Cambridge Healthtech Institute’s 2nd Annual

SYMPOSIUM: Predictive Protein Production

January 19, 2026

As the demand for faster, smarter, and more reliable protein production intensifies, the tools enabling predictive science are rapidly evolving. Cambridge Healthtech Institute’s 2nd Annual Predictive Protein Production Symposium showcases how researchers leverage machine learning and advanced data modeling to guide experimental design and manufacturability, and drive innovation. Discover how experts apply predictive science to develop scalable, reproducible protein production workflows—featuring robust biosensors and high-throughput assays that accelerate screening. Whether you’re focused on developing novel therapeutic proteins or optimizing bioproduction workflows, this symposium provides actionable insights for building more scalable, data-informed, and efficient protein production pipelines.

Coverage will include, but is not limited to:

Emerging Tools & Technologies

Synthetic Biology for Expression Systems
Cell-Free Lysate Systems: Design, Optimization, and Application
Mammalian Membrane Protein and Antigen Complexes

High-Throughput & Scalable Approaches

Robust Biosensors for Expression
Screening and Assay Design

Data-Driven Innovation

Predictive Modeling
Digital Twins

Proteomics & Multi-Omics Integration into Predictive Models
Assay and Screening Technologies

Miniaturized Screening Assays

Strategies for Optimization

Enhancing Biomanufacturability Early in Discovery
Protein Production of Novel Membrane Targets

The deadline for priority consideration is June 27, 2025.

All proposals are subject to review by session chairpersons and/or the Scientific Advisory Committee to ensure the overall quality of the conference program. Additionally, as per Cambridge Healthtech Institute’s policy, a select number of vendors and consultants who provide products and services will be offered opportunities for podium presentation slots based on a variety of Corporate Sponsorships.

Opportunities for Participation: