Current progress in network research: toward reference networks for key model organisms.
The collection of multiple genome-scale datasets is now routine, and the frontier of research in systems biology has shifted accordingly. Rather than clustering a single dataset to produce a static map of functional modules, the focus today is on data integration, network alignment, interactive visualization and ontological markup. Because of the intrinsic noisiness of high-throughput measurements, statistical methods have been central to this effort. In this review, we briefly survey available datasets in functional genomics, review methods for data integration and network alignment, and describe recent work on using network models to guide experimental validation. We explain how the integration and validation steps spring from a Bayesian description of network uncertainty, and conclude by describing an important near-term milestone for systems biology: the construction of a set of rich reference networks for key model organisms.
Srinivasan
Balaji S
BS
318 Campus Drive, Clark Center S251, Stanford, CA 94305, USA. balajis@stanford.edu
Shah
Nigam H
NH
Flannick
Jason A
JA
Abeliuk
Eduardo
E
Novak
Antal F
AF
Batzoglou
Serafim
S
D000818:Animals; D019295:Computational Biology / Q000639:trends*; D003198:Computer Simulation; D005544:Forecasting; D020869:Gene Expression Profiling / Q000639:trends*; D006801:Humans; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000639:trends*; D020543:Proteome / Q000378:metabolism*; D012106:Research / Q000639:trends*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome