Systems Biology - Associated Disciplines

Associated Disciplines

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-organisamal cell specific variation. (i.e. Telomere length variation etc.).

• Epigenomics / Epigenetics

Organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e. DNA methylation, Histone Acetylation etc.).

• Transcriptomics

Organismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression

• Interferomics

Organismal, tissue, or cell level transcript correcting factors (i.e. RNA interference)

• Translatomics / 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 HPLC systems coupled with mass spectrometry). Sub disciplines include phosphoproteomics, glycoproteomics and other methods to detect chemically modified proteins.

• Metabolomics

Organismal, tissue, or cell level measurements of all small-molecules known as metabolites.

• Glycomics

Organismal, tissue, or cell level measurements of carbohydrates.

• Lipidomics

Organismal, tissue, or cell level measurements of lipids.

In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell. This includes:

• Interactomics

Organismal, tissue, or cell level study of interactions between molecules. Currently the authoritative molecular discipline in this field of study is protein-protein interactions (PPI), although the working definition does not preclude inclusion of other molecular disciplines such as those defined here.

• NeuroElectroDynamics

Organismal, brain computing function as a dynamic system, underlying biophysical mechanisms and emerging computation by electrical interactions.

• Fluxomics

Organismal, tissue, or cell level measurements of molecular dynamic changes over time.

• Biomics

systems analysis of the biome.

The investigations are frequently combined with large-scale perturbation methods, including gene-based (RNAi, mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries. Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality. A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists, engineers, and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.

The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., Flux balance analysis).

Other aspects of computer science and informatics are also used in systems biology. These include:

• New forms of computational model, such as the use of process calculi to model biological processes (notable approaches include stochastic -calculus, BioAmbients, Beta Binders, BioPEPA and Brane calculus) and constraint-based modeling.
• Integration of information from the literature, using techniques of information extraction and text mining.
• Development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via loose coupling of software, websites and databases, or commercial suits.
• Development of syntactically and semantically sound ways of representing biological models.