Network models for dissecting plant development by functional mapping

Song Wu, John Stephen Yap, Yao Li, Qin Li, Guifang Fu, Jiahan Li, Kiranmoy Das, Arthur Berg, Yanru Zeng, Rongling Wu

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)183-187
Number of pages5
JournalCurrent Bioinformatics
Issue number3
StatePublished - Sep 1 2009

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics


Dive into the research topics of 'Network models for dissecting plant development by functional mapping'. Together they form a unique fingerprint.

Cite this