Modeling Biomedical Networks

steady state vs. equilibrium
If the rate of change of all variables (concentrations of matters) are constant we get a steady state. If Additionally all reactions fluxes are zero, we have an equilibrium.

Calculating steady state
There are several numerical methods to calculate steady state, such as improved Newton method, forward integration and backward integration. However none of them are perfect even to find a steady state in complex systems, which may have several steady states.

Metabolic Control Analysis
MCA describes how the systems reacts to changes of parameters. Elasticities describes how the reaction rates depend on the metabolite concentrations. Control coefficients describes how the systems behavior depend on the reaction rates

References:
http://projects.eml.org/downloads/copasi/CopasiTutorial.pdf

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]