4 A Practical Guide to Cancer Systems Biology
2.4. Metabolome
The metabolome is defined as the complete set of small-molecule chemicals
in a cell, a tissue or an organism. Metabolomics is the study of small
molecules, such as sugars and amino acids, which are substrates, products, or
intermediates of metabolic reactions in an organism. The most used methods
to measure metabolome are nuclear magnetic resonance (NMR) spectroscopy
and MS. One of the most popular metabolomics database is Kyoto Encyclo-
pedia of Genes and Genomes (KEGG,http://www.genome.jp/kegg/).
2.5. Bioinformatics
Omics techniques generate big data such as genome, transcriptome, pro-
teome, and metabolome. How to integrate and mine these data is an
important issue.^4 There is no doubt that bioinformatics or computational
biology can help researchers analyze these big omics data. The categories
of bioinformatics contain many subfields such as sequence analysis, genome
annotation, transcriptome and proteome analyses, as well as network biology
and dynamic modeling.^5
Both network biology and modeling are important in the study of
systems biology.^1 Network biology includes network and pathway analyses
which help researchers understand the relationships within metabolic,
protein–protein interaction networks and phospho-signaling pathways.
Dynamic modeling requires gene or protein expression time-series data and
mathematical methods (Fig. 2).^5 Docking simulation such as protein-drug or
DNA-drug docking has the potential to enhance drug development.^6 These
methods can be beneficial for disease biomarker and target identification as
well as drug discovery.
Figure 2. Dynamic modeling. Gene or protein expression time-series data and mathe-
matical methods are required to infer regulatory or signaling networks.