Qaiser Zaidi

Identifying Cell Fate Determining Transcription Factors during Zebra fish development

Class of 2020


  • Education
    • The College of Wooster, BA in Biochemistry and Molecular Biology, 2020
  • Professional experience
    • Independent Study, The College of Wooster: Analyzed how transcription factors act as master regulators to drive cell state decision-making at cell-state differentiation branch points during zebra fish embryogenesis
      • Performed data mining and clustering to identify master regulators and important developmental switches
      • Built workflow to identify master regulators from single-cell RNA sequence data using Python
    • Research Intern, Imperial College: Optimizing CRISPR-Cas9 systems in mammalian cells to develop screens for breast cancer therapies
      • Attended weekly team meetings where results and methods were presented
      • Used GFP mutants to test gene knockout efficiency of CRISPR-Cas9 systems with several endonucleases
    • Health Coach, The College of Wooster & Wooster Community Hospital work with patients to encourage healthier lifestyle and gain experience in patient care
      • Made weekly patient visits to maintain med-box and record vitals
      • Recorded and presented SOAP notes at bi-weekly team meetings
    • Writing Center Staff, The College of Wooster: provided students with supplemental support on their assignments, projects and applications
      • Provided customer service to students and staff, including those with differing abilities and needs
      • Assisted in grading weekly assignments with clear implementable feedback
    • Intern, Sukoon Water, Karachi:
      • Created workflow chart for employees to improve quality assurance
      • Collected confidential customer data (frequency, age, product type, etc.)
      • Spearheaded installation of Ozonator to improve sanitation of bottles


IS Thesis Abstract

During development of any multicellular organism, a large variety of cell state eventually arise. All of these cells originate from a single cluster of pluripotent stem cells. As these stem cells differentiate they coordinate and change their gene expression profiles in order to establish different cell states to make up morphological features. These changes are moderated by Gene regulatory networks (GRN). Recent advances in technology have provided a new way of studying GRN’s using computational approaches. A recent study by Wagner et al. developed a computational model for zebra fish development. Using this model I identified cell fate determining TF that act as biological switches for cell fate decisions. These master regulators cannot only define cell state lineages but can also be used to reprogram cell fates. Using hierarchical clustering I identified both known and putative master regulators, hence this study provides a simple approach to identify master regulators from gene expression data. The simplistic nature of this approach and minimal assumptions it carries makes it widely applicable to all multicellular organisms. This approach can be used in the future to inform other computational models such as Boolean models.