linkstream2 microblog

http://www.wired.com/business/2012/11/health-tap/?cid=4565974

This paper has: (1) Large-scale datasets compiled from literature and databases, (2) comprehensive gold standards for positive and negative samples, (3) a classifier algorithm (regularized Bayesian), and (4) further analysis beyond “functional prediction”, including an interaction network. It predicts a list of genes having some possible functions, and the authors have experimentally validated them.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694471/

Genome Res. 2009 Jun;19(6):1093-106. Epub 2009 Feb 26.
Exploring the human genome with functional maps.
Huttenhower C, Haley EM, Hibbs MA, Dumeaux V, Barrett DR, Coller HA, Troyanskaya OG.

Large-scale genomic datasets can easily be transformed into various networks. The authors aimed to infer for each particular edge, whether or not it shows up in a particular tissue by training a model based on well curated tissue-specific expression as gold standards. The algorithm arrives at different tissue-specific networks from large-scale genomics datasets; without surprise, tissue-specific networks are more informative in predicting genes corresponding to diseases related to that particular tissue. For instance, a
testis-specific network performs better in predicting genes associated with male fertility phenotypes.

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002694 PLoS Comput Biol. 2012 Sep;8(9):e1002694.
doi: 10.1371/journal.pcbi.1002694. Epub 2012 Sep 27.
Tissue-specific functional networks for prioritizing phenotype and disease genes.
Guan Y, Gorenshteyn D, Burmeister M, Wong AK, Schimenti JC, Handel MA, Bult CJ, Hibbs MA, Troyanskaya O

http://www.ncbi.nlm.nih.gov/pubmed/22825316

http://www.forbes.com/forbes/2007/0507/052.html

QT:”

Using a 35mm slide (see chart below), I argued that my customers needed their Ethernets to grow above a certain critical mass if they were to reap the benefits of the network effect. …. The cost of installing the cards at, say, a corporation would be proportional to the number of cards installed. The value of the network, though, would be proportional to the square of the number of users…..
Why should that be so? The network effect says that the value of that Ethernet card to the person on whose desk it sits is proportional to the number, N, of other computer users he can connect to. Now multiply this value by the number of users, and you have a value for the whole operation that is roughly proportional to N^2.
…
In 1993 George Gilder, seeking to quantify the network effect, uncovered a slide from my 1980s Ethernet sales presentation and the formula saying that value is proportional to N 2. He christened it Metcalfe’s Law….
Recall that there is a critical mass beyond which the value of the network exceeds its cost. Where is this crossover point? You can find it by solving CxN=BxN 2, where C is the constant of proportionality of cost and B is the constant of proportionality of value. The critical mass threshold can be expressed as N=C÷B. Not surprisingly, the lower the cost per connection, the lower the critical mass. The higher the value per connection, the lower the critical mass.

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From C Myers:

Some links to a paper examining the structure of software networks and their connection to biological networks. “Much of the software design involved object-oriented programming (with networks describing interactions among classes, rather than among functions as in procedural call graphs);” Thus, perhaps some of the conclusions are specific to OOP.

http://pre.aps.org/abstract/PRE/v68/i4/e046116

Summary adapted from from Koon-Kiu (KKY):

This paper studied cyclic motifs (cycles) in biological and
technological networks. A cycle can be characterized by the number of clockwise and counter-clockwise links, the number of pass-through nodes and the number of sources/sinks, etc. Direct counting of cycles of various length suggests a dependence between neighboring links, and such dependence is modeled by an interacting spin model. Fitting to the spin model shows that neighboring links tend to be in opposite directions (antiferromagnetic), resulting in a depletion of feedback loops in networks. Stability analysis concluded that the lack of feedback loop stabilizes the system in terms of perturbation around the fixed point.

Ma’ayan A, Cecchi GA, Wagner J, Rao AR, Iyengar R, Stolovitzky G. Ordered Cyclic Motifs Contributes to Dynamic Stability in Biological and Engineered Networks. Proceedings of the National Academy of Sciences 105, 19235-40 (2008) PMID: 19033453
http://ukpmc.ac.uk/abstract/MED/19033453/reload=0;jsessionid=aeku6lFJSKlT8wnr9czW.12

Summary adapted from Declan (DC):

The authors attempt to devise a few simple statistical metrics using high-throughput experimental data (many experiments involving immuno-precipitation coupled with mass spec) in order to predict protein-protein and domain-domain interactions involved in
transcription-related complexes. Each experiment entails using mass spec in order to identify the “prey” proteins that associate with a given “bait” protein. Broadly, protein-protein interactions between such prey proteins are predicted with statistical metrics that assign a likely interaction between a pair of proteins if that pair consistently co-occurs (in high abundance) across multiple
experiments. An example of one of their well-performing metrics is the Sorenson coefficient, which is the ratio of twice the number of experiments in which both proteins occur to the number of experiments in which either or both of these proteins occur (naively, this can be thought of as the degree of intersection between the experiments in which Protein A occurs and the experiments in which Protein B occurs). Using the top 10% of predicted interactions for each of their 4 statistical metrics, they validate many interactions with data from the literature, and they also perform experimental validation and docking studies in order to validate a tiny number of their
predictions. They supply their resultant networks as web-accessible data files.

Mazloom AR et al.
Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes. PLoS Computational Biology. 7, e1002319 (2011) PMID: 22219718
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002319

Functions of Bifans in Context of Multiple Regulatory Motifs in Signaling Networks
Azi Lipshtat, , Sudarshan P. Purushothaman, Ravi Iyengar and Avi Ma’ayan http://www.cell.com/biophysj/abstract/S0006-3495(08)70511-3
Biophys J. 94, 2566-2579 (2008) PMID: 18178648

Summary adapted from Chao (CC):

The authors constructed ordinary differential equations to model the quantitative dynamical behavior in an example bifan motif, in which two kinases p38alpha and JNK1 cross-regulate two transcription factors ATF2 and Elk-1. The simulation indicates that the bifan motif provide temporal regulation of signal propagation and can act as signal sorters, filters, and synchronizers. Bifan motifs with OR gate configurations mediate rapid responses, whereas the one with AND gate configurations introduces delays and allows prolongation of signal outputs. The authors also conducted several sets of simulations, using different initial conditions or considering bifan in a more complex network, and found that synchronization is a robust property of bifan motifs. This study makes a thorough investigation into the dynamical characteristics of the bifan motif based on decent mathematical models; however, there is no experimental result to further support those simulation results.

http://www.biotechniques.com/BiotechniquesJournal/2012/July/The-new-molecular-gastronomy-or-a-gustatory-tour-of-network-analysis/biotechniques-332722.html