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Hello,

I am experimenting with PROM code of Chandrasekaran 2010. I am using a COBRA model (.mat) from Palsson 2018 (https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-018-0557-y), also it is in the BIGG database in .json format, so I think it is well-formatted.

I could run the PROM in Matlab but I think I still don't understand the result of the script. It returns two variables, "f" and "v". (As PROM documentation said: the algorithm gives the growth rate (f) and flux response (v) after knock out of all the regulators in the regulatory model).

But I don't understand what those arrays (f ,v ) mean because they don't let you know which genes were knocked out, and also the dimensions don't fit my inputs.

Do you have any information that can clarify the results of the PROM approach?

Regards

Agustin Pardo

Hello,

I have some questions about the methodology of PROM that maybe you could help to understand and solve. I wrote to
Sriram Chandrasekaran but he didn't answer these questions.

I run the PROM methodology as is publish in https://www.pnas.org/content/107/41/17845.abstract. I get the code from https://www.igb.illinois.edu/labs/price/downloads/.

I also read some supplementary documentation (https://link.springer.com/protocol/10.1007/978-1-62703-299-5_6, https://link.springer.com/chapter/10.1007/978-94-017-9041-3_12) to get a better insight in the methodology. But I still have some theoretical/technical concerns that I hope you could help me to solve:

🚩 1) You calculate the probability (P) for the relationship of FT-target pair when they are ON/OFF:

So "P" (probability) is in constraints model formula as:

You can calculate all of these probabilities:
P(A=1|B=1), P(A=1|B=0), P(A=0|B=1) or P(A=0|B=0)
So, which is the probability "P" that you choose to put in the formula when you run PROM? Which of those probabilities make sense when you have a negative or positive regulation?

🚩2) Knocking each TF.

How is it done theoretically? Looking the formula:

I think that when you knock a TF you put a "P = 0". But, what happens if the regulation is negative or positive relation? How do you manage that kind of relationship? You also still have the alpha and beta components.

How do you handle TF knockout?

🚩3) Is there a way to have the results of the PROM without knocking any FT? As we talked before, you get a vector of the size of the TF as a result of PROM, so the non-knockout FT is not in those results.

🚩4) knocking genes of the model. Do you think it is possible to knock out genes (targets) using PROM? I think that I could do that just by removing genes/targets from the COBRA metabolic model before using PROM, but, do you think that is the correct way to knock out targets using PROM?

🚩5) Export the model with integrated regulation constraints (probabilities). Is there a way to export the COBRA model (in mat, json or sbml format) with the regulatory constraints that PROM uses. That is, create a structure of the model that contains the alpha, beta, kappa, and probabilities for each TF/target pair so you could run the model in the future without using the input data from microarrays, and the regulation network because it will be integrated into the model. Do you think it is possible?

Regards