1.

There is no real interpretation here

I like this answer:D

Some people, particularly those not used to thinking in terms of networks, might be tempted to say that if genes are up regulated, then the pathway is up-regulated.

That's exactly my point, and another problem here is weak understanding how ontologies are composed. For phenotype based ontologies I can get list of genes for each category and list of references, from where the information was inferred. And in most general cases I don't have information on up and down regulation unless I go through each reference. But even then, let's say I got gene A up-regulated in 50 references and down-regulated in other 50. It could mean the gene is totally irrelevant for the given category and needs to be excluded from the phenotype, or it could make perfect sense if its expression depends on the experimental factors (as it's hard to imagine 100 perfectly identical experiments).

Of course one might infer that if most genes in a pathway are up-regulated then this pathway is more important in condition A than condition B, and vice versa.

I think this is a nice explanation, but sometimes I struggle to explain that it's not antagonistic to cases with "mixed" genes in the enriched category. I wrote this post to hear all different opinions, because it was hard for me to put this into words, and I want to have decent discussion next time such topic pop ups.

2.

SPIA is a package that tries to track the effects of the changes through the network, sorting out the positive regulators from the negative regulators etc.

Very interesting, thanks for suggesting, I will look into it. I wonder if it's possible to do the same for non-pathway based ontologies, e.g. by inferring up and down regulation from literature (basically the thing I wrote in 1 when answering your comments).

For my money the best way to measure changes in the activity of a pathway is to measure the enrichment of up and down genes that are targets of the pathway, not members of the pathway. However, this is generally not widely available information, and might need to be specifically generated for your pathway of interest from integration of knockout and DNA-binding data.

Super nice idea, not easy to implement though, and hard to get data unless produced specifically. But sounds like a nice scientific challenge.

Interpretation of the result also depends on this context

That's true. If I'm lucky I get pathway, which is tightly connected with studied biological question, so it won't be that difficult to say if up and down genes make sense at all. But sometimes I get 5-10 pathways, which I did not expect (or even heard of), so I was wondering what people do in such cases and how they interpret results, i.e. if they just go though each "enriched" gene in the pathway and check literature for the given context.

Some people try to infer gene regulatory networks

I do that too, but it works better only if one has substantial amount of samples. And I think in most cases it answers a bit more general question, e.g. if the expression of particular genes is connected somehow, which we kind of already have if we study pathways. Or you mean we can use defined pathway structure and simulate genes states? I guess it heavily depends on method, correlation based or pure information theory rather give "associations", and here we also need "sign" and "direction" of interaction, so probabilistic graphical models might be more suitable.