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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 interactomics (all molecular interactions within a cell), organismal, tissue, and/or cell. The current authoritative molecular disciple in the field is protein-protein interactions (PPI), although the working definition does not preclude inclusion of other molecular disciplines such as genetic interactions. Other molecular disciplines include; neuroelectrodynamics, the brain computing function as a dynamic system including underlying biophysical mechanisms and emerging computation by electrical interactions ; 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 ; 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.

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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.

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Other aspects of computer science, informatics, and statistics are also used in systems biology. These include new forms of computational models, such as the use of 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.