## change current solver to cplexAPI
       SYBIL_SETTINGS("SOLVER", "cplexAPI")
     ## End(Not run)
     

modelorg-class              package:sybil              R Documentation

_S_t_r_u_c_t_u_r_e _o_f _C_l_a_s_s "_m_o_d_e_l_o_r_g"

_D_e_s_c_r_i_p_t_i_o_n:

     Structure of the class ‘"modelorg"’. Objects of that class are
     returned by functions like ‘readTSVmod’.

     Structure of the class ‘"react"’. This depicts a subset of a
     metabolic model that contains only one reaction. Multiple ‘react’
     objects can be combined to an ‘"modelorg"’ object.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the function ‘modelorg’:

     ‘test <- modelorg(id = "foo", name = "bar", subSys = NULL,
     compartment = NULL)’.

     ‘id’: a single character string giving the model id.

     ‘name’: a single character string giving the model name.

     ‘subSys’: an optional single character string giving the metabolic
          subsystems of the model. Default: ‘NULL’

     ‘compartment’: an optional single character string giving the
          compartments of the model. Default: ‘NULL’

     This constructor also generates the model key used in slot
     ‘mod_key’.

_S_l_o_t_s:

     ‘mod_desc’: Object of class ‘"character"’ containing a description
          of the model.

     ‘mod_name’: Object of class ‘"character"’ indicating the model
          name.

     ‘version’: Object of class ‘"character"’ indicating the model
          version.

     ‘mod_id’: Object of class ‘"character"’ indicating the model id.

     ‘mod_key’: Object of class ‘"character"’ containing a single
          character string functioning as a unique key to a model
          object.

     ‘mod_attr’: Object of class ‘"data.frame"’ to store additional
          attributes of the model.

     ‘mod_compart’: Object of class ‘"character"’ containing the model
          compartments.

     ‘comp_attr’: Object of class ‘"data.frame"’ to store additional
          attributes for each compartment.

     ‘met_num’: Object of class ‘"integer"’ indicating the number of
          metabolites.

     ‘met_id’: Object of class ‘"character"’ containing the metabolite
          id's.

     ‘met_name’: Object of class ‘"character"’ containing the
          metabolite names.

     ‘met_comp’: Object of class ‘"integer"’ containing the metabolites
          compartment.

     ‘met_attr’: Object of class ‘"data.frame"’ to store additional
          attributes for each metabolite.

     ‘met_single’: Object of class ‘"logical"’ with length ‘met_num’.
          Element ‘i’ is ‘TRUE’, if metabolite ‘i’ appears only once in
          S.

     ‘met_de’: Object of class ‘"logical"’ with length ‘met_num’.
          Element ‘i’ is ‘TRUE’, if metabolite ‘i’ is a dead end
          metabolite.

     ‘react_num’: Object of class ‘"integer"’ indicating the number of
          reactions.

     ‘react_rev’: Object of class ‘"logical"’ indicating whether a
          reaction is reversible or not.

     ‘react_id’: Object of class ‘"character"’ containing the reaction
          id's.

     ‘react_name’: Object of class ‘"character"’ containing the
          reaction names.

     ‘react_attr’: Object of class ‘"data.frame"’ to store additional
          attributes for each reaction.

     ‘react_single’: Object of class ‘"logical"’ with length
          ‘react_num’. Element ‘i’ is ‘TRUE’, if reaction ‘i’ uses
          metabolites appearing only once in S.

     ‘react_de’: Object of class ‘"logical"’ with length ‘react_num’.
          Element ‘i’ is ‘TRUE’, if reaction ‘i’ uses dead end
          metabolites.

     ‘S’: Object of class ‘"matrix"’ containing the stoichiometric
          matrix.

     ‘lowbnd’: Object of class ‘"numeric"’ containing the reactions
          lower bounds.

     ‘uppbnd’: Object of class ‘"numeric"’ containing the reactions
          upper bounds.

     ‘obj_coef’: Object of class ‘"numeric"’ containing the objective
          coefficients.

     ‘gprRules’: Object of class ‘"character"’ containing the
          gene-reaction association rules in computable form.

     ‘genes’: Object of class ‘"list"’ containing the genes
          corresponding to each reaction. Every list element is a
          vector of the type ‘character’.

     ‘gpr’: Object of class ‘"character"’ containing the gene-reaction
          association rules for each reaction.

     ‘allGenes’: Object of class ‘"character"’ containing a unique list
          of all genes.

     ‘rxnGeneMat’: Object of class ‘"matrix"’ containing a reaction to
          gene mapping.

     ‘subSys’: Object of class ‘"matrix"’ giving one or more subsystem
          name for each reaction.

_M_e_t_h_o_d_s:

     ‘allGenes<-’: ‘signature(object = "modelorg")’: sets the
          ‘allGenes’ slot.

     ‘allGenes’: ‘signature(object = "modelorg")’: gets the ‘allGenes’
          slot.

     ‘dim’: ‘signature(object = "modelorg")’: gets the ‘dimension’
          attribute of slot ‘S’.

     ‘genes<-’: ‘signature(object = "modelorg")’: sets the ‘genes’
          slot.

     ‘genes’: ‘signature(object = "modelorg")’: gets the ‘genes’ slot.

     ‘gpr<-’: ‘signature(object = "modelorg")’: sets the ‘gpr’ slot.

     ‘gpr’: ‘signature(object = "modelorg")’: gets the ‘gpr’ slot.

     ‘gprRules<-’: ‘signature(object = "modelorg")’: sets the
          ‘gprRules’ slot.

     ‘gprRules’: ‘signature(object = "modelorg")’: gets the ‘gprRules’
          slot.

     ‘lowbnd<-’: ‘signature(object = "modelorg")’: sets the ‘lowbnd’
          slot.

     ‘lowbnd’: ‘signature(object = "modelorg")’: gets the ‘lowbnd’
          slot.

     ‘met_comp<-’: ‘signature(object = "modelorg")’: sets the
          ‘met_comp’ slot.

     ‘met_comp’: ‘signature(object = "modelorg")’: gets the ‘met_comp’
          slot.

     ‘met_de<-’: ‘signature(object = "modelorg")’: sets the ‘met_de’
          slot.

     ‘met_de’: ‘signature(object = "modelorg")’: gets the ‘met_de’
          slot.

     ‘met_id<-’: ‘signature(object = "modelorg")’: sets the ‘met_id’
          slot.

     ‘met_id’: ‘signature(object = "modelorg")’: gets the ‘met_id’
          slot.

     ‘met_name<-’: ‘signature(object = "modelorg")’: sets the
          ‘met_name’ slot.

     ‘met_name’: ‘signature(object = "modelorg")’: gets the ‘met_name’
          slot.

     ‘met_num<-’: ‘signature(object = "modelorg")’: sets the ‘met_num’
          slot.

     ‘met_num’: ‘signature(object = "modelorg")’: gets the ‘met_num’
          slot.

     ‘met_single<-’: ‘signature(object = "modelorg")’: sets the
          ‘met_single’ slot.

     ‘met_single’: ‘signature(object = "modelorg")’: gets the
          ‘met_single’ slot.

     ‘mod_compart<-’: ‘signature(object = "modelorg")’: sets the
          ‘mod_compart’ slot.

     ‘mod_compart’: ‘signature(object = "modelorg")’: gets the
          ‘mod_compart’ slot.

     ‘mod_desc<-’: ‘signature(object = "modelorg")’: sets the
          ‘mod_desc’ slot.

     ‘mod_desc’: ‘signature(object = "modelorg")’: gets the ‘mod_desc’
          slot.

     ‘mod_id<-’: ‘signature(object = "modelorg")’: sets the ‘mod_id’
          slot.

     ‘mod_id’: ‘signature(object = "modelorg")’: gets the ‘mod_id’
          slot.

     ‘mod_key<-’: ‘signature(object = "modelorg")’: sets the ‘mod_key’
          slot.

     ‘mod_key’: ‘signature(object = "modelorg")’: gets the ‘mod_key’
          slot.

     ‘mod_name<-’: ‘signature(object = "modelorg")’: sets the
          ‘mod_name’ slot.

     ‘mod_name’: ‘signature(object = "modelorg")’: gets the ‘mod_name’
          slot.

     ‘obj_coef<-’: ‘signature(object = "modelorg")’: sets the
          ‘obj_coef’ slot.

     ‘obj_coef’: ‘signature(object = "modelorg")’: gets the ‘obj_coef’
          slot.

     ‘printObjFunc’: ‘signature(object = "modelorg")’: prints the
          objective function in a human readable way.

     ‘react_de<-’: ‘signature(object = "modelorg")’: sets the
          ‘react_de’ slot.

     ‘react_de’: ‘signature(object = "modelorg")’: gets the ‘react_de’
          slot.

     ‘react_id<-’: ‘signature(object = "modelorg")’: sets the
          ‘react_id’ slot.

     ‘react_id’: ‘signature(object = "modelorg")’: gets the ‘react_id’
          slot.

     ‘react_name<-’: ‘signature(object = "modelorg")’: sets the
          ‘react_name’ slot.

     ‘react_name’: ‘signature(object = "modelorg")’: gets the
          ‘react_name’ slot.

     ‘react_num<-’: ‘signature(object = "modelorg")’: sets the
          ‘react_num’ slot.

     ‘react_num’: ‘signature(object = "modelorg")’: gets the
          ‘react_num’ slot.

     ‘react_rev<-’: ‘signature(object = "modelorg")’: sets the
          ‘react_rev’ slot.

     ‘react_rev’: ‘signature(object = "modelorg")’: gets the
          ‘react_rev’ slot.

     ‘react_single<-’: ‘signature(object = "modelorg")’: sets the
          ‘react_single’ slot.

     ‘react_single’: ‘signature(object = "modelorg")’: gets the
          ‘react_single’ slot.

     ‘rxnGeneMat<-’: ‘signature(object = "modelorg")’: sets the
          ‘rxnGeneMat’ slot.

     ‘rxnGeneMat’: ‘signature(object = "modelorg")’: gets the
          ‘rxnGeneMat’ slot.

     ‘show’: ‘signature(object = "modelorg")’: prints some details
          specific to the instance of class ‘modelorg’.

     ‘Snnz’: ‘signature(object = "modelorg")’: prints the number of
          non-zero elements in ‘S’.

     ‘S<-’: ‘signature(object = "modelorg")’: sets the ‘S’ slot as
          matrix, see Details below.

     ‘S’: ‘signature(object = "modelorg")’: gets the ‘S’ slot as
          matrix.

     ‘subSys<-’: ‘signature(object = "modelorg")’: sets the ‘subSys’
          slot.

     ‘subSys’: ‘signature(object = "modelorg")’: gets the ‘subSys’
          slot.

     ‘uppbnd<-’: ‘signature(object = "modelorg")’: sets the ‘uppnds’
          slot.

     ‘uppbnd’: ‘signature(object = "modelorg")’: gets the ‘uppbnd’
          slot.

     ‘version<-’: ‘signature(object = "modelorg")’: sets the ‘version’
          slot.

     ‘version’: ‘signature(object = "modelorg")’: gets the ‘version’
          slot.

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_S_e_e _A_l_s_o:

     ‘modelorg_irrev’ for models in irreversible format.

_E_x_a_m_p_l_e_s:

       showClass("modelorg")
     
       ## print human readable version of the objective function
       data(Ec_core)
       printObjFunc(Ec_core)
     
       ## change objective function and print
       Ec_objf <- changeObjFunc(Ec_core, c("EX_etoh(e)", "ETOHt2r"), c(1, 2))
       printObjFunc(Ec_objf)
     

optsol-class               package:sybil               R Documentation

_C_l_a_s_s _o_p_t_s_o_l

_D_e_s_c_r_i_p_t_i_o_n:

     The class ‘optsol’ provides data structures to store and access
     the results of optimizations. This class is extended by other
     classes and will not be used as is. The representation of class
     ‘optsol’ is used as superclass.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     A virtual Class: No objects may be created from it.

_S_l_o_t_s:

     ‘mod_id’: Object of class ‘"character"’ containing the model id of
          the used model.

     ‘mod_key’: Object of class ‘"character"’ containing the model key
          of the used model.

     ‘solver’: Object of class ‘"character"’ indicating the used
          solver.

     ‘method’: Object of class ‘"character"’ indicating the used
          method.

     ‘algorithm’: Object of class ‘"character"’ containing the name of
          the algorithm used for optimizations.

     ‘num_of_prob’: Object of class ‘"integer"’ indicating the number
          of optimization problems.

     ‘lp_num_cols’: Object of class ‘"integer"’ indicating the number
          of columns.

     ‘lp_num_rows’: Object of class ‘"integer"’ indicating the number
          of rows.

     ‘lp_obj’: Object of class ‘"numeric"’ containing the optimal
          values of the objective function after optimization. If no
          flux distribution is available, slot ‘lp_obj’ contains the
          cross-product of the objective coefficients in slot
          ‘obj_coef’ and the part of the flux distribution in slot
          ‘fluxdist’ containing the values representing fluxes in the
          entire metabolic network (slot ‘fldind’).

     ‘lp_ok’: Object of class ‘"integer"’ containing the exit code of
          the optimization.

     ‘lp_stat’: Object of class ‘"integer"’ containing the solution
          status of the optimization.

     ‘lp_dir’: Object of class ‘"character"’ indicating the direction
          of optimization.

     ‘obj_coef’: Object of class ‘"numeric"’ containing the objective
          coefficients of the used model (slot ‘obj_coef’ of an object
          of class ‘modelorg’).  These are not necessarily the
          objective coefficients of the used algorithm.

     ‘obj_func’: Object of class ‘"character"’ containing the objective
          function of the used model. Usually, it contains the return
          value of ‘printObjFunc’.

     ‘fldind’: Object of class ‘"integer"’ pointers to columns
          (variables) representing a flux (reaction) in the original
          network. The variable ‘fldind[i]’ in the problem object
          represents reaction ‘i’ in the original network.

     ‘fluxdist’: Object of class ‘"fluxDistribution"’ containing the
          solutions flux distributions.

     ‘alg_par’: Object of class ‘"list"’ containing a named list
          containing algorithm specific parameters.

_M_e_t_h_o_d_s:

     ‘algorithm<-’: ‘signature(object = "optsol")’: sets the
          ‘algorithm’ slot.

     ‘algorithm’: ‘signature(object = "optsol")’: gets the ‘algorithm’
          slot.

     alg_par ‘signature(object = "optsol")’: gets the ‘alg_par’ slot.

     alg_par<- ‘signature(object = "optsol")’: sets the ‘alg_par’ slot.

     ‘checkStat’: ‘signature(opt = "optsol")’: returns the indices of
          problems with a non optimal solution status.

     ‘fldind<-’: ‘signature(object = "optsol")’: sets the ‘fldind’
          slot.

     ‘fldind’: ‘signature(object = "optsol")’: gets the ‘fldind’ slot.

     ‘fluxdist<-’: ‘signature(object = "optsol")’: sets the ‘fluxdist’
          slot.

     ‘fluxdist’: ‘signature(object = "optsol")’: gets the ‘fluxdist’
          slot.

     ‘fluxes<-’: ‘signature(object = "optsol")’: sets the ‘fluxes’ slot
          of slot ‘fluxdist’.

     ‘fluxes’: ‘signature(object = "optsol")’: gets the ‘fluxes’ slot
          of slot ‘fluxdist’.

     ‘plot’: ‘signature(x = "optsol")’: plots a ‘histogram’ of the
          values of the objective function given in the model in
          optimal state.  Additional arguments can be passed to
          ‘histogram’ via the ‘...’ argument.

     ‘length’: ‘signature(x = "optsol")’: returns the number of
          optimizations.

     ‘lp_dir<-’: ‘signature(object = "optsol", value = "character")’:
          sets the ‘lp_dir’ slot. Argument ‘value’ can be ‘"min"’
          (minimization) or ‘"max"’ (maximization).

     ‘lp_dir<-’: ‘signature(object = "optsol", value = "factor")’: sets
          the ‘lp_dir’ slot.

     ‘lp_dir<-’: ‘signature(object = "optsol", value = "numeric")’:
          sets the ‘lp_dir’ slot. Argument ‘value’ can be ‘1’
          (minimization) or ‘-1’ (maximization).

     ‘lp_dir’: ‘signature(object = "optsol")’: gets the ‘lp_dir’ slot.

     ‘lp_num_cols<-’: ‘signature(object = "optsol")’: sets the
          ‘lp_num_cols’ slot.

     ‘lp_num_cols’: ‘signature(object = "optsol")’: gets the
          ‘lp_num_cols’ slot.

     ‘lp_num_rows<-’: ‘signature(object = "optsol")’: sets the
          ‘lp_num_rows’ slot.

     ‘lp_num_rows’: ‘signature(object = "optsol")’: gets the
          ‘lp_num_rows’ slot.

     ‘lp_obj<-’: ‘signature(object = "optsol")’: sets the ‘lp_obj’
          slot.

     ‘lp_obj’: ‘signature(object = "optsol")’: gets the ‘lp_obj’ slot.

     ‘lp_ok<-’: ‘signature(object = "optsol")’: sets the ‘lp_ok’ slot.

     ‘lp_ok’: ‘signature(object = "optsol")’: gets the ‘lp_ok’ slot.

     ‘lp_stat<-’: ‘signature(object = "optsol")’: sets the ‘lp_stat’
          slot.

     ‘lp_stat’: ‘signature(object = "optsol")’: gets the ‘lp_stat’
          slot.

     ‘method<-’: ‘signature(object = "optsol")’: sets the ‘method’
          slot.

     ‘method’: ‘signature(object = "optsol")’: gets the ‘method’ slot.

     ‘mod_id<-’: ‘signature(object = "optsol")’: sets the ‘mod_id’
          slot.

     ‘mod_id’: ‘signature(object = "optsol")’: gets the ‘mod_id’ slot.

     ‘mod_key<-’: ‘signature(object = "optsol")’: sets the ‘mod_key’
          slot.

     ‘mod_key’: ‘signature(object = "optsol")’: gets the ‘mod_key’
          slot.

     ‘mod_obj’: ‘signature(object = "optsol_fluxdel")’: returns always
          the cross-product of the objective coefficients in slot
          ‘obj_coef’ and the part of the flux distribution in slot
          ‘fluxdist’ containing the values representing fluxes in the
          entire metabolic network (slot ‘fldind’). If slot ‘obj_coef’
          is ‘NA’, the content of slot ‘lp_obj’ is returned.  In
          contrast, method ‘lp_obj’ always returns the value of the
          objective function of the used algorithm after optimization.

     ‘nfluxes’: ‘signature(object = "optsol")’: gets the number of
          elements in the flux distribution matrix.

     ‘num_of_prob<-’: ‘signature(object = "optsol")’: sets the
          ‘num_of_prob’ slot.

     ‘num_of_prob’: ‘signature(object = "optsol")’: gets the
          ‘num_of_prob’ slot.

     ‘obj_coef<-’: ‘signature(object = "optsol")’: sets the ‘obj_coef’
          slot.

     ‘obj_coef’: ‘signature(object = "optsol")’: gets the ‘obj_coef’
          slot.

     ‘obj_func<-’: ‘signature(object = "optsol")’: sets the ‘obj_func’
          slot.

     ‘obj_func’: ‘signature(object = "optsol")’: gets the ‘obj_func’
          slot.

     ‘react_id<-’: ‘signature(object = "optsol")’: sets the ‘react_id’
          slot.

     ‘react_id’: ‘signature(object = "optsol")’: gets the ‘react_id’
          slot.

     ‘show’: ‘signature(object = "optsol")’: prints a summary of the
          content of instance of class ‘optsol’.

     ‘solver<-’: ‘signature(object = "optsol")’: sets the ‘solver’
          slot.

     ‘solver’: ‘signature(object = "optsol")’: gets the ‘solver’ slot.

_A_u_t_h_o_r(_s):

     Gabriel Gelius-Dietrich <geliudie@uni-duesseldorf.de>

     Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

_S_e_e _A_l_s_o:

     ‘checkOptSol’, ‘optsol_optimizeProb’ ‘optsol_fluxdel’,
     ‘optsol_genedel’, ‘optsol_robAna’ and ‘optsol_fluxVar’

_E_x_a_m_p_l_e_s:

       showClass("optsol")
     

Error: processing vignette 'sybil.Rnw' failed with diagnostics:
Running 'texi2dvi' on 'sybil.tex' failed.
LaTeX errors:
! Undefined control sequence.
\Ginclude@graphics ...\Ginput@path \set@curr@file 
                                                  {#1}\edef \uq@curr@file {\...
l.304 \includegraphics{net-crop}
                                
! Undefined control sequence.
\Ginclude@graphics ...e \expandafter {\@curr@file 
                                                  }}\expandafter \filename@p...
l.304 \includegraphics{net-crop}
                                
! Undefined control sequence.
<recently read> \unquote@name 
                              
l.304 \includegraphics{net-crop}
                                
! Undefined control sequence.
<recently read> \quote@name 
                            
l.304 \includegraphics{net-crop}
                                
! Undefined control sequence.
<recently read> \quote@name 
                            
l.304 \includegraphics{net-crop}
                                
! LaTeX Error: File `net-crop' not found.

See the LaTeX manual or LaTeX Companion for explanation.
Type  H <return>  for immediate help.
 ...                                              
! Undefined control sequence.
\Ginclude@graphics ...\Ginput@path \set@curr@file 
                                                  {#1}\edef \uq@curr@file {\...
l.1242 \includegraphics{sybil-036}
                                  
! Undefined control sequence.
\Ginclude@graphics ...e \expandafter {\@curr@file 
                                                  }}\expandafter \filename@p...
l.1242 \includegraphics{sybil-036}
                                  
! Undefined control sequence.
<recently read> \unquote@name 
                              
l.1242 \includegraphics{sybil-036}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1242 \includegraphics{sybil-036}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1242 \includegraphics{sybil-036}
                                  
! LaTeX Error: File `sybil-036' not found.

See the LaTeX manual or LaTeX Companion for explanation.
Type  H <return>  for immediate help.
 ...                                              
! Undefined control sequence.
\Ginclude@graphics ...\Ginput@path \set@curr@file 
                                                  {#1}\edef \uq@curr@file {\...
l.1274 \includegraphics{sybil-037}
                                  
! Undefined control sequence.
\Ginclude@graphics ...e \expandafter {\@curr@file 
                                                  }}\expandafter \filename@p...
l.1274 \includegraphics{sybil-037}
                                  
! Undefined control sequence.
<recently read> \unquote@name 
                              
l.1274 \includegraphics{sybil-037}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1274 \includegraphics{sybil-037}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1274 \includegraphics{sybil-037}
                                  
! LaTeX Error: File `sybil-037' not found.

See the LaTeX manual or LaTeX Companion for explanation.
Type  H <return>  for immediate help.
 ...                                              
! Undefined control sequence.
\Ginclude@graphics ...\Ginput@path \set@curr@file 
                                                  {#1}\edef \uq@curr@file {\...
l.1315 \includegraphics{sybil-040}
                                  
! Undefined control sequence.
\Ginclude@graphics ...e \expandafter {\@curr@file 
                                                  }}\expandafter \filename@p...
l.1315 \includegraphics{sybil-040}
                                  
! Undefined control sequence.
<recently read> \unquote@name 
                              
l.1315 \includegraphics{sybil-040}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1315 \includegraphics{sybil-040}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1315 \includegraphics{sybil-040}
                                  
! LaTeX Error: File `sybil-040' not found.

See the LaTeX manual or LaTeX Companion for explanation.
Type  H <return>  for immediate help.
 ...                                              
! Undefined control sequence.
\Ginclude@graphics ...\Ginput@path \set@curr@file 
                                                  {#1}\edef \uq@curr@file {\...
l.1348 \includegraphics{sybil-041}
                                  
! Undefined control sequence.
\Ginclude@graphics ...e \expandafter {\@curr@file 
                                                  }}\expandafter \filename@p...
l.1348 \includegraphics{sybil-041}
                                  
! Undefined control sequence.
<recently read> \unquote@name 
                              
l.1348 \includegraphics{sybil-041}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1348 \includegraphics{sybil-041}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1348 \includegraphics{sybil-041}
                                  
! LaTeX Error: File `sybil-041' not found.

See the LaTeX manual or LaTeX Companion for explanation.
Type  H <return>  for immediate help.
 ...                                              
! Undefined control sequence.
\Ginclude@graphics ...\Ginput@path \set@curr@file 
                                                  {#1}\edef \uq@curr@file {\...
l.1375 \includegraphics{sybil-042}
                                  
! Undefined control sequence.
\Ginclude@graphics ...e \expandafter {\@curr@file 
                                                  }}\expandafter \filename@p...
l.1375 \includegraphics{sybil-042}
                                  
! Undefined control sequence.
<recently read> \unquote@name 
                              
l.1375 \includegraphics{sybil-042}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1375 \includegraphics{sybil-042}
                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1375 \includegraphics{sybil-042}
                                  
! LaTeX Error: File `sybil-042' not found.

See the LaTeX manual or LaTeX Companion for explanation.
Type  H <return>  for immediate help.
 ...                                              
! Undefined control sequence.
\Ginclude@graphics ...\Ginput@path \set@curr@file 
                                                  {#1}\edef \uq@curr@file {\...
l.1392 ...raphics[width=\textwidth]{sybil-phpp_rf}
                                                  
! Missing $ inserted.
<inserted text> 
                $
l.1392 ...raphics[width=\textwidth]{sybil-phpp_rf}
                                                  
! Extra }, or forgotten $.
\Ginclude@graphics ...put@path \set@curr@file {#1}
                                                  \edef \uq@curr@file {\expa...
l.1392 ...raphics[width=\textwidth]{sybil-phpp_rf}
                                                  
! Undefined control sequence.
\Ginclude@graphics ...e \expandafter {\@curr@file 
                                                  }}\expandafter \filename@p...
l.1392 ...raphics[width=\textwidth]{sybil-phpp_rf}
                                                  
! Undefined control sequence.
<recently read> \unquote@name 
                              
l.1392 ...raphics[width=\textwidth]{sybil-phpp_rf}
                                                  
! Undefined control sequence.
<recently read> \quote@name 
                            
l.1392 ...raphics[width=\textwidth]{sybil-phpp_rf}
                                                  
! Undefined control sequence.
<recently read> 
--- failed re-building ‘sybil.Rnw’

SUMMARY: processing the following file failed:
  ‘sybil.Rnw’

Error: Vignette re-building failed.
Execution halted