Unraveling cellular dynamics through stochastic hybrid models for biomolecular and neuronal circuits
| Author(s) | Vahdat, Zahra | |
| Date Accessioned | 2024-10-29T16:41:49Z | |
| Date Available | 2024-10-29T16:41:49Z | |
| Publication Date | 2024 | |
| SWORD Update | 2024-10-13T19:02:42Z | |
| Abstract | Living cells exhibit significant stochastic fluctuations due to low molecular counts and inherently random biochemical processes. Leveraging stochastic models, we aim to unravel the underlying mechanisms governing information transfer and precision in the fundamental cellular processes. We delve into the intricate interplay between molecular variability and cellular processes, focusing on gene expression, cell size regulation, and neuronal synaptic transmission. ☐ Through systematic analyses, we explore various sources of noise, including stochastic bursting in protein synthesis, random events in protein turnover, and noise in cell-cycle progression. These analyses provide insights into the statistical properties of protein levels and their dependence on distinct noise mechanisms. ☐ Furthermore, we explore the role of feedback mechanisms in gene expression regulation, investigating how negative feedback circuits influence noise and sensitivity in protein levels. By comparing different feedback mechanisms under varying conditions, we uncover trade-offs between noise reduction and input-output sensitivity, highlighting the complex interplay between molecular regulation and cellular function. ☐ Moving beyond gene expression, we investigate the homeostatic mechanisms underlying cell size regulation. By modeling continuous growth and division processes, we uncover the role of nonlinear growth dynamics in maintaining size homeostasis. Our analyses reveal the intricate balance between cell growth, division, and feedback mechanisms, providing valuable insights into cellular size control mechanisms. ☐ In the realm of neuronal synaptic transmission, we explore the impact of diverse noise mechanisms on neurotransmitter release and postsynaptic neuron activity. Through mechanistic stochastic models, we quantify the dynamics of neurotransmitter release and synaptic efficacy, uncovering the complex relationship between noise, synaptic strength, and neuronal activity. ☐ Overall, this dissertation offers a comprehensive exploration of stochastic phenomena in cellular biology, providing novel insights into the mechanisms governing precision and variability in gene expression, cell size regulation, and neuronal synaptic transmission. By combining analytical approaches with stochastic modeling techniques, we elucidate fundamental questions surrounding molecular variability and its implications for cellular function and behavior. | |
| Advisor | Singh, Abhyudai | |
| Degree | Ph.D. | |
| Department | University of Delaware, Department of Electrical and Computer Engineering | |
| DOI | https://doi.org/10.58088/jed8-rq91 | |
| Unique Identifier | 1500133012 | |
| URL | https://udspace.udel.edu/handle/19716/35468 | |
| Language | en | |
| Publisher | University of Delaware | |
| URI | https://www.proquest.com/pqdtlocal1006271/dissertations-theses/unraveling-cellular-dynamics-through-stochastic/docview/3116041348/sem-2?accountid=10457 | |
| Keywords | Computational biology | |
| Keywords | Gene expression | |
| Keywords | Mathematical modeling | |
| Keywords | Stochastic hybrid modeling | |
| Keywords | Synaptic transmission | |
| Title | Unraveling cellular dynamics through stochastic hybrid models for biomolecular and neuronal circuits | |
| Type | Thesis |