B.S. Biology · Cum Laude · Graduated 2026

Jonathan M.
Pawl

Recent biology graduate who worked on a RAD52–GFP yeast DNA-damage biosensor project at the bench and designed AI tools that turn lab assay data into insight, working at the intersection of the bench and the algorithm.

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GPA · Cum Laude
2026
B.S. Biology, Ave Maria

01. About

Jonathan M. Pawl

I'm a recent biology graduate with a builder's instinct. In the lab I engineered genetic constructs and ran assays; at the keyboard I built the software that makes data legible.

My undergraduate research project was titled "Construction and Expression of a RAD52-GFP Fusion Gene in Yeast as a Biosensor of DNA Damage." Using Golden Gate Assembly, I constructed the RAD52–GFP repair template and designed the CRISPR-Cas9 editing plasmid intended to deliver it into yeast (the project's next step), targeting a repair pathway central to BRCA-deficient cancers. Alongside the bench work, I built an AI tool that predicts whether a compound is genotoxic from its molecular structure. I was an NAIA student-athlete; disciplined, curious, and an aggressive problem solver.

Molecular Biology
Golden Gate Assembly PCR / qPCR Primer design Plasmid cloning Gel electrophoresis Bacterial transformation
Microbiology & Chemistry
Aseptic technique Bacterial culturing Gram staining Biochemical ID Titration · TLC
Instrumentation & Software
Cytek Muse Flow Cytometer qPCR thermocycler Spectrophotometry Benchling VectorBee

02. Research Experience

Undergraduate Research

Jan – May 2026
Molecular Biology Lab, Ave Maria University · Mentor: Dr. Stephen R. Cronin

Research project titled "Construction and Expression of a RAD52-GFP Fusion Gene in Yeast as a Biosensor of DNA Damage," mentored by Dr. Stephen R. Cronin. I designed and constructed a RAD52–GFP repair template via Golden Gate Assembly, verified and ready for the next step: introducing it into yeast via a CRISPR-Cas9 editing plasmid I designed alongside it, targeting a repair pathway relevant to BRCA1/BRCA2-deficient cancers.

Designed & constructed a RAD52–GFP repair template via Golden Gate Assembly, the donor DNA intended for CRISPR-based integration into yeast.
Designed sequence-specific primers; amplified RAD52 & GFP by PCR; verified by gel, plasmid prep & sequence analysis.
Performed DNA extraction, bacterial transformation & red/white colony screening; quantified recombinant clones across replicates.
Once verified, the repair template was intended to be used with a CRISPR-Cas9 plasmid to introduce the GFP tag into RAD52, the next step toward measuring the DNA-damage response by flow cytometry.
Maintained sterile technique & detailed, reproducible documentation of every protocol, reagent & result.
pGA-red-maxi pCEC-red Addgene S. cerevisiae
Goal 01

Construct a C-terminal RAD52–GFP fusion for an undergraduate genetics lab.

Goal 02

Develop a yeast gene-editing protocol usable in a teaching lab.

Goal 03

Build a eukaryotic biosensor to detect DNA-damaging agents in environmental samples.

Construct design

Four fragments → one 6,847 bp construct

Six BsaI-flanked primers were designed and three gene fragments amplified by PCR (Q5 polymerase), then assembled with the backbone in a single BsaI Golden Gate reaction, achieving 100% predicted overhang-ligation fidelity.

RAD52 homology
296 bp
GFP
718 bp
Downstream RAD52
291 bp
pGA-red-maxi backbone
5,618 bp
Total construct
6,847 bp · circular

Amplicons total ~6,923 bp; the assembled construct is 6,847 bp because the BsaI sites and spacer bases flanking each fragment are excised during the digestion–ligation reaction. Backbone pGA-red-maxi (Addgene #196337) · donor pCEC-red (#196040) · BsaI-HFv2 · T4 ligase · NEBridge Golden Gate · native RAD52 intended for replacement via CRISPR-Cas9 (yeast-editing step not completed).

Lab results · real data

Red/white screening recovered the recombinants

White colonies (cassette inserted, red marker lost) were the candidate correct assemblies. Several hundred were screened across replicate platings; negative controls (assembly mix alone) gave zero colonies, a clean background.

26
candidate recombinants
14
advanced to sequencing
0
background colonies (control)
100%
predicted ligation fidelity
% white (recombinant) per plate
850 µl platings 1–4 · 100 µl platings 5–8
6.5
1
4.3
2
14.3
3
50.0
4
6.7
5
1.9
6
7.7
7
5.6
8

Why it matters in industry. A yeast GFP DNA-damage reporter is the same assay class commercialized as the GreenScreen genotoxicity test used by pharma and chemical companies. This undergraduate project builds the donor construct and editing strategy for that concept from scratch.

Design

Primer & guide-RNA design and construct planning in Benchling and VectorBee.

Build

Golden Gate assembly, CRISPR-Cas9 guide/plasmid design, plasmid cloning, bacterial transformation.

Validate

Colony screening, gel electrophoresis, plasmid prep, Sanger sequence verification.

Quantify (planned)

GFP reporter readout by flow cytometry (Cytek Muse) after mutagen exposure, the step following yeast integration.

03. Projects

Automating biology, one pipeline at a time.

Fisheries Ecosystem Monitor dashboard
Software · Data pipelines
Live · Prototype

Fisheries Ecosystem Monitor

Platform that automates fisheries stock assessment by harvesting species records, survey data, and catch reports from NOAA and public databases, assembling them into a shared framework, running simplified models (FIMS, WHAM, Demo), and publishing results with quantified uncertainty. Three dashboards serve three audiences: public fish-status browser with biomass and fishing-mortality trends, stakeholder quality-check for citizen reports, and reproducible run history with diffs between pipeline runs.

Cloud Run Data pipelines Stock assessment Open science
Launch the platform →
AI Genotoxicity Predictor screenshot
Software · Machine learning
Live · local app

AI Genotoxicity Predictor

A machine-learning companion to the wet-lab RAD52–GFP work that predicts whether a chemical compound is likely to damage DNA, the same genotoxic signal the RAD52–GFP assay is designed to detect in living cells. Enter a compound name or SMILES string and it returns a probability of mutagenicity (Ames), the molecule structure, an applicability-domain check, and the model's cross-validated performance. Under the hood: RDKit ECFP4 fingerprints → a 400-tree random forest, served from a Flask dashboard.

Machine learning RDKit · scikit-learn Cheminformatics DNA damage
Launch the predictor →
Live interactive demo: enter a compound name or SMILES and get an Ames-mutagenicity prediction.
Software · QC automation
Live · runnable

Lab Assay Auto-Report Tool

Drop raw assay files (CSV, TSV, XLSX) into a folder and get a formatted QC report: no spreadsheet wrangling. It auto-detects the data type, from red/white recombinant colony screening to plate-reader RFU and qPCR Cq, and computes totals, per-plate %, means, hit-calls (≥3 SD) and outlier flags. Parsing, statistics and flags are deterministic code: numbers are never invented; an optional grounded Claude step writes the one-line interpretation using only the figures already computed. A watch mode regenerates reports as files land, and an "Ask your reports" box answers natural-language queries. Everything runs locally, with export to HTML, CSV, JSON or PDF.

Python QC automation Grounded AI Local-first
Launch the dashboard →
Lab Assay Auto-Report Tool screenshot

04. Education & Honors

Ave Maria University

2022 – 2026
B.S. Biology · Minor in Business Administration
Cum Laude · GPA 3.73 · Ave Maria, FL

Coursework: Genetics · Molecular Biology · Microbiology · Botany · Zoology · General & Organic Chemistry · Statistics

Honors
St. Albert the Great Biology Research Award (2026)
Dean's List (2022–2026) · academic & athletic scholarships
NAIA Men's Basketball student-athlete
Presentations
Oral Presentation, Florida Academy of Sciences Conference (2026)
Poster, Ave Maria University Research Symposium (2026)

Contact Me For More Information

Open to research, R&D, and lab positions.

Name and organization are required, plus an email or phone so I can reply.
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