Reverse Engineering Biological Networks [antikvár]

With its powerful sequencers, ever-improving gene expression profile and proteomics microarrays, high-resolution mass spectrometry, multi-color flow cytometers, robotics, and microfluidics, high throughput technology is un- doubtedly changing the landscape of biological research. Its ability to measure cellular activities at the molecular level and in genome-wide fashion is open- ing the door to the exploration of fundamental questions about physiological and pathological cellular processes. Among many others, a key venue of in- vestigation opened by the flood of new data has been the development of methodologies to map the very fabric of molecular interactions that underlie cell structure and behavior. Sequencing the genome was like letting Humpty Dumpty fall off the wall. Once broken into its genes, micro-RNAs and a myriad of other tassels that we may not yet fully understand, the complex mosaic of the cell needed to be put together again. The computational and systems biology community has whole- heartedly embraced this challenge and has begun to develop methodologies to infer the network of molecular interactions that could produce a more holistic view of the fragmented knowledge that a random list of genomic pieces may provide. The task of discovering the underlying biochemical wiring, dynamic inter- actions, and design principles of the cellular environment is known as reverse engineering, a name borrowed from other research fields, most notably soft- ware and electrical engineering. But this task goes also by other names, such as pathway inference or system identification. In the process of reverse engineer- ing biological circuits, we are trying to "read the mind" of the unintelligent designer (evolution) that engineered the system. It is without a doubt a very am- bitious dream. In this volume, we favor the term reverse engineering, because the knowledge it generates can then be used to forward engineer biological sys- tems (i.e., to design novel working biological systems from first principles), which constitutes the basis for the budding field of synthetic biology. What is the novelty in reverse engineering? After all, molecular biologists have been painstakingly assembling the dozens of pathways that we know today. Aren't cases such as the developmental network of the endomesoderm in the sea urchin, the p53, NF-kB, and p-catenin signaling networks, the E. coli transcriptional regulatory network, and the many known metabolic routes (to name a few) sterling examples of reverse engineering? Yes, of course. But in each of these examples the mapping of each interaction in the pathway constituted a truly heroic and mostly experimental tour de force. In the age of computation, however, there may be "shortcuts" to accomplish similar feats in a more systematic way. Maybe we can use the high throughput biological data and the unprecedented computer capabilities available to us to systematically Ann. N.Y. Acad. Sci. 1115: xi-xiv (2007). © 2007 New York Academy of Sciences. doi: 10.1196/annals.1391.022