| According to the interpretation of Systems Biology as the ability to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools, some typical technology platforms are [[phenomics]], organismal variation in [[phenotype]] as it changes during its life span; [[genomics]], organismal [[deoxyribonucleic acid]] (DNA) sequence, including intra-organismal cell specific variation. (i.e., [[telomere]] length variation); [[epigenomics]]/[[epigenetics]], organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., [[DNA methylation]], [[Histone acetylation and deacetylation]], etc.); [[transcriptomics]], organismal, tissue or whole cell [[gene expression]] measurements by [[DNA microarray]]s or [[serial analysis of gene expression]]; [[interferomics]], organismal, tissue, or cell-level transcript correcting factors (i.e., [[RNA interference]]), [[proteomics]], organismal, tissue, or cell level measurements of proteins and peptides via [[two-dimensional gel electrophoresis]], [[mass spectrometry]] or multi-dimensional protein identification techniques (advanced [[High-performance liquid chromatography|HPLC]] systems coupled with [[mass spectrometry]]). Sub disciplines include [[phosphoproteomics]], [[glycoproteomics]] and other methods to detect chemically modified proteins; [[metabolomics]], measurements of small molecules known as [[metabolites]] in the system at the organismal, cell, or tissue level;<ref name=":1">{{Cite journal|last=Cascante|first=Marta|last2=Marin|first2=Silvia|date=2008-09-30|title=Metabolomics and fluxomics approaches|journal=Essays in Biochemistry|language=en|volume=45|pages=67–82|doi=10.1042/bse0450067|pmid=18793124|issn=0071-1365}}</ref> [[glycomics]], organismal, tissue, or cell-level measurements of [[carbohydrate]]s; [[lipidomics]], organismal, tissue, or cell level measurements of [[lipids]]. | | According to the interpretation of Systems Biology as the ability to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools, some typical technology platforms are [[phenomics]], organismal variation in [[phenotype]] as it changes during its life span; [[genomics]], organismal [[deoxyribonucleic acid]] (DNA) sequence, including intra-organismal cell specific variation. (i.e., [[telomere]] length variation); [[epigenomics]]/[[epigenetics]], organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., [[DNA methylation]], [[Histone acetylation and deacetylation]], etc.); [[transcriptomics]], organismal, tissue or whole cell [[gene expression]] measurements by [[DNA microarray]]s or [[serial analysis of gene expression]]; [[interferomics]], organismal, tissue, or cell-level transcript correcting factors (i.e., [[RNA interference]]), [[proteomics]], organismal, tissue, or cell level measurements of proteins and peptides via [[two-dimensional gel electrophoresis]], [[mass spectrometry]] or multi-dimensional protein identification techniques (advanced [[High-performance liquid chromatography|HPLC]] systems coupled with [[mass spectrometry]]). Sub disciplines include [[phosphoproteomics]], [[glycoproteomics]] and other methods to detect chemically modified proteins; [[metabolomics]], measurements of small molecules known as [[metabolites]] in the system at the organismal, cell, or tissue level;<ref name=":1">{{Cite journal|last=Cascante|first=Marta|last2=Marin|first2=Silvia|date=2008-09-30|title=Metabolomics and fluxomics approaches|journal=Essays in Biochemistry|language=en|volume=45|pages=67–82|doi=10.1042/bse0450067|pmid=18793124|issn=0071-1365}}</ref> [[glycomics]], organismal, tissue, or cell-level measurements of [[carbohydrate]]s; [[lipidomics]], organismal, tissue, or cell level measurements of [[lipids]]. |