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Editorial [ Hot Topic: Systems Biology, from Archae to Man (Guest Editor: John Aitchison)]
Whole genome sequencing has provided us with genetic “parts lists” of a growing number of model organisms and spawned the development of a remarkable array of new technologies to enable the global and quantitative molecular interrogation of biological systems. These global approaches have also catalyzed the development of computational tools for capturing, quantifying, storing, analyzing, displaying and modeling biological information. While these analyses in isolation provide valuable information about various cellular properties, the integration and collective analysis of different data types can provide important new insights at a systems level.
Ultimately, a major challenge for systems biologists is to explain biological information, at any of its hierarchical levels (DNA, mRNA, proteins and metabolites; to hierarchical levels of dynamic interacting networks comprised of these elements; to extended networks of cells, organs, organisms and ecologies), in terms of digital genomic information and its interplay with external forces that act upon it. Currently the most accessible system to begin a comprehensive understanding of the relationship between the genome and external cues is that of transcriptional regulation. At this level of hierarchy, we can realistically expect to enumerate a core of relevant players and, using global experimental approaches combined with computational tools, build a comprehensive and predictive network of dynamic molecular interactions. These philosophies and goals lie at the heart of Systems Biology and are presented in two reviews in this issue of Current Genomics.
Both reviews highlight the unique challenges and successes of studying cellular regulation from a systems biology perspective and enumerate methods that promise to lead to an understanding of how extrinsic and intrinsic contextual information controls the digital genome. The first article by Facciotti et al. reviews systems biology approaches with a focus on relatively simple prokaryotic systems. This review highlights many aspects of the integrative approach towards fully describing and predicting a simple model organism. The second article by Smith and Thorsson takes these approaches to human innate immunity. While considerably more complex and hindered by the fact that many of the players in the game still need to be identified, this review highlights the advances systems biology enables despite these current limitations.
While these reviews represent the state-of-the art for systems biology, they tend to neglect the impact that the spatial organization of the cell has on regulation. Thus, the third review in the series, by Dundr and Misteli, represents some of the most significant challenges systems biologists must ultimately face to fully appreciate and predict biological behavior at the level of gene regulatory networks. In essence, the challenge presented is to understand the regulation of the genome in the context of a living cell, incorporating ideas such as single cell responses (as apposed to that of the population), the dynamic and temporal binding of regulatory proteins, chromatin organization, and gene positioning within the three-dimensional context of the nucleus. Biologists now have the ability to know the identity of the elements in a system and have many of the tools to measure the concentrations or relationships of these elements. These comprehensive measurements, when coupled with emerging methods to fuse divergent large-scale data sets and computer modeling, can yield a global perspective that will enable the functional characterization of biological systems as a whole. The three reviews emphasize the need to generate different types of dynamic molecular data on defined systems and the tight integration of biological inquiry with technological and computational advances that go hand in hand with iterative model refinement. These principles are the essence of systems biology and promise to ultimately lead to the accurate prediction of cellular behavior.
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