PhD Proposal Exam
Tuesday, January 25th, 2018 at 10:10 am – Ramsay Wright Building, Room 432
Luís Eduardo Abatti (Mitchell lab)
“Investigating the SOX2 transcriptional network in estrogen-responsive and estrogen-resistant breast cancer cells”
Breast cancer is a multifactorial disease characterized by aberrant gene expression. The sex-determining region Y box2 (SOX2), a key transcription factor associated with pluripotency, is often overexpressed in breast cancer cells, where it has been linked to epithelial-mesenchymal transition (EMT) and hormone resistance. In mouse embryonic stem cells, Sox2 is regulated by a wide transcription factor network that interacts with its distal enhancer. However, the SOX2 transcriptional network in breast cancer cells remains unknown. Mammary epithelial cells rely on the estrogen receptor alpha (ESR1) and its cofactors – FOXA1 and GATA3 – to properly respond to estrogen stimulation, while breast cancer cells frequently display a dysfunctional estrogen response. My hypothesis is that SOX2 is normally downregulated by the repressive action of ESR1, FOXA1 and GATA3 at a distal enhancer. Once the estrogen pathway is disrupted in hormone-resistant cells, the repressive effect of estrogen over SOX2 expression is abolished, and SOX2 recruits the RNA Polymerase II transcriptional complex at multiple genomic targets. To better understand the role and regulation of SOX2 in this scenario, I propose three objectives: first, to identify the SOX2 transcriptional network in breast cancer cells; second, to investigate SOX2 cis- and trans-regulatory elements in MCF-7 cells; and third, to understand SOX2 upregulation in hormone-resistant MCF-7 cells. This SOX2 functional investigation will elucidate how breast cancer cells rely on this transcription factor to maintain their tumourigenesis and how its upregulation is linked to hormone resistance.
PhD Proposal Exam
Tuesday, June 27th, 2017 at 10:10 am – Earth Sciences Centre, Rm. 3087
Sonhita Chakraborty (Yoshioka lab)
“Investigation of the interplay between CYCLIC NUCLEOTIDE GATED ION CHANNEL 2 (CNGC2), Ca2+ and auxin signaling”
Cyclic nucleotide-gated channels (CNGCs) are non-selective cation channels that were first discovered in animals, where they were reported to be involved in visual and olfactory systems. While the biological role and channel properties of animal CNGCs have been well studied, not much is known about these channels in plants. The Arabidopsis thaliana CNGCs consists of twenty members, that have been implicated in development, ion homeostasis, thermotolerance, and pathogen defense. The “defense, no death” dnd1 and dnd2/hlm1, mutants of CNGC2 and CNGC4 respectively, exhibit autoimmune phenotypes such as dwarf morphology, constitutive expression of PR genes and elevated salicylic acid (SA) levels. To elucidate CNGC2-mediated signaling, repressor of defense no death 1 (rdd1), the first suppressor of dnd1 was identified. rdd1 is a loss-of-function mutation in YUC6, an auxin biosynthesis gene. Recent data shows that dnd1 has alterations in auxin signaling and auxin-induced Ca2+ flux. I hypothesize that CNGC2 is involved in development and auxin signaling in addition to its role in plant immunity. The aim of this project is to understand CNGC2-mediated signaling by elucidating the mechanism by which rdd1-1 supresses dnd1. YUC6 is involved in auxin biosynthesis and ROS homeostasis. Hence, I will explore if the suppression of dnd1 is through either of these functions. CNGC2 might also be important for auxin transport. Results from this project will provide new insights into the role of auxin in CNGC2 and Ca2+ signaling in the context of plant immunity.
PhD Proposal Exam
Tuesday May 16th, 10:10 am – Ramsay Wright Building, Rm. 432
Annik Yalnizyan Carson (Richards lab)
“Episodic Control: The Role of Memory Systems in Decision Making”
Reinforcement learning (RL) is an area of machine learning concerned with optimal behavioural control. RL provides a normative framework in which to understand how the brain can learn to make decisions for maximizing subjective reward in the absence of an explicit teaching signal. Currently, even agents using state-of-the-art control systems in RL tasks are data inefficient and challenged by nonstationary environmental conditions, including changes in statistics of reward probability and transitions between states, which biological agents handle with relative ease. It has been proposed that storing information about experienced episodes in a memory cache — modeled after the activity of the hippocampus in animals — can help bootstrap learning in RL systems to improve the speed of learning and ability to cope with nonstationary environments. My research proposes three different representations for episodic memories stored in such a system and aims to resolve which provides the greatest benefit to RL systems when used in conjunction with a standard controller. Furthermore I aim to resolve how these representations can account for features of animal behaviour, and which of these representations — if any — are likely to explain how episodic memory is represented in the hippocampus.
Ramsay Wright is a wheelchair accessible building.