following problem:

I have data on a certain complex association network (from monomers 1 ... 8 to complexes of all combinations, such as, 12 ... 13 ... 18 and so on until 12345678, so that I know

  • the structure of the network (255 vertices)
  • the edges of the network
  • the concentrations of the vertices

From this data I now wish to infer the preferred route(s) of assembly for those complexes - which should be the rate constants for the edges, such as 1 --> 12 or 145 --> 1458. Does that make sense?

I have now tried various standalone applications for network creation/analysis as well as R packages. Metabolic flux analysis seemed promising for me, but it turned out that it only works with set external concentrations and assumes steady-state for all metabolites within the network. Which is not the case for me. Do you know of any alternative method?

Thanks and best wishes, Simon

  • $\begingroup$ Hi. May be this question would be more suitable for cross validated, for example. But anyway, if i got you right, you are trying to put some marks for edges that is associated with the way they need to be assembled? I'm not biologist at all, just try to get your full task. $\endgroup$ – dshulgin Mar 23 '16 at 13:59
  • $\begingroup$ Thanks for your recommendation, I will look into posting it over there. Yes, exactly, I want to find out the relevant routes of assembly (=paths), which should be the ones with the highest "flux" there... $\endgroup$ – Simon Mar 23 '16 at 14:01
  • 1
    $\begingroup$ yes, but the best resource for this question, i guess, this biostars.org $\endgroup$ – dshulgin Mar 23 '16 at 14:04
  • $\begingroup$ Flux balance analysis always assumes steady-state, and in this case concentration data is irrelevant. It sounds like you're after some type of dynamic method that can handle time-varying concentrations. In this case the problem is much more complex, and you really need to describe the problem in more detail. What kinetic assumptions are you willing to make? (Michalis-Menten, or ... ?) Are you going to fit time series data? What are boundary conditions? What type of data do you have, a pulse experiment (unit step input), or ... ? $\endgroup$ – Roland Mar 24 '16 at 22:27

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