Translation Of Graph Theory Into Experimental Verification

Question

[GreenDiamond]

Hi BioStars,

Graph theoretical approaches are becoming very common in bioinformatics. Have you ever come across a group that generated a network to construct hypotheses, and then used that information to design follow-up experiments that discovered something new and important?

I am interested to see an example of this translation, regardless of the network generation technique (what represents nodes - genes, etc - and edges), the experimental technique, the organism of study, etc.

Thank you!!

Answer 1

[donfreed]

The Eichler lab created a network of interacting proteins more likely to harbor de novo [mutations in autism probands: http://www.nature.com/nature/journal/v485/n7397/full/nature10989.html]. They then performed follow-up sequencing to confirm that these genes are more likely to be mutated. I did not see a reference for the follow-up study (it may be unpublished), but [this review: http://www.ncbi.nlm.nih.gov/pubmed/24387789] briefly describes the follow-up (page 100).

I am not sure that their follow-up discovered something 'new and important' but it certainly confirmed that their protein network contained many genes likely to have de novo mutations in sporadic autism populations.

[Ashutosh Pandey]

Edited as the links were not visible.

Answer 2

[B. Arman Aksoy]

Here are two such examples from our lab, where networks are being inferred from the data and then they are being used for further prediction followed by experimental follow-up:

1. Perturbation Biology: Inferring Signaling Networks in Cellular Systems
2. Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4 and IGF1R as Synergistic Drug Targets