Network Biology 2.0 part 5

Aviv Regev gave a talk "Unbiased Reconstruction of Mammalian Regulatory Networks". This was definitely one of my favorite talks of the conference. She had previously done work that I really liked with Daphne Koller (like the module network paper in nature genetics). She started by saying how she wanted to apply the lessons she had learned in yeast network reconstruction to mammalian models. She wanted a primary cell that actually reflects cell biology and a model where transcriptional responses played a major role in environmental responses.

She chose to work with dendritic cells as they sense large clasess of pathogens via a cohort of receptors and that a lot is known about the receptor pathways but not as much about the transcriptional response.

Her basic flow for doing this was to gather mRNA expression profiles in time course, select regulators, select a minimal signature of regulated genes that was most important, perturb each candidate regulator then measure signature of regulated genes after perturbation and derive network model.

It seemed to me that the main new thing from her previous work was the choosing the optimal genes to measure and the usage of a neat new expression technology for ~200 gene size. She showed real improvement and integration of data in her work.


Eric E. Schadt gave a talk on "Moving Toward an Understanding of the Molecular Networks Underlying Biological Hydrogen Production by Bacteria". Like the first pacbio talk this one was incredibly piolished and really impressive. As was pointed out in the first talk distinct nucleotide modifications (methylation for instance) create distinct changes in how their SMRT system reads a base. This resutls in letting them create "Kinetic signatures" for each genome. He created these kinetic signatures across 125 strains of R. palustris across the whole genome. He chose R. palustris as a possible bacteria that would be an efficient way to produce hydrogen.

He found that hydrogen production varied signifcantly from strain to strain making a population based systems genetic approach viable. He used the kinetic variation across the entire genome as covariants and mapped them like eQTLs. He then constructed a regulatory network from this variational data.

I found this talk (which I don't really do justice) immensely impressive and I think that pac bio's technology will definitely be something to watch out for in a huge way.

Nicholas Eriksson gave a talk on web-based parallel gwas. He is part of 23andme.com and talked about how they not only genotype their customers but also create a social network for them. In this network they create surveys that ask their customers questions. They use their 20,000 responding customer base with questions from their surveys. They found a few novel SNPs associated with various things like curly hair and I believe parkinsons.

They also stated in a follow up panel that they were indeed likely to patent the novel genes they discovered which I personally find completely ethically reprehensible.