Computational Modelling and Prediction of Plant Genome Evolution

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Evolution is a process that involves an organism's DNA changing over time. Recognizing how genomes evolve is particularly important for crop species that exhibit rapid rates of evolution. The fast-increasing number and diversity of sequenced genomes, in addition to advances in computational infrastructure and computing power, opens the door to analyzing the dynamics of evolution at a whole-genome resolution. The long-term objective of my research program is to improve the scientific knowledge of genome evolution in various biological systems through formal modelling and mathematical analysis. In particular, evolutionary patterns will be scrutinized not only from the conventional aspect based on genes, but also using novel approaches based on transposable elements (a type of repetitive elements) in the genome. Transposable Elements (TEs) are DNA sequences that have the ability to move or copy to new positions within a genome. In the past, our group has created theoretical and statistical methods to predict relative age order of TEs and a TE-interaction network from a single genome. The methods will be extended to the genomes of six related Brassica crop species to help elucidate the evolution of TEs woven throughout the divergence of these species, which will enhance our understanding of the impact of TEs in defining and differentiating the genome structure between species. We will also study and model polyploid evolution using Brassica species as a specific genus of focus and develop pertinent algorithms to predict evolutionary processes under the model. The prediction will take into account both maximum parsimony and likelihood. A genome tripling algorithm specifically for the whole genome triplication problem as occurred in Brassica species will be investigated to reconstruct the ancestor genome of these species. Resolving how the Brassica species evolved and identifying their shared evolutionary pathways will relate to how important agronomic traits adapt, thus will offer many economic benefits for genetically improving these crops. The computational methods, algorithms, and ancestor genome reconstruction developed for Brassica evolution could be transferred to other important crop species to guide genomic-assisted breeding, as all economically important crops are polyploids. Overall, my research program focuses on understanding genome evolution through both conventional models to study evolutionary mechanisms directly from gene orders and taking into consideration the evolution of TEs that serve as an ancestral record in genomes. This work has the potential to significantly improve our understanding of evolution among species, sub-genomes, genomic constitutions, conserved genes, and ultimately phenotypic functions. Moreover, the study of TE evolution provides a novel opportunity to examine genome evolution, which will significantly impact the conventional analysis and provide more comprehensive evidence to model evolutionary paths.