What are the key takeaways from this Axial Podcast episode?

Key insights include: **Evolutionary screening scale:** Triplebar's microfluidic platform screens hundreds of millions of genetic variants daily by encapsulating cells in picoliter-to-nanoliter droplets controlled by custom chips. This brute-force coverage — analogous to buying every lottery ticket — eliminates the need to design solutions from first principles, compressing multi-year strain development programs into months with small teams.; **Platform focus discipline:** Triplebar deliberately restricts its organism library to CHO cells for antibody production and Pichia pastoris for food proteins, rather than pursuing organism-agnostic flexibility. This constraint means onboarding a second project in the same organism takes roughly 20% of the time the first project required — Agresti's concrete threshold for classifying something as a true platform.; **Horizontal business model prerequisite:** Vertically integrated synthetic biology companies (discovery through consumer product) exist because slow, expensive discovery forces them down the supply chain to justify economics. Faster, cheaper discovery unlocks a horizontal supplier model — analogous to Michelin making tires without building cars — where Triplebar licenses or delivers optimized strains to partners who handle manufacturing and commercialization.

What did Jeremy Agresti discuss on Axial Podcast?

Jeremy Agresti, founder and CTO of Triplebar Bio, explains how microfluidic droplet screening at picoliter scale enables testing hundreds of millions of biological variants per day, unlocking a horizontal platform business model for biologics discovery across antibody therapeutics, cultivated meat cell lines, and precision fermentation proteins. Key topics include: **Evolutionary screening scale:** Triplebar's microfluidic platform screens hundreds of millions of genetic variants daily by encapsulating cells in picoliter-to-nanoliter droplets controlled by custom chips. This brute-force coverage — analogous to buying every lottery ticket — eliminates the need to design solutions from first principles, compressing multi-year strain development programs into months with small teams.; **Platform focus discipline:** Triplebar deliberately restricts its organism library to CHO cells for antibody production and Pichia pastoris for food proteins, rather than pursuing organism-agnostic flexibility. This constraint means onboarding a second project in the same organism takes roughly 20% of the time the first project required — Agresti's concrete threshold for classifying something as a true platform..

How long is this episode of Axial Podcast?

This episode is 51 minutes long. SignalCast provides an AI-generated summary so you can get the key insights in about 3 minutes.

Axial Podcast

Evolutionary Intelligence and Biologics Discovery with Jeremy Agresti

March 23, 2025

51 min episode · 2 min read

Jeremy Agresti

Episode

51 min

Read time

2 min

Topics

Productivity, Relationships, Startups

AI-Generated Summary

Published Mar 14, 2026

Key Takeaways

✓Evolutionary screening scale: Triplebar's microfluidic platform screens hundreds of millions of genetic variants daily by encapsulating cells in picoliter-to-nanoliter droplets controlled by custom chips. This brute-force coverage — analogous to buying every lottery ticket — eliminates the need to design solutions from first principles, compressing multi-year strain development programs into months with small teams.
✓Platform focus discipline: Triplebar deliberately restricts its organism library to CHO cells for antibody production and Pichia pastoris for food proteins, rather than pursuing organism-agnostic flexibility. This constraint means onboarding a second project in the same organism takes roughly 20% of the time the first project required — Agresti's concrete threshold for classifying something as a true platform.
✓Horizontal business model prerequisite: Vertically integrated synthetic biology companies (discovery through consumer product) exist because slow, expensive discovery forces them down the supply chain to justify economics. Faster, cheaper discovery unlocks a horizontal supplier model — analogous to Michelin making tires without building cars — where Triplebar licenses or delivers optimized strains to partners who handle manufacturing and commercialization.
✓Customer discovery via deliberate provocation: When exploring antibody markets, Triplebar assumed binding discovery was a solved problem and stated this directly to potential customers. The rebuttal — that finding a binder is easy but predicting therapeutic viability is not — revealed the actual unmet need. Stating a wrong assumption generates more candid correction than open-ended questions, particularly with technically sophisticated scientific customers.
✓AI-biology data gap opportunity: Current AI biology models succeed where large, relevant datasets exist — protein structure prediction being the primary example. Functional biological data remains scarce, limiting AI utility for engineering applications. Triplebar positions its high-throughput functional screening as a generator of the large, labeled datasets needed to make machine learning models genuinely predictive for biological design problems.

What It Covers

Jeremy Agresti, founder and CTO of Triplebar Bio, explains how microfluidic droplet screening at picoliter scale enables testing hundreds of millions of biological variants per day, unlocking a horizontal platform business model for biologics discovery across antibody therapeutics, cultivated meat cell lines, and precision fermentation proteins.

Key Questions Answered

•Evolutionary screening scale: Triplebar's microfluidic platform screens hundreds of millions of genetic variants daily by encapsulating cells in picoliter-to-nanoliter droplets controlled by custom chips. This brute-force coverage — analogous to buying every lottery ticket — eliminates the need to design solutions from first principles, compressing multi-year strain development programs into months with small teams.
•Platform focus discipline: Triplebar deliberately restricts its organism library to CHO cells for antibody production and Pichia pastoris for food proteins, rather than pursuing organism-agnostic flexibility. This constraint means onboarding a second project in the same organism takes roughly 20% of the time the first project required — Agresti's concrete threshold for classifying something as a true platform.
•Horizontal business model prerequisite: Vertically integrated synthetic biology companies (discovery through consumer product) exist because slow, expensive discovery forces them down the supply chain to justify economics. Faster, cheaper discovery unlocks a horizontal supplier model — analogous to Michelin making tires without building cars — where Triplebar licenses or delivers optimized strains to partners who handle manufacturing and commercialization.
•Customer discovery via deliberate provocation: When exploring antibody markets, Triplebar assumed binding discovery was a solved problem and stated this directly to potential customers. The rebuttal — that finding a binder is easy but predicting therapeutic viability is not — revealed the actual unmet need. Stating a wrong assumption generates more candid correction than open-ended questions, particularly with technically sophisticated scientific customers.
•AI-biology data gap opportunity: Current AI biology models succeed where large, relevant datasets exist — protein structure prediction being the primary example. Functional biological data remains scarce, limiting AI utility for engineering applications. Triplebar positions its high-throughput functional screening as a generator of the large, labeled datasets needed to make machine learning models genuinely predictive for biological design problems.

Notable Moment

Triplebar initially pursued growth factor production for cultivated meat companies, believing market reports identified it as the sector's primary bottleneck. Every company beyond Series A had already solved that problem internally. Those same conversations redirected Triplebar toward cell line suspension adaptation — the actual persistent need — demonstrating how early customer conversations can invalidate entire product hypotheses before any lab work begins.

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Episode Transcript

Welcome to the Axial podcast. Axial is an early stage investment firm based in San Francisco. We partner with great founders and inventors investing in early stage life science companies often when they are no more than an idea. Axial is fanatical about helping the right venture who's compelled to build their own and journey business. Jeremy, thank you for taking time to do this podcast. I'm really excited to talk about your story in Triple Barr. David, start off. You can just introduce yourself. Yeah. Hi. Thanks. Thanks for for inviting me to come. It's it's great to talk to you. So, Jeremy Agresti. I'm the founder and the CTO of Triplebar, and, we're a company that, we're a company that uses, miniaturization and microfluidics to greatly accelerate the the discovery of new biological systems. And, really, Triplebar is based around, my career. Like, the I've sort of built toward this my whole career. So, starting as an undergrad at UC Davis, majoring in genetics, studying a lot of natural evolution and population genetics with the with, in my time there. And I think where the that that sort of laid, like, a a you know, I've always been kind of inspired by the idea of using biology to to kind of, solve problems for for humanity. And and, and the thing that really kind of sparked my interest in in continuing, you know, in higher levels of science was, work that was being done at the University of Cambridge, in the lab of a of a scientist named Greg Winter, a technology called phase display. It's used for, you know, kind of the the standard for discovery of of antibodies. And the thing that really sparked my interest and it relates to my kind of training in evolution was that what phase display showed is that if you can sort of look at enough biological variation, you can you can engineer, in this case, antibodies without the need to understand from first principles how to design. And so I don't know how to design an antibody that will bind to a particular target to be a a medicine, but but if I look at a billion antibody candidates, I'll find some that do. Right? And that's just that was kind of, like, this fundamental, spark that I you know, that that brought that threw me to to work in that lab in in, in Cambridge for my PhD. And when I got that time, though, screening was pretty limited. And could you talk more around maybe triple bar, what you guys do? Because, like, you know Yeah. When phase display's limit was invented, I maybe you how many I don't know how many antibodies could scream. I mean, no one's doing that much. Yeah. And that's the that was the thing about phase display is that it's it you know, at that time, maybe there was there were liquid handling handling robotics. You could screen thousands of tests …

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