Understanding the genetic machinery of plant growth and development is of fundamental importance in agriculture and biology. Recently, a novel statistical framework, coined functional mapping, has been developed to study the genetic architecture of the dynamic pattern of phenotypic development at different levels of organization. By integrating mathematical aspects of cellular and biological processes, functional mapping provides a quantitative platform in which a seemingly unlimited number of hypotheses about the interplay between genes and development can be asked and tested. However, plant development involves a series of multi-hierarchical, sequential pathways from DNA to mRNA to proteins to metabolites and finally to high-order phenotypes, and thus it is unlikely that the control mechanisms of plant development can be understood using genetic knowledge alone. Here, we describe a network biology approach for functional mapping of phenotypic formation and progression through their underlying biochemical pathways. The integration of functional mapping with information-rich spectroscopic data sets including transcriptome, proteome, and metabolome can be used to model and predict physiological variation and plant development, and will pave the way for future genetic studies capable of addressing the complex nature of growth and development.
All Science Journal Classification (ASJC) codes
- Molecular Biology
- Computational Mathematics