Still No Sense of Signaling Network Research

As the time of graduation is approaching, I still have no a clear sense of my research subject-insulin signaling network. I would like to admit my laziness and it is mostly because it is a very new and unclear research area. If I have also started with a traditional research, cell culture, gene cloning and purification of proteins, I would mostly finish my research. And now it is too late to switch to an easy topic and it is stupid to do that. Thank that I have read many enlightening papers in this area and learn to use some softwares, why should I give up. It won't be very difficult to graduate no matter what research you have did. It is just a try.

After I realized the above idea, I decided to read systematically publications in this area. Today I am reading the Science STKE Signaling Breakthroughs of the Year. And now another list of paper to be read (The number of papers in this list is increasing expotentially, I don't know when can I have my sense of them)

[1]G. Altan-Bonnet, R. N. Germain, Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol. 3, e356 (2005).[CrossRef][Medline]

[2]J. R. Pomerening, S. Y. Kim, J. E. Ferrell, Jr., Systems-level dissection of the cell-cycle oscillator: Bypassing positive feedback produces damped oscillations. Cell 122, 565–578 (2005).[CrossRef][Medline]

[3]O. Brandman, J. E. Ferrell, Jr., R. Li, T. Meyer, Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310, 496–498 (2005).[Abstract/Free Full Text]

Paper Analysis -2007-03-02

Reconstruction of Cellular Signaling Networks and Analysis of Their Properties Nature Reviews Molecular Cell Biology 6, 99-111 (2005); doi:10.1038/nrm1570

A NETWORK RECONSTRUCTION includes a chemically accurate representation of all of the biochemical events that are occurring within a defined signalling network, and incorporates the interconnectivity and functional relationships that are inferred from experimental data.

This article give a enlightening theoretical analysis of signal transduction networks: the order of magnitude of numbers of network components (receptor, kinase, phophatase), the order of magnitude of interconnectivity(~2.5 degree of interconnectivity per component). We can use Combinatorial Complexity to characterize this idea. The catalog of network components without post-translational modification can be inferred from the results the genome annotation. The spectrom of network components after PTM and protein-protein interaction during varies states of the network is expected to be assayed with future proteomic experimental techniques (though I feel passive with expectation). But what use or what consequences of these large potential spectrum of various network components means?

The following paper it refers may be worth reading.

[1]
Papin, J. A. & Palsson, B. O. The JAK–STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys. J. 87, 37–46 (2004).

[2]
Resat, H., Wiley, H. S. & Dixon, D. A. Probability-weighted dynamic Monte Carlo method for reaction kinetics simulations. J. Phys. Chem. B 105, 11026–11034 (2001)

[3]
Bhalla, U. S. & Iyengar, R. Emergent properties of networks of biological signaling pathways. Science 283, 381–387 (1999).
Describes some of the first large-scale analyses of signalling reactions.

[4]
Hoffmann, A., Levchenko, A., Scott, M. L. & Baltimore, D. The IkappaB–NF-kappaB signaling module: temporal control and selective gene activation. Science 298, 1241–1245 (2002).
Shows the powerful integration of mathematical modelling with experimental investigation

[5]
Lee, E., Salic, A., Kruger, R., Heinrich, R. & Kirschner, M. W. The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol. 1, 116–132 (2003).

[6]
Prill, R., Iglesias, P.A. and Levchenko, A. Dynamic Properties of Small Regulatory Motifs Contribute to Biological Network Organization. PLoS Biology 3(11): e343 (2005)

[7]
Sivakumaran, S., Hariharaputran, S., Mishra, J. & Bhalla, U. S. The database of quantitative cellular signaling: management and analysis of chemical kinetic models of signaling networks. Bioinformatics 19, 408–415 (2003)