Tutorial on biological networks by Francisco G. Vital-Lopez, Vesna Memišević, and Bhaskar Dutta. (Vital-Lopez, F. G., Memišević, V. and Dutta, B. (2012), Tutorial on biological networks. WIREs Data Mining Knowl Discov, 2: 298–325. doi: 10.1002/widm.1061)
Abstract:
Understanding how the functioning of a biological system emerges from the interactions among its components is a long-standing goal of network science. Fomented by developments in high-throughput technologies to characterize biomolecules and their interactions, network science has emerged as one of the fastest growing areas in computational and systems biology research. Although the number of research and review articles on different aspects of network science is increasing, updated resources that provide a broad, yet concise, review of this area in the context of systems biology are few. The objective of this article is to provide an overview of the research on biological networks to a general audience, who have some knowledge of biology and statistics, but are not necessarily familiar with this research field. Based on the different aspects of network science research, the article is broadly divided into four sections: (1) network construction, (2) topological analysis, (3) network and data integration, and (4) visualization tools. We specifically focused on the most widely studied types of biological networks, which are, metabolic, gene regulatory, protein–protein interaction, genetic interaction, and signaling networks. In future, with further developments on experimental and computational methods, we expect that the analysis of biological networks will assume a leading role in basic and translational research.
As a frozen artifact in time, I would suggest reading this article before it is too badly out of date. It will be sad to see it ravaged by time and pitted by later research that renders entire sections obsolete. Or of interest only to medical literature spelunkers of some future time.
Developers of homogeneous and “correct” models of biological networks should take warning from the closing lines of this survey article:
Currently different types of networks, such as PPI, GRN, or metabolic networks are analyzed separately. These heterogeneous networks have to be integrated systematically to generate comprehensive network, which creates a realistic representation of biological systems.[cite omitted] The integrated networks have to be combined with different types of molecular profiling data that measures different facades of the biological system. A recent multi institutional collaborative project, named The Cancer Genome Atlas,[cite omitted] has already started generating much multi-‘omics’ data for large cancer patient cohorts. Thus, we can expect to witness an exciting and fast paced growth on biological network research in the coming years.
Interesting.
Nature uses heterogeneous networks, with great success.
We can keep building homogenous networks or we can start building heterogeneous networks (at least to the extent we are capable).
What do you think?