Mike Arpaia

Synthetic Biology

I collaborate with Chris Myers at the University of Colorado Boulder and Gonzalo Vidal at the University of Bristol on computational and experimental approaches to genetic design and experimentation. I work across a few different related domains including laboratory automation, microfluidic simulation, genomic foundation models, biological design standards, data infrastructure, and formal methods.

My research investigates methods to accelerate the development of vaccines, diagnostics, and therapeutics for emerging infectious diseases, including viral-vector vaccine development. I also serve as an editor of the Synthetic Biology Open Language (SBOL).

Research Affiliations

Genetic Logic Lab

Genetic Logic Lab

June 2026 – Present

University of Colorado Boulder

DRAGGON Lab

DRAGGON Lab

October 2026 – Present

University of Bristol

Publications

sbol-rs: A Rust SDK and CLI for the Synthetic Biology Open Language

In Review

Michael Arpaia and Chris J. Myers

2026

Talks

Building an SBOL Stack in Rust to Enable Scalable Data Infrastructure for Synthetic Biology

COMBINE Workshop · Keele University, UK

Michael Arpaia and Chris J. Myers

2026

Research Software

I maintain a growing ecosystem of open source software which aims to make genetic design and experimentation more reproducible and automated. These projects span computational genetics, lab automation, synthetic biology standards, machine learning, formal methods, and data infrastructure.

Lab Automation

Lab Compiler, liquid-handler integrations, and instrument interfaces for automating experiments.

Genetic Design & Vaccine Development

Tools for designing genetic circuits and planning recombinant viral vaccines.

Standards & Data Infrastructure

SBOL libraries and database tools for validating, exchanging, and querying biological designs.

Simulation & Machine Learning

Microfluidic simulation, machine learning for biodesign, and genomic foundation models.

Formal Methods & Verification

Lean formalizations of reaction networks and gene regulation, with Python tools for evaluating candidate networks.