Biology Reference
In-Depth Information
nineteenth century and the structural elucidation of DNA, tech-
nologies for genome sequencing such as next generation sequenc-
ing (NGS) have revolutionized biological sciences comparable to
the early times of physics in the beginning of the twentieth century.
However, genome information still remains static information and
molecular processes from transcription to translation to metabolic
networks result in the exponential amplifi cation of compounds in a
multicellular organism. This complexity is neither measurable nor
predictable with technologies nowadays available [ 1 ]. Systems
biology aims to generate and predict dynamic models starting from
the genome sequence. In combination with genome-scale mea-
surements of transcripts, proteins, and metabolites these models
can be verifi ed and optimized leading to the iterative knowledge
cycle of systems biology.
Genome sequencing and the principal role of proteins as key
regulators of life makes proteomics technology to be the backbone
of systems biology approaches to reveal the regulatory principles of
an organism.
Here, techniques for quantitative proteomics are most decisive
[ 2 ]. In the following sections we will discuss a platform (Fig. 1 )
which is suited for high-throughput quantitative exploratory pro-
teomics (MAPA [ 3 ]) and targeted proteomics (MASS WESTERN
[ 4 ]) combined with multivariate statistical data mining (COVAIN
[ 5 - 7 ]). A core module of this platform is a mass spectral reference
database (PROMEX [ 8 , 9 ]), on the one hand storing data from
the exploratory phase, and on the other providing a resource for
targeted proteomics. COVAIN is a data mining toolbox for multi-
variate statistics and modeling approaches also able to integrate
different kinds of molecular data such as proteomics, metabolo-
mics, and transcriptomics data [ 5 - 7 ].
In the following sections these tools are discussed and COVAIN
is used to integrate and statistically analyze MAPA and MASS
WESTERN data of two different growth conditions of
Chlamydomonas reinhardtii .
Furthermore, a proteogenomic approach based on proteomics
and metabolomics data as well as metabolic modeling for
Chlamydomonas reinhardtii is presented.
In another section examples for the combination of MAPA
with metal-oxide affi nity chromatography (MOAC) of phospho-
proteins are presented demonstrating the convenient application
of the complete workfl ow to phosphoproteomics and signaling.
2
Strategies for Quantitative Proteomics in the Era of Systems Biology
To capture the dynamics of a biological system, its components
need to be identifi ed and quantifi ed. Proteomics is confronted
with a task that appears unsolvable. Both in plant proteomics and
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