When watching the presentation on the Genetics of Parkinson's Disease by Ellen Sidransky featured yesterday. I could not but notice a few other things on top of yesterday's question and meta-question.
For one, it is abundantly clear that it is not because a metabolic circuit becomes disrupted , thanks to the lack of a certain gene, that it necessarily implies a large physiological change (also see ). Let us note that the robustness in random processes observed in Nature parallels some of the information preserving capacity in compressive sensing with random matrices. When people find that a specific gene is responsible for one specific disease, it really means that the culpable gene is a single point of failure. But what about those, probably more important, cases when no specific gene is a single point of failure, how do we investigate those cases in some automatized fashion (instead of just relying on chance as we have done so far) ?
Or let me put it in a different perspective that is familiar to readers of Nuit Blanche: What measurements are required for the blind deconvolution of these metabolic circuits ? I note the paucity of studies in that regard, see for instance Reverse Engineering Biochemical Networks and Compressive Sensing, It's quite simply, the stuff of Life... and Instances of Null Spaces: Can Compressive Sensing Help Study Non Steady State Metabolic Networks ?.
In the post-screening analysis, a natural extension of compressive sensing could include a better sensor and better image reconstruction for confocal imaging but I wonder if group testing and other known devices could help the other processes like it did for High Throughput Testing ?
 In Properties of Metabolic Networks: Structure versus Function by R. Mahadevan and B. O. Palsson on can read:
It is observed that, functionally speaking, the essentiality of reactions in a node is not correlated with node connectivity as structural analyses of other biological networks have suggested.
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