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.
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.
Good quotes related to management hierarchies:
“Companies have been flattening out their management hierarchies in recent years, eliminating layers of middle management that can create bottlenecks and slow productivity.”
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The bossless structure can be chaotic at times, he says….
Since it was founded in 1958, W.L. Gore has operated under what it calls a “lattice” management structure, which relies on teams in place of bosses and traditional chains of command, and which was discussed by Malcolm Gladwell in his 2000 book “The Tipping Point.”
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http://online.wsj.com/article/SB10001424052702303379204577474953586383604.html
Nat. Methods. 2011 Aug; vol. 8(8) pp. 677-83
A large-scale method to measure absolute protein phosphorylation stoichiometries.
Wu R, Haas W, Dephoure N, Huttlin EL, Zhai B, Sowa ME, Gygi SP URL – http://www.ncbi.nlm.nih.gov/pubmed/21725298?dopt=Citation
Q: how does phosphorylation stoichiometry relate to the phosphorylome ? how many of the things are fully phosphorylated ?
http://www.ncbi.nlm.nih.gov/pubmed/21183079
A tissue-specific atlas of mouse protein phosphorylation and expression
interesting to intersect with mouse encode in relation to TFs & gene expression – how does it relate to cell lines ?
http://www.sciencemag.org/content/325/5948/1682.abstract
Relates to the lab’s rewiring paper (Shou et al.) and should have been cited as it has the same conclusion
linkstream2 microblog 