Skip to main content
ARS Home » Southeast Area » Gainesville, Florida » Center for Medical, Agricultural and Veterinary Entomology » Chemistry Research » Research » Publications at this Location » Publication #343022
Research Project: Insect, Nematode, and Plant Semiochemical Communication Systems

Location: Chemistry Research

Title: Application of mathematical models and computation in plant metabolomics

Author
item Willett, Denis
item Rering, Caitlin
item ARDURA, DOMINIQUE - University Of California
item Beck, John

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 7/28/2017
Publication Date: 5/1/2018
Citation: Willett, D.S., Rering, C.C., Ardura, D.A., Beck, J.J. 2018. Application of mathematical models and computation in plant metabolomics. In Sarker, S.D., Lutfun, N., editors. Computational Phytochemistry. 1st edition. Netherlands, Amsterdam: Elsevier. p. 231-254.

Interpretive Summary: For millennia, humans have used plants, and ultimately their chemical components, for medicinal or nutritional roles. This investigation of plants’ chemical constituents has greatly evolved over the centuries of plant-based research. Starting from the extraction and identification of plant-based bioactive components, such as historical salicin (the precursor for aspirin) or more recent paclitaxel (a potent anti-cancer compound), plant-based research now includes plant metabolomics that can help delineate plants by their chemical secondary metabolites, plant genetic-based chemical biomarkers, or functional genetics of a plant’s response to various biological or environmental stressors. Here, we discuss the invaluable contributions of mathematical and computational modeling for analyses of the plethora of metabolomics data. Important in this chapter is the application of statistical methods for the improved visualization and interpretation of plant metabolomics data, and their relevance for future project planning.

Technical Abstract: The investigation and reporting of plants’ chemical constituents has greatly evolved over the centuries of natural products and phytochemical research. Starting from the extraction and identification of plant-based bioactive components, such as historical salicin or more recent paclitaxel, phytochemistry-based research now includes plant metabolomics that can help delineate chemotaxonomy, phylogenetic biomarkers, or functional genetics of a plant’s response to biotic or abiotic stressors. Here, we discuss the invaluable contributions of mathematical and computational modeling for analyses of the plethora of metabolomics data, as well as the predictive power of statistical modeling. Important in this chapter is the application of statistical methods for the improved visualization and interpretation of plant metabolomics data, and their relevance for future project planning.