How to …

... choose free fluxes?

Since v7.0, this step is not really necessary. Using MTF input format allows user to create its .netw file with just a network. And at first run of influx_si, a new file .tvar.def with default values will be generated. It can be used as .tvar file with partition between free and dependent fluxes. User can label some fluxes as constrained in this .tvar. In which case, a newly introduced possible incoherence in flux partition will be signaled as error and recommendations will be given about how to avoid such situation.

Don’t use 0 as starting value in NET fluxes as it might lead to singular matrices in cumomer balances.

... get statistical information for a given set of free fluxes without
fitting measurements?

Put these values in the corresponding FTBL file as starting values for free fluxes and use influx_si with --noopt option.

... accelerate calculations?

You can relax stopping criterion and pass from 1.e-5 (by default) to, for example, 1.e-2 if this precision is sufficient for you. Use optctrl:nlsic:errx option in FTBL file (section OPTIONS) for this.

If you mean to accelerate Monte-Carlo simulations in Unix environment, you can use a hardware with many cores. In this case, the wall clock time can be reduced significantly. Note that distant nodes, even inside of the same cluster, are not used in the such kind of Monte-Carlo simulations.

Check that your system is not using swap (disk) memory. If it is the case, stop other applications running in parallel with influx_si. If possible extend the RAM on your hardware.

... extend upper limit for non linear iterations?

By default, this value is 50 which should be largely sufficient for most cases. If not, you can set another value via optctrl:nlsic:maxit option in the FTBL file (section OPTIONS). But most probably, you would like to check your network definition or to add some data or to change a substrate labeling, anyway to do something to get a well defined network instead of trying to make converge the fitting on some biologically almost meaningless situation.

... make FTBL file with synthetic data?

Note

Deprecated since v7.0. Simply use *.sim files as MTF input files (cf. examples in notes of res2ftbl_meas: simulated data and ffres2ftbl: import free fluxes)

Follow for example steps outlined hereafter:

  • edit FTBL file(s) with NA in measurements and realistic SD, name it e.g. new_NA.ftbl

  • simulate data:

    $ influx_s.py --noopt --addnoise new_NA

  • prepare FTBL sections with simulated data:

    $ res2ftbl_meas.py new_NA_res.kvh

    It will create file (or files if there are parallel experiments) with synthetic data formatted for inclusion in FTBL file: new_NA_sim1.ftbl, new_NA_sim2.ftbl, etc.)

  • copy/paste simulated data to a new file new.ftbl with numeric data instead of NA.

  • use FTBL with synthetic data:

    $ influx_s.py new.ftbl
... do custom post-treatment of Monte-Carlo iterations?

Let suppose you want to filter some of Monte-Carlo (MC) iterations based on their cost values. In OPTIONS/posttreat_R of your FTBL file add save_all.R. The file save_all.R can be found in R directory of influx_si distribution. Execution of save_all.R at the end of calculations will simply save all session variables in mynetwork.RData file (supposing that your FTBL file is named mynetwork.ftbl). In particular, you need free_mc matrix which contains free parameters (each column results from a given MC iteration). After that you can open an interactive R session in your working directory and run something similar to:

# preparations
load("mynetwork.RData") # to obtain .RData file in 'my_res/my/tmp/', use 'posttreat_R save_all.R' in 'my.opt' file
source(file.path(dirx, "libs.R"))
source(file.path(dirx, "opt_cumo_tools.R"))
#source(file.path(dirx, "opt_icumo_tools.R")) # uncoment for influx_i use
tmp=sparse2spa(spa)

# doing something useful
# here, we calculate a vector of cost values, one per MC iteration
cost_mc=apply(free_mc, 2, function(p) cumo_cost(p, labargs))
# do something else ...

If, instead of cost values, you need for example a full set of net-xch fluxes then do

allflux_mc=apply(free_mc, 2, function(p) param2fl(p, labargs)$fallnx)

for residuals, do:

resid_mc=apply(free_mc, 2, function(p) lab_resid(p, FALSE, labargs)$res)

After that, you can filter or do whatever needed with obtained vectors and matrices.