Posts Tagged ‘networks’

correlated noise in TF co-association / FFLs

March 9, 2013

http://dx.doi.org/10.1016/j.bpj.2013.01.033

Cross Talk and Interference Enhance Information Capacity of a Signaling Pathway Sahand Hormoz
QT:”
A recurring motif in gene regulatory networks is transcription factors (TFs) that regulate each other and then bind to overlapping sites on DNA, where they interact and synergistically control transcription of a target gene. Here, we suggest that this motif maximizes information flow in a noisy network. Gene expression is an inherently noisy process due to thermal fluctuations and the small number of molecules involved. A consequence of multiple TFs interacting at overlapping binding sites is that their binding noise becomes correlated. Using concepts from information theory, we show that in general a signaling pathway transmits more information if 1), noise of one input is correlated with that of the other; and 2), input signals are not chosen independently.

Complex networks: Degrees of control : Nature : Nature Publishing Group

March 3, 2013

http://www.nature.com/nature/journal/v473/n7346/full/473158a.html

Influential Few Predict Behavior of the Many: Scientific American

February 28, 2013

QT:” looked at the entire human metabolic network and found that concentrations of about 10 percent of the body’s 2,763 metabolites could be used to determine the levels of all the rest.”

http://www.scientificamerican.com/article.cfm?id=influential-few-predict-behavior

Predicting the Binding Patterns of Hub Proteins: A Study Using Yeast Protein Interaction Networks

February 24, 2013

used the singlish/multi concept. Then used machine learning to predict whether a protein is singlish or multi

PLOS ONE
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0056833
Taner Z. Sen

Influential Few Predict Behavior of the Many: Scientific American

February 19, 2013

http://www.scientificamerican.com/article.cfm?id=influential-few-predict-behavior

Article: Health Science: Finding The Doctors Your Doctor Trusts

November 17, 2012

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

Exploring the human genome with functional maps.

November 11, 2012

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.

Tissue-specific functional networks for prioritizing phenotype and disease genes.

November 8, 2012

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

Network medicine: linking disorders. Hum Genet. 2012 – PubMed – NCBI

November 3, 2012

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

It’s All In Your Head – Forbes – Nice description of Metcalfe’s Law by its inventor

October 13, 2012

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.