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)

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!

Paper Analysis: Microarray technology: beyond transcript profiling and genotype analysis

Microarray technology: beyond transcript profiling and genotype analysis
Nature Reviews Genetics 7, 200-210 (March 2006) | doi:10.1038/nrg1809

I have spent nearly three days reading this review on microarray. It is partly because this paper involves too many new concepts for me to digest, partly because, I have to admit, I have wasted too much time on BBS, films and music ^_^. Even until now I still cannot declare to absorb all materials in this paper, but i think it is better to take some notes here for it may urge me to concentrate on research.

This paper describe the following microarray development

Process	Status*
*From most to least developed: mature, in progress, under development, early stages, pilot phase, idea. CGH, comparative genomic hybridization; ChIP-on-chip, on-chip chromatin immunoprecipitation.
Transcriptional profiling	Mature, but still to be improved
Genotyping	Mature, but still to be improved
Splice-variant analysis	In progress
Identification of unknown exons	Early stages
DNA-structure analysis	Pilot phase
ChIP-on-chip	In progress
Protein binding	Under development
Protein–RNA interaction	Idea
Chip-based CGH	In progress
Epigenetic studies	Under development
DNA mapping	Mature
Resequencing	In progress
Large-scale sequencing	Under development
Gene/genome synthesis	Early stages
RNA/RNAi synthesis	Pilot phase
Protein–DNA interaction	Under development
On-chip translation	Under development
Universal microarray	Under development

He thoughts transcriptional profiling is relative in technique but the data analysis and interpretation. Some organization are take effect in this path, such as Microarray Gene Expression Data (MGED) Society, Gene Ontology Consortium and Bioconductor.

Expanding RNA studies the transcried RNA profile is a mixture of pre-mRNA, various form of alternative spliced mature mRNA, non-coding RNA and regualatory RNA. If we think about the effect of alternative splicing, it is possible that we may ignorant other forms and exons in the genome sequence which is not seen in our experiement samples. Then how to know other exons and what condition they are retained in mature mRNA, we can built an array consisting of oligonucleotide representing all known exons from genome annotation analysis. This array can then be used for the above condition.

Another question arising is that how can we find exons that escape the notice of genome annotation analysis. "One option is to synthesize oligonucleotides that correspond to the sequences at the exon–intron boundaries with their 5' ends attached to the chip surface "

Another approach is the entire genome microarray (tiling path), but the fragment is rather long which may miss some active sites of interest.

ChIP-on-chip on-chip chromatin immunoprecipitation. But, how this technique get high throughput if only one kind of protein can be precipitated due to the specificity of antibody binding? Needs more reading to understand this technique.

The author also predicted that " all analyses that are carried out with DNA are feasible at the level of RNA also."

comparative genomic hybridization (CGH), a method that is used to analyse variations in DNA copy number

The following part of this paper describes on demand sythesis based on microfluidic microarray, such as probe production (parallel production of large amount of different of oligomers), gene synthesis, RNAi production and protein in situ synthesis. Finally he introduced universal microarray platform based on L-DNA with great enthusiasm.

Conclusions:
1. To some extent, microarray technique means a new data-driven method e.g placing data production before intellectual concepts. This method is different from traditional hypothesis driven research in biology but is successful in physics.
2. The global view obtained by microarray approaches might lead researchers to appreciate more complexity of biological systems.
3. Experimental multiplexing by analysing different processes on a single system platform will become important. The in vitro systems biology will emerge competing (or complementing) in silico systems biology.

Here is a list of notable research project about microarray analysis http://filtr.blogspot.com/2007/02/research-projects-on-microarray.html