Theses
Date of Award
2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Biological Sciences
First Advisor
Stephen Redenti
Abstract
Post-mitotic neurons can reenter the cell cycle and begin cell division, but this process typically ends in apoptosis. In this work I will use systems pharmacology and machine learning to target cell cycle and survival genes in post-mitotic neurons to drive successful cell division. In the first part of this work, I have selected through published data genes involved in neuron cell cycle re-entry and apoptosis. I have then used the systems pharmacology database drug gene budger to identify drugs to direct the expression of these genes toward cell cycle and survival. I provide eight tables of key identified genes and drugs resulting from drug gene budger with predicted changes associated with cell cycle and survival. In the second part of this work, I have identified full gene expression sets of post-mitotic neuron rod cells and (mitotic) late retinal progenitor cells and have worked with a collaborator to use machine learning to perform combinatorial perturbations of gene sets to drive re-entry into cell cycle and survival. I have identified network genes predicted to facilitate post-mitotic neuron rod cells re-entry to cell cycle and transition into (mitotic) late retinal progenitor state. This work holds promise for application in neural regeneration.
Recommended Citation
Hector, Allison, "Systems Biology and Machine Learning Approaches to Drug Selection and Candidate Target Gene Transition of Cells to Desired State" (2026). CUNY Academic Works.
https://academicworks.cuny.edu/le_etds/52
