Browsing by Author "Sadria, Mehrshad"

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Aging affects circadian clock and metabolism and modulates timing of medication
(Elsevier, 2021-04) Sadria, Mehrshad; Layton, Anita T.
Aging is associated with impairments in the circadian rhythms, and with energy deregulation that affects multiple metabolic pathways. The goal of this study is to unravel the complex interactions among aging, metabolism, and the circadian clock. We seek to identify key factors that inform the liver circadian clock of cellular energy status and to reveal the mechanisms by which variations in food intake may disrupt the clock. To address these questions, we develop a comprehensive mathematical model that represents the circadian pathway in the mouse liver, together with the insulin/IGF-1 pathway, mTORC1, AMPK, NAD+, and the NAD+ -consuming factor SIRT1. The model is age-specific and can simulate the liver of a young mouse or an aged mouse. Simulation results suggest that the reduced NAD+ and SIRT1 bioavailability may explain the shortened circadian period in aged rodents. Importantly, the model identifies the dosing schedules for maximizing the efficacy of anti-aging medications.
Deep Learning Models of Cellular Decision-Making Using Single-Cell Genomic Data
(University of Waterloo, 2025-01-27) Sadria, Mehrshad; Layton, Anita
Cellular decision-making, essential to regenerative medicine, disease research, and developmental biology, relies on complex molecular mechanisms that guide cells in responding to stimuli and committing to specific fates. This thesis introduces several deep learning methods to analyze single-cell RNA sequencing data, uncover regulatory programs driving these processes, and predict the outcomes of gene perturbations. By applying representation learning and generative models, meaningful structures within high-dimensional data are identified, enabling tasks such as mapping cellular trajectories, reconstructing regulatory networks, and generating realistic synthetic data. Furthermore, integrating deep learning with dynamical systems theory enables the prediction of cellular decision timing and the identification of key regulatory genes involved in these processes. These methods enhance our understanding of gene activity dynamics, improve predictions of cellular behavior, and offer new avenues for progress in regenerative medicine, developmental biology, and disease research.