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School of Biological Sciences

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Start date: 1966 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Sciences × search.filters.applied.f.isSeriesOfPublicationTitle: Doctor of Philosophy with a Major in Bioinformatics ×

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Computational Models of Actin Regulation Driving Cytoskeletal Dynamics, Cell Polarity and Motion

2023-04-27 , Hladyshau, Siarhei

Cell morphodynamics is a fundamental biological process required for the healthy functioning of a eukaryotic organism. Understanding its regulatory mechanisms is needed for developing new strategies to treat numerous diseases, including cancer metastasis, excessive angiogenesis, congenital disorders, and chronic wounds. My work focuses on Rho family GTPases (RhoA, Rac1, and Cdc42), known as the key regulators of actin cytoskeleton and cell motion. I developed a computational platform that allowed me to study different configurations of GTPase signaling pathways and capture the complex spatiotemporal distribution of these proteins driving cytoskeletal organization and dynamics. I applied this platform to investigate signaling bistability and the mechanisms of polarity establishment in yeast. I also used this methodology to study wave dynamics of GTPases and F-actin in the cortex of Patiria miniata and Xenopus laevis oocytes. I quantitatively reproduced different actin behaviors in these two organisms and revealed a critical role of quasi-static, low-amplitude patterns in the emergence of complex wave dynamics. Finally, I studied the regulation of cell ruffling by Cdc42 and Rac1 in epithelial breast cancer cells and mouse embryonic fibroblasts. Using my computational approach, I showed that cell edge velocity is regulated by the kinetic rate of GTPase activation rather than the concentration of the active molecules. My analysis also suggested that the timing of Rac1 and Cdc42 activity is cell-type dependent. I developed a model that reproduced such dependences and showed that feedback from Cdc42 and Rac1 was sufficient to control the activation delay when these GTPases have a common upstream regulatorily motif. I developed a series of image analysis pipelines for these studies that allowed precise tracking of GTPase activity and cell edge motion in simulations and experimental data.

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Probing Genomic, Metabolic, And Phenotypic Evolution In Microbes Using Comparative And Experimental Evolution Data

2023-01-13 , Castro Gordillo, Juan Camilo Camilo

Microbial model systems offer unique pportunities for evolutionary biologists, due to the ability to probe evolutionary dynamics using both comparative and experimental evolution techniques. This thesis leverages these opportunities to address questions on genomic, metabolic, and phenotypic evolution in bacteria. First, we exploit the growing availability of closed genomes for model bacteria (E. coli and P. aeruginosa) to build pan-genomes where we can track the physical linkage of all genes. Through a combination of evolutionary simulations and data-analysis, we ask how mutation, selection and gene interactions combine to shape genome structural organization (linkage) and variation (co-segregation) across strains. We show that co-egregation networks are modular, associate with physical linkage, and map to metabolic (for P. aeruginosa) and regulatory networks (for E. coli). The results imply that modular gene interactions are sufficient to guide the evolution of persistent gene clusters and are the primary force shaping genome structural evolution. Next, we focus on metabolic network evolution, and assess whether we can predict the metabolic wiring of P. aeruginosa, both before and after experimental evolution in defined environments. Standard flux-balance analysis (FBA) models have weak predictive value for ancestral strains both before and after experimental evolution adaptation to a novel defined environment. We reasoned that FBA models are limited by their focus on primary metabolic processes, and therefore fail to capture adaptation of secondary metabolism. By incorporating Tn-seq data on gene essentiality into our FBA model predictions we build metabolic predictions spanning primary and secondary metabolism. Our enhanced FBA models show (1) consistent predictive improvements following experimental evolution, and (2) highest predictive performance in the specific environment in which the Tn-seq data was generated. Finally, we turn to a phenotypic scale of evolutionary analysis, with a focus on biofilm production. Using a combination of theory and comparative data, we ask how biofilm investment strategies vary across strains of P. aeruginosa and are shaped by population dynamical processes and phylogenetic constraints. Our data illustrates substantial variation in biofilm allocation, with the proportion of biofilm cells varying from ~5 to 55%. Our data analysis allows us to reject a simple allocation tradeoff model and favors the ‘growth engine’ model introduced in earlier work (Lowery et al., 2017). Under the growth engine model, maximal biofilm production requires robust planktonic growth, generating a hump-shaped relationship between the total abundance of biofilm cells and planktonic cells. Finally, our heritability analysis indicates that biofilm phenotypic variation is substantially determined by phylogeny.

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