Abstract
Measuring technologies in the field of molecular biology and cellular biology have developed rapidly over the recent years, most obviously in the sequencing field where capacity and throughput have increased by several orders of magnitude. This has been a major factor in systems biology research , which thrives on technologies facilitating efficient and relatively economical genome-wide readout on DNA, mRNA, protein, and metabolome level. With data generation increasing exponentially, we are faced with new challenges of transforming this data into useful models that help to predict the outcome of genomic aberrations and to develop novel diagnostic and therapeutic strategies. There is currently a technological and digital transition from many array-based assays to second-generation sequencing approaches that analyse gene expression , genotype, single nucleotide polymorphisms and methylation patterns. Sequencing technologies are developing rapidly, as are preparatory enrichment techniques, which include the amplification of sample signal or target enrichment to reduce sample complexity. Proteomics and functional assays have also been much advanced as a result of technological progress in mass spectrometry or automatic microscopy and image analysis. Nevertheless, we are still far away from routinely measuring whole proteome data, because of the complexity of different transcripts and post-translational modifications. In spite of this, numerous new or improved analytical techniques embedded in integrated systems approach frameworks will potentially generate clinical usefulness.
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Abbreviations
- BRET:
-
bioluminescence resonance energy transfer
- BS:
-
bisulfite
- CDK:
-
cyclin dependent kinases
- CGH:
-
comparative genome hybridization
- CGP:
-
cancer genome project
- CNVs:
-
copy number variations
- DIGE:
-
differential-in-gel-electrophoresis
- ds:
-
double stranded
- esiRNA:
-
endoribonuclease-derived short interference RNAs
- FISH:
-
fluorescence in situ hybridizations
- FFPE:
-
formalin-fixed and paraffin-embedded
- FRET:
-
fluorescence resonance energy transfer
- GC:
-
gas chromatography
- HPR:
-
Human Protein Atlas
- HRG:
-
Histidine rich glycoprotein
- ICGC:
-
international cancer genome consortia
- IEF:
-
isoelectric focusing
- IHC:
-
immuno-histo chemistry
- IMAC:
-
immobilized metal affinity chromatography
- InDel:
-
insertions/deletions
- IPG:
-
immobilized pH gradient
- ICAT:
-
isotope encoded affinity tags
- LC-MS:
-
liquid chromatography MS
- LUMIER:
-
luminescence-based mammalian interactome mapping
- MBP:
-
methyl-binding protein
- MeDIP:
-
methylated DNA immuno-precipitation
- MGS:
-
microarray -based genomic selection
- MS:
-
mass spectrometry
- MSCC:
-
Methyl-Seq and methyl-sensitive cut counting
- NGS:
-
next-generation sequencing
- NMR:
-
nuclear magnetic resonance
- NSL:
-
non-small lung cancer
- ORF:
-
open reading frame
- PAC:
-
phosphoramidate chemistry
- PGP:
-
personal genome project
- PLA:
-
proximity ligation assay
- PPI:
-
protein-protein interaction
- PSA:
-
prostate specific antigen
- PTM:
-
posttranslational modification
- QQQ:
-
triple quadrupole
- RISC:
-
RNA-induced silencing complex
- RNAi:
-
RNA interferences
- RPPA:
-
reverse phase protein array
- RRBS:
-
reduced representation BS sequencing
- SBGN:
-
Systems Biology Graphical Notation
- SBML:
-
Systems Biology Markup Language
- SNPs:
-
single nucleotide polymorphisms
- SILAC:
-
stable-isotope labelling by amino acids in cell culture
- TAP:
-
tandem affinity purification
- TCGA:
-
The Cancer Genome Atlas
- Y2H:
-
yeast-two-hybrid
- 4sU:
-
4-thiouridine
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Acknowledgments
We thank our colleagues for comments on text and discussions, in particular M. Ralser for his extensive comments and suggestions on the MS part of the manuscript and C. Wierling for contributing Fig. 3.2. Our work is funded through: BL: NGFN IG Cellular Systems Biology, IG Neuronet, IG Mutanom; MS: IG Mutanom, IG Intestinal Modifiers; HL: Max Planck Society
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Lange, B., Schweiger, M., Lehrach, H. (2011). Global Molecular and Cellular Measurement Technologies. In: Cesario, A., Marcus, F. (eds) Cancer Systems Biology, Bioinformatics and Medicine. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1567-7_3
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