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]

Omics is Just a Startup

When I was listening the report titled Using Genomics to Explore the Microbial World by Prof. James Tiedje this afternoon, an idea had been daunting in my mind all the time. "Omics is dead" -I forgot where I read this remarks, but it stroke me then and now. Omics is like listing all the components of a computer. However, due to technique limitations and time constraints, we will never be able to get a full list of genes and proteins, though genomics and proteomics optimisticly promised. Even if we could get the full catalogue of human machine, we still can not understand how human body functions and malfunctions, as knowing all the components of a computer does not necessarily imply understanding its working.

Now besides proteomics and genomics, here comes the metabolomics, with similar promising declarations. As the lates Nature essay (Meet the human metabolome)states,

Metabolomics is the study of the raw materials and products of the body's biochemical reactions, molecules that are smaller than most proteins, DNA and other macromolecules. The aim is to be able to take urine, blood or some other body fluid, scan it in a machine and find a profile of tens or hundreds of chemicals that can predict whether an individual is on the road to a disease, say, or likely to experience side-effects from a particular drug.

In fact, researchers in metabolomics are even more optimistic, declaring that

Small changes in the activity of a gene or protein (which may have an unknown impact on the workings of a cell) often create a much larger change in metabolite levels particular concentrations and combinations can reveal something about drugs or disease

However, I am suspecious about their promise. First, considering the great diversity of metabolites in human fluids, we still have not a powerful enough assay to identify the all metabolite in a high-throughout manner and measure their concentrations. Second, the changes in the metabolome is more susceptible to enviromental factors, thus it will be difficult to tell significant changes related to human diseases from temporal fluctuations.

Anyway, let be a little optimistic, omics is just a startup!

Xianghong Zhou's Papers

If you know the enemy and know yourself, you need not fear the result ofa hundred battles.

--Sun Tze, the Art of War

Comments on Zhou's papers:
1. Gene Aging Nexus: A Web Database and Data Mining Platform for Microarray Data on Aging
keywords:
meta-analysis: by first extracting expression patterns form individual microarray datasets and then identifying recurrent signals, these approaches may enhance signal-noise separation.
differential expression analysis:
co-expression analysis: Zhou proposed a new method to mine regulatory modules in previous papers Mining dense subgraphs across massive biological networks for functional discovery.
no major biological breakthrough.

2. Integrative missing value estimation for microarray data
Question Answered:
Due to the inherent noise and the limitation of experimental systems, a microarray dataset on average has more than 5% missing values, affecting more than 60% of the genes. Such missing values made some subsequent analysis methods inapplicable or greatly decrease their performance. Thus the question of missing value estimation.

Basic Idea:
How to choose neighboring genes when not enough information is available in internal microarray dataset. Intuitively, if a set of genes frequently show expression similarity to the target gene over multiple data sets, they constitute a robust neighborhood which tend to show expression co-variations with the target gene.

other concepts:
LLS Local Least Square
Bayesian principle component analysis
singular value decomposition
support vector machines