"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.
| Domain | Tools |
|---|---|
| Pipeline Engineering | Python (Pandas, NumPy, Matplotlib), Git & GitHub |
| Proteomics | FragPipe, DIA-NN, Proteome Discoverer, Orbitrap Astral |
| Data Science | Jupyter, Scanpy, Pyteomics, SciKit-Learn |
| Wet Lab | Mass Spectrometry, Mammalian Cell Culture, High-Throughput Screening (HTS), Flow Cytometry |
- 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!