Quantifying Phenotypic Variation Through Local Persistent Homology and Imaging
Understanding the genetic basis of phenotypic variation in organisms is a central problem in developmental and evolutionary biology. In plant science, to gain insights on such problems as how plants respond to environmental changes and how to breed the next generation of crop...
| Other Authors: | |
|---|---|
| Format: | Others |
| Language: | English English |
| Published: |
Florida State University
|
| Subjects: | |
| Online Access: | http://purl.flvc.org/fsu/fd/FSU_2016SP_Li_fsu_0071E_13155 |
| Summary: | Understanding the genetic basis of phenotypic variation in organisms is a central problem in developmental and evolutionary
biology. In plant science, to gain insights on such problems as how plants respond to environmental changes and how to breed the next
generation of crops, a sound quantification of the variation in complex plant phenotypes is crucial. For example, the shape of leaves, the
architecture of root systems, and the morphology of pollen grains are all important and interesting phenotypic traits that require
mathematical informed methods to model their variation comprehensively. In this dissertation, we develop topological methods and
algorithms based on persistent homology, which let us construct informative summaries of the shape of data. We propose a localized form of
persistent homology represented by a continuous persistence diagram field. We prove that such fields are stable and robust to noise and
outliers. This technique lets us produce compact, and yet rich summaries of global and local morphology useful for modeling and
quantifying variation in complex shapes. This enables statistical approaches such as quantitative trait loci (QTL) analysis, time series
analysis of dynamical traits, and the investigation of correlations between morphological traits to study their evolution and
developmental constraints. We apply the methods to: (i) QTL analysis of multiple tomato introgression lines through a study of leaf shape
and root architecture; (ii) time series analysis of dynamic growing maize root systems; (iii) quantitative analysis of morphology of grass
pollen grains; and (iv) an analysis of the complexity of dryland spatial vegetation patterns. === A Dissertation submitted to the Department of Mathematics in partial fulfillment of the requirements
for the degree of Doctor of Philosophy. === Spring Semester 2016. === April 14, 2016. === leaf shape, local persistent homology, pattern complexity, phenotypic variation, quantitative trait loci
analysis, root architecture === Includes bibliographical references. === Washington Mio, Professor Directing Dissertation; Sudhir Aggarwal, University Representative;
Richard Bertram, Committee Member; Mike Mesterton-Gibbons, Committee Member. |
|---|