The paper aims at developing a high level conceptual framework of reasoning and problem solving for bioinformatics. Putting it simple, it is the goal of the authors to use this framework for identifying ways to aid biologists and bioinformatics people using computer technologies. That is actually not too far off what we are doing, and that is why it could be worthwhile to take a look at what the authors came up with.

Link to the paper:

http://www.informatik.uni-rostock.de/~hs162/stuff/cognitivebioinformatics.pdf

Suggested reading: mainly the first half of section 3, and if you want also the rest

I do not want to discuss the paper itself, but rather use it as a starting point that provides us with the needed vocabulary and the way of thinking to discuss the following questions:

What are typical higher-level problems in our graduate school: experiment design, model building,...?

For these concrete problems:

What would be examples of "goal knowledge", "strategic knowledge", "domain knowledge" and "new knowledge"?
Who or what provides the needed knowledge: oneself (experience), Google, eDB, Model, Simulation, lucky guesses,...?

Finally:

Do we have all the technology in place to aid us with our higher-level problem solving, or are there still gaps to be filled?
Are the pieces of technology that we have connected well enough?
Is the technology that we have good enough or do we need to upgrade? (e.g. from "lucky guesses" to the combined experience stored in eDB)
Where do we still spend too much time on digging for information instead of finding solutions?