Same Model Generated by Two Different Transcriptomes

[Rohak72]

Hi @NantiaL,

I hope you're doing well! I've successfully run pymCADRE on my system, generating models for the infected and un-infected conditions, respectively. However, despite using different ubiquity scores and transcriptomes for each model run, I end up with the same generic model? I've tried implementing pruning but going through each reaction iteratively would take 16-24 hours and I'm not sure if that's worth the time. Is pruning the solution here, or is it an inherent limitation of the software?

Any clarification would be greatly appreciated, thanks!

Best,
Rohak

[NantiaL]

Hey Rohak,

did I understand correctly that you ran only the ranking and not the pruning so far? The pruning would actually result in different final models based on the previous ranking of reactions.

So, although the pruning might take a while, it definitely needs to be done. We have though plans on optimizing the performance of the pruning step.

[Rohak72]

Hi Nantia,

Ah, I see. Given the size of the generalized model, I'm still unsure whether my machine would be able to finish running the pruning step. Do you know of ways to manually reduce/cut down the genes/reactions/metabolites of the model before running the pruning step (beyond the blocked reactions in the generic model)? Thanks!

Best,
Rohak

[NantiaL]

Hi Rohak!

based on your research question you could for example remove pathways (and associated reactions) known to be irrelevant at this point.

Best
Nantia