"Bridging the gap between High-Throughput Mass Spectrometry and Computational Automation."

I am a hybrid wet/dry lab researcher currently engineering the Python infrastructure for the BYU Mass Spectrometry Core. My focus is on Industrializing Biology: transforming manual, error-prone assays into robust, reproducible data engines.

• 10x Throughput: Architected Python pipelines (Pandas/NumPy) for the Orbitrap Astral, scaling daily capacity from 15 to 100+ proteomes.
• 85% QC Reduction: Built programmatic visualization modules to automate instrument performance monitoring, removing manual validation bottlenecks.
• Precision at Scale: Validated complex 3-species libraries (HeLa, E. coli, Yeast) identifying 11,000+ protein groups with <7% CV.

• BYU-MS-Core-Automative-Proteomics-Tools (Live Production Code)
  • Overview: The active OS powering the BYU Mass Spectrometry Core.
  • Tech: Flask, NumPy, Pandas, Matplotlib.
  • Key Metrics:
    • 10x Throughput: Enabled scaling from 15 to 100+ proteomes/day.
    • 85% Efficiency Gain: Reduced manual QC time by automating extraction workflows.
    • Data Integrity: Ensured <20% CV validation across multi-lab studies.

I am pursuing a hybrid wet/dry lab trajectory designed to bridge the gap between bench science and data engineering.

• Phase 1 (Current): Build high-throughput automation expertise in Biotech Industry (Recursion/TechBio).
• Phase 2 (Concurrent): Complete MS in Translational Bioinformatics to formalize computational skills without leaving the workforce.
• Phase 3 (Future): Pursue PhD in Oncolytic Virotherapy with a focus on industrial-grade translational research.

Goal: To combine industrial operational excellence with rigorous academic research training—ensuring therapies survive the transition from bench to bedside.

• Research projects in virology, immunology, or cancer biology
• Open-source bioinformatics tools for genomic data analysis
• Cancer research initiatives that bridge computational and experimental approaches
• Educational resources for students pursuing non-traditional paths to PhD programs

• Networking with scientists working in oncolytic virotherapy or cancer immunology
• Advice on PhD applications from people who took non-traditional paths (industry experience before PhD)
• Mentorship from translational scientists who bridge clinical and research worlds

• Career planning for non-traditional paths to PhD programs
• Balancing work and graduate school (I'll be doing MS part-time while working full-time!)
• Biotech industry experience as a foundation for translational research
• Oncolytic virotherapy and cancer immunology (my passion!)
• Flow cytometry and immunology lab techniques
• Building a sustainable timeline for long-term academic/career goals

• I'm building a clinical-computational-research triple threat skill set that is in high demand across disciplines
• I'm passionate about mentoring students from non-traditional backgrounds who want to pursue research careers
• I enjoy cooking (with or without recipes), gaming, reading and hiking in my free time!

• LinkedIn: linkedin.com/in/jonathanto99
• Focus: Industrializing Drug Discovery, High-Throughput Screening, Computational Biology

"The best time to plant a tree was 20 years ago. The second-best time is now." - Chinese Proverb

I'm planting my tree now at age 26, and I'm excited to see it grow into a career advancing oncolytic virotherapy research!