Editing the Systems biology page:
Working on this paragraph (second one under Associated discipline):
In addition to the identification and quantification of the above given molecules additional techniques analyze the dynamics and interactions within a cell. The interactions studied include
neuroelectrodynamics, the brain computing function as a dynamic system including underlying biophysical mechanisms and emerging computation by electrical interactions
[2];
fluxomics, measurements of molecular dynamic changes over time in a system such as a cell, tissue, or organism; metabolomics, analysis of metabolites in the system
[3];
biomics, systems analysis of the
biome; and molecular biokinematics, the study of "biology in motion" focused on how cells transit between steady states such as in proteins molecular mechanism
[4].
Adding a paragraph in Associate disciplines section:
In approaching a systems biology problem there are two main approaches. These are the top down and bottom up approach. The top down approach takes as much of the system into account as possible and relies largely on experimental results. The RNA-seq technique is an example of an experimental top down approach. Conversely, the bottom up approach is used to create detailed models while also incorporating experimental data. An example of the bottom up approach is the use of circuit models to describe a simple gene network.[5]
Working on Bioinformatics section:
Other aspects of computer science,
process calculi to model biological processes (notable approaches include stochastic
π-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; network-based approaches for analyzing high dimensional genomic data sets. For example,
weighted correlation network analysis is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data analysis, e.g. approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members. Much of the analysis of genomic data sets also include identifying correlations. Additionally, as much of the information comes from different fields, the development of syntactically and semantically sound ways of representing biological models is needed.
[6]
