A Systems View of Responses to Nutritional Cues in Arabidopsis: Toward a Paradigm Shift for Predictive Network Modeling

One of the major challenges of plant biology is to understand and model plant molecular networks coordinating growth and development, in response to nutritional cues. Further, the predictions and/or the genetic modifications of these responses would have the potential of providing solutions to tweak nutrient inputs of the entire food chain. However, the functioning of a plant as a system is not the result of a simple network, but rather a combination of multiple, intertwined, dynamic, and linear or nonlinear interactions between its elements (DNA, RNA, proteins, metabolites, organelles, cell types, organs, etc.). In addition, this sessile system has to face a combination of fluctuating environmental conditions, including biotic and abiotic stresses. It becomes obvious that the comprehensive knowledge of how this plant system functions in its environment cannot be achieved by the sequential characterization of its elements one by one, or a single class of elements in isolation of the others. Thus, in plant science as well as in other fields of fundamental biology, a paradigm shift is needed. The complexity of the system is so large that understanding it as a whole is without any doubt a necessity to further understand biological systems. Indeed, the reductionist approach is mainly based on reverse genetics (use of mutants or overexpressors to decipher gene function). Several articles cite the fact that the reductionist approach, the one used by biologists on a daily basis, would fail, if it was applied to the study of complex physical system such as a radio (Lazebnik, 2002). Removing components of a radio would probably identify the major components of the studied systems (e.g. speakers or antenna), but it certainly will never give a global understanding of how the radio receives an electromagnetic signal and transform it into audible sounds. This change in our point of view needs to be applied to biological systems, and in particular to plant systems biology.