mkSpurs                package:ccems                R Documentation

_M_a_k_e _S_p_u_r _M_o_d_e_l _S_p_a_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     This function takes 'g' from 'mkg' and maps it into a dataframe of
     spur graph model definitions.

_U_s_a_g_e:

     mkSpurs(g,state=list(globMdlIndex=0,globCmbIndex=0,relCmbIndex=0,
              config=NULL), maxnKjPs=NULL, maxTotalPs=NULL,minTotalPs=NULL, 
              batchSize=500,doTights=FALSE,
              atLeastOne=TRUE,atLeastOneOfEach=FALSE,
              KIC=1,kIC=1,m1=-90,p=-1,forceM1=FALSE,forceP=FALSE) 

_A_r_g_u_m_e_n_t_s:

       g: The generic model output list of 'mkg'. 

   state: The current state of model space generation. This is a list
          with several components.  'globMdlIndex' is the global model
          space index.  When 'mkSpurs' is called within 'ems' the
          'globMdlIndex' of  the spurs begins where it ends for grids. 
          'globCmbIndex' is the global spur model index.  This index is
          used to trim the last 'batchSize', if needed, to avoid
          attempts to extend the spur space size beyond its upper limit
          of '2^g$nZ'.  'relCmbIndex' is the most critical component of
          the 'state'.  This is the column number of the current matrix
          output of 'combn', i.e. it is a relative index.   The spur
          space 'chunk' returned by 'mkSpurs' begins just after this
          column.  'config' is a vector of the integer positions in
          'g$Z' of the  last model's finite K (i.e. the last model of
          the previous chunk). 'config'  is  the 'relCmbIndex'th column
          of the current 'combn' matrix.  Its length is the current
          number of K parameters in the model unless 'relCmbIndex=0', 
          in which case the number of  parameters is one more than this
           (in this case the end of the last batch coincides with the
          end of a 'combn' matrix). 

maxnKjPs: The maximum number of Kj parameters of models in the model
          space. Full chunks  are created and then trimmed, so
          descreases in the value of this option will not solve  "out
          of memory" problems. 

maxTotalPs: This is the maximum number of freely estimated k or K
          parameters.  

minTotalPs: The minimum number of parameters of models in the model
          space. If 'NULL' no minimum is imposed. 

batchSize: This is the number of K infinity models fitted per batch.
          Chunk sizes  are bigger than this if 'doTights' is 'TRUE'
          and/or   if 'pRows' is 'TRUE', and/or if activity parameter
          constraints split models further. Values less than ~1000 are
          recommended for  quad core 8 GB motherboards. 

doTights: This should be 'TRUE' if infinitely tight binding models  of
          single edge spur graphs are to be created.

atLeastOne: Leave 'TRUE' if only models with at least one complex  of
          maximal size are to be considered. Set 'FALSE' if there is no
          prior knowledge supportive of the assertion that the largest
          oligomer must be in the model.

atLeastOneOfEach: Set 'TRUE' if only models with at least one complex 
          of each oligomer size are to be considered. This is useful
          when the data are multivariate proportions (i.e. mass
          distribution data) and each j-mer is clearly present. 

     KIC: The initial condition of all K parameters optimized. The
          default is 'IC=1' (in uM). 

     kIC: The initial condition of all k parameters optimized. The
          default is 'kIC=1' (in 1/seconds per occupied active site). 

      m1: The hub protein's monomer mass in kDa.  The default is 90 for
          the big (R1) subunit of ribonucleotide reductase (RNR). This
          only matters if the data is mass data. Negative numbers imply
          fixed values and positive numbers imply starting values to be
          fitted to the data.

       p: Probability that hub can oligomerize, i.e. is not damaged. 
          Set to a positive value if additional rows are to be added to
          the output dataframe to include models  with 'p' freely
          estimated. Set negative to hold fixed. Value is the initial
          or fixed value.

 forceM1: Set 'TRUE' to force all models to estimate M1, i.e. to not
          generate models with M1 fixed. 

  forceP: Set 'TRUE' to force all models to estimate p, i.e. to not
          generate models with fixed p. 

_D_e_t_a_i_l_s:

     This function is complicated by the fact that one readily runs out
     of memory  with 29 complexes and thus roughly 500,000,000 spur
     models (in this case 8 GB RAM  allows at most 16 bytes per
     model!).   Thus, chunks of the spur model space must be created,
     fitted and summarized in sizes  small enough to fit into memory. 
     As both an input and an output, 'state' links successive calls to
     this function.  It keeps track of where we are in the spur graph
     model space and it allows searches through  the low parameter
     number models without first defining all of the higher parameter
     number models (and thus consuming all of the RAM in the process).

_V_a_l_u_e:

     A list with components  

   chunk: A dataframe where each row is a spur model. 

   state: The state, defined in the same way as the input argument
          'state'  since the output 'state' of one call is the input
          'state' of the next call.

maxReached: This is 'TRUE' if the maximum number of parameter has been
          reached.

lastCompleted: When all of the models with  j K parameters have  been
          specified, 'lastCompleted' equals j.   This is useful in
          'ems' when 'smart = TRUE' as it defines how far into the 
          model space the fitting process has gone so far in terms of
          numbers of model parameters. 

_N_o_t_e:

     This work was supported by the National Cancer Institute
     (K25CA104791).

_A_u_t_h_o_r(_s):

     Tom Radivoyevitch (txr24@case.edu)

_R_e_f_e_r_e_n_c_e_s:

     Radivoyevitch, T. (2008) Equilibrium model selection: dTTP 
     induced R1 dimerization. _BMC Systems Biology_ *2*, 15.

_S_e_e _A_l_s_o:

     'mkGrids', 'ccems', 'combn'

_E_x_a_m_p_l_e_s:

     library(ccems)
     topology <- list(  
             heads=c("R1t0","R2t0"),  
             sites=list(       
                     s=list(                     # s-site    thread #
                             m=c("R1t1"),        # monomer      1
                             d=c("R2t1","R2t2")  # dimer        2
                     )
             )
     ) 
     g <- mkg(topology) 
     mkSpurs(g,p=0.95,doTights=TRUE)

