Arabidopsis thaliana as a model organism in systems biology.
Significant progress has been made in identification of genes and gene networks involved in key biological processes. Yet, how these genes and networks are coordinated over increasing levels of biological complexity, from cells to tissues to organs, remains unclear. To address complex biological questions, biologists are increasingly using high-throughput tools and systems biology approaches to examine complex biological systems at a global scale. A system is a network of interacting and interdependent components that shape the system's unique properties. Systems biology studies the organization of system components and their interactions, with the idea that unique properties of that system can be observed only through study of the system as a whole. The application of systems biology approaches to questions in plant biology has been informative. In this review, we give examples of how systems biology is currently being used in Arabidopsis to investigate the transcriptional networks regulating root development, the metabolic response to stress, and the genetic regulation of metabolic variability. From these studies, we are beginning obtain sufficient data to generate more accurate models for system function. Further investigation of plant systems will require data gathering from specific cells and tissues, continued improvement in metabolic technologies, and novel computational methods for data visualization and modeling.
Department of Biology, Duke University, Durham, NC 27708, USA.|IGSP Center for Systems Biology, Duke University, Durham, NC 27708, USA.
Jaimie M
JM
Van Norman
Department of Biology, Duke University, Durham, NC 27708, USA.|IGSP Center for Systems Biology, Duke University, Durham, NC 27708, USA.
Philip N
PN
Benfey
D017360:Arabidopsis / Q000502:physiology*; D053263:Gene Regulatory Networks; D008954:Models, Biological; D018517:Plant Roots; D013312:Stress, Physiological; D049490:Systems Biology
Root development; high-throughput methods; metabolism; quantitative traits; transcriptional networks