As with every scientific endeavor, experimental data are essential for modeling and understanding biological processes and systems. Correspondingly, without models and hypotheses, accumulated experimental data are generally unstructured and uninformative. Systems biology research integrates experimental data of diverse types with coherent models, with the goal of understanding the biological processes and systems being investigated.

The molecular components and processes which make up the systems in all living organisms are extremely complex, and often nearly inscrutable. Thus, generating, managing, and analyzing experimental data from biological organisms necessitates using technologically sophisticated tools and techniques. In fact, progress in life-sciences research is ever dependent upon the continued advancement of technologies which support these fundamental research activities:

Data generation: Microarrays, DNA sequencing and mass spectrometry are used to collect data on the organisms under study. These may be combined with other analytic data, e.g., data from collecting and correlating genotype/phenotype data across sample research-related populations. Ongoing technology development aims to increase throughput and efficiency, improve accuracy and sensitivity, and decrease the cost of this work.

Data management: Modern data generation techniques often create enormous datasets, some growing up to terabytes in size. Current technologies provide us with the means to automate portions of collecting, processing, annotating, and integrating experimental data. High-density data storage arrays and database frameworks further enable us to store and retrieve the data more efficiently.

Data visualization and analysis: An extensive range of bioinformatics tools and genomic databases are used by scientists to organize and reduce their raw data, correlate with other sources, identify patterns and trends, and ultimately validate their research hypotheses. Data analysis can also entail using modeling software to simulate the dynamics of biological processes or systems, and compare simulations and experimental observations, again toward the end of validating research hypotheses. Other software tools are essential for translating between multiple researchers´ encoding schemes, and visualizing the interactions and dependencies of the components which make up biological systems.

Research is closely linked with the development of new technology at the Institute for Systems Biology (ISB). For example, Leroy Hood and Alan Aderem co-founded the Nanosystems Biology Alliance (www.nanosysbio.org) in conjunction with faculty at Caltech and the University of California Los Angeles (UCLA) to advance research in the areas of cancer and the immune system. The Alliance´s goal is to integrate newly emerging nanotechnology and microfluidics tools for ultra-rapid disease diagnostics. These technologies have the potential to perform thousands of multi-parameter measurements on a single cell, and to perform such measurements simultaneously on a large number of cells.

In the course of developing new technologies for research projects, the faculty and senior scientists at the ISB have also helped spin off companies to commercialize some of the technologies pioneered at ISB. Examples include Nanostring Technologies (www.nanostring.com), which is developing a barcode approach to molecular profiling, and Cytopeia http://www.cytopeia.com) which is developing cell-sorting technologies based on Ger. Van den Engh´s research. Commercialization represents one way that knowledge is transferred to society, eventually to be used for the improvement of general medical care.