mkGrids                package:ccems                R Documentation

_M_a_k_e _G_r_i_d _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 grid model definitions. It also augments and returns the input
     list 'g'.

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

      mkGrids (g,maxTotalPs=NULL,pRows=FALSE,contig=TRUE, atLeastOne=TRUE, 
                                      IC=1,kIC=1,fullGrid=FALSE) 

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

       g: The output of 'mkg'. 

maxTotalPs: The maximum number of parameters of models in the model
          space.  If 'NULL' all models are found (but see 'fulGrid'
          below). 

   pRows: Set to 'TRUE' if additional rows are to be added to the
          output dataframe to include models  with 'p' freely
          estimated. 

  contig: Set to 'TRUE' to allow Kd equalities of threads only in
          contiguous runs. Tentatively,  this should always be 'TRUE'.

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.

      IC: 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). 

fullGrid: Set 'TRUE' if full binary K models are wanted, e.g. to obtain
          grid  reparameterizations of full spur models to form model
          averages across common binary K parameters. 

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

     In a run of equal threads, the first head node of the run is the
     leader (optimized parameter) and the remaining nodes in the run
     are followers  (i.e. constrained to track the leader in parameter
     estimate optimizations);  a default in 'ems' is that only leader
     estimates are reported in html in the 'results' subdirectory. In
     contrast to its counterpart 'mkSpurs', a means of traversing the
     grid model space incrementally with increasing numbers of
     parameters remains to be  found and implemented, i.e.  'mkGrids'
     does not have 'state' inputs and outputs and the whole space is
     found in one batch.   When this drawback  limits research due to
     too much memory usage,  attempts will be made to identify an
     approach that, similar to what has been implemented for 
     'mkSpurs', specifies chunks of grid models, fits them, and then
     specifies the next chunk  based on knowledge of where the previous
     chunk stopped.  Note that setting  'maxTotalPs' to smaller values
     will not help this foreseen memory problem  as the entire grid
     model space dataframe is first generated  and then, only later,
     truncated to 'maxTotalPs'.

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

     A list with components  

   chunk: The entire K equality model space requested by the arguments.
           In this dataframe each row specifies a model. If the
          'activity' field of 'g' is 'TRUE'  this dataframe includes k
          columns. The row names encode the equality constraints. 
          Therein . separates K models from k models, I stands for
          infinity,  J stands for freely estimated (in spur
          components), and other letters are the same when  parameters
          that correspond to their positions equal each other.
          Unmatched other letters are freely estimated and thus just
          like J's but in k and binary K components of the model names.            

    Keqs: A list of K equality constraints indexed by  model names
          where each element is a vector of character strings whose
          names are followers and values are leaders. 

    keqs: Similar to 'Keqs' but for activities parameter constraints.
          This is 'NULL' if  'activity' in 'g' is 'TRUE'. 

_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:

     'mkSpurs',  'ccems'

_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,TCC=TRUE) 
     gridL <- mkGrids(g) 
     print(gridL$chunk)
     topology <- list(
         heads=c("R1X0","R2X2","R4X4","R6X6"), 
         sites=list(                # s-sites are already filled only in (j>1)-mers 
             a=list(  #a-site                                                    thread
                 m=c("R1X1"),                                            # monomer   1
                 d=c("R2X3","R2X4"),                                     # dimer     2
                 t=c("R4X5","R4X6","R4X7","R4X8"),                       # tetramer  3
                 h=c("R6X7","R6X8","R6X9","R6X10", "R6X11", "R6X12")     # hexamer   4
             ), # tails of a-site threads are heads of h-site threads
             h=list(   # h-site
                 m=c("R1X2"),                                            # monomer   5
                 d=c("R2X5", "R2X6"),                                    # dimer     6
                 t=c("R4X9", "R4X10","R4X11", "R4X12"),                  # tetramer  7
                 h=c("R6X13", "R6X14", "R6X15","R6X16", "R6X17", "R6X18")# hexamer   8
             )
         )
     )

     g <- mkg(topology,TCC=TRUE) 
     gridL <- mkGrids(g,maxTotalPs=2) 
     print(gridL$chunk)

     gridL <- mkGrids(g,maxTotalPs=4) 
     # the next line should be run separately since its output is large.
     # print(gridL$chunk)

