Postdoctoral Scholar Donald Danforth Plant Science Center
Phenotypic data is used to measure a variety of traits that can be traced to genes. In this study I use a mathematical approach to generate new traits that describe dynamic processes. By abstracting the process of the above ground Setaria plant tissue growth, a top-down trait based dynamical model was constructed. This model describes events that occur within the growth and development of above-ground tissue. The mathematical framework considers above-ground plant tissue as either ‘resource generating’ or ‘non-resource generating’/’structural’. Care was taken in the mathematical representation to model the underlying growth and developmental processes, as well as the actual measurables described in the data. The data consist of biomass and height estimates by the image processing software PlantCV, as well as total water usage over 250 Setaria lines across 1100 plants in drought and well-watered conditions. To estimate the parameters of the dynamic processes, the model was fitted to the data. The model parameters were constrained when possible based on plant physiological understanding of Setaria growth and development as well as plant biomass measurements. The heritability of the parameters were calculated for both wet and dry conditions. We found that heritability of these parameters differs between wet and dry conditions, as well as certain processes that describe events critical to the dynamics of Setaria growth as described by the model. The parameters in our model are describing growth and developmental decisions of the plant. This method provides a novel way to identify plant phenotypic trait for identifying new genes that control dynamic processes. This novel framework will be used in the future to understand if phenotypic variability may be emergent from the interaction between environmental space searching strategies, biomass allocation strategies, and genotype.
Coauthors: Ivan Baxter – Donald Danforth Plant Science Center