DC Science Series #6 - Irem Ergenlioğlu (DC5)
Welcome to the SYNSENSO DC Science Series. In this blog post, Irem shares her research interests with us. Enjoy!
From pipettes to pipelines: Automation, AI, and the new face of synthetic biology
Synthetic biology has long promised engineering-like repeatability. What’s changed in the past few years is that the design–build–test–learn (DBTL) cycle is increasingly executed by robots and shaped by machine learning—turning biology into a software-defined practice. Biofoundries now coordinate automated DNA assembly, screening, and analytics; the Global Biofoundries Alliance connects dozens of these facilities worldwide to share methods and standards.
Automation: from benchtop robots to “cloud labs”
Automation is landing at both ends of the spectrum. On the bench, accessible liquid-handling robots and platforms from companies such as Opentron, Hamilton, Tecan, Beckman Coulter helps automate DNA assembly, PCR and plate work with a Python API—an on‑ramp to reproducible, scripted biology.
At the other end, cloud labs such as Emerald Cloud Lab (ECL) and Strateos offer remote-controlled access to instruments via software so you can design, execute and analyze experiments remotely—useful for scale, for teaching, and for distributed teams.
The software layer that orchestrates DBTL is also maturing. Ginkgo Bioworks continues to pair high‑throughput automation with AI/ML workflows; tools like TeselaGen and Synthace help labs script experiments, capture structured data and bridge instruments with design environments.
Artificial intelligence meets high-throughput biology
Breakthroughs like AlphaFold reframed what’s knowable about proteins, and modern sequence-to-function models now turn that knowledge into design proposals. In the lab, those proposals drive closed-loop campaigns: AI selects the next variants, automation executes, measurements feed the model, iterate. Instead of massive screens, teams run compact, information-rich rounds that converge quickly to better enzymes, antibodies, and circuits.
One emerging player is Future House, a nonprofit AI‑for‑science lab that aims to build an “AI scientist”—agentic systems that generate hypotheses, plan and run experiments, and write up findings.
Conferences & communities
Here are leading conferences and communities for lab automation, AI, and synthetic biology—great places to learn, share work, and join the communities:
- SynBioBeta: Global Synthetic Biology Conference for industry and investment trends
- SEED (Synthetic Biology Engineering Evolution Design): for technical deep dives
- GBA (The Global Biofoundries Alliance): meetings specifically for the automation community
- Lab of the Future Congress Europe: focus on connected/automated labs, interoperability, and AI-enabled R&D operating models
- The Society for Laboratory Automation and Screening (SLAS) Conference and Exhibitions: the leading gathering for lab automation and AI-driven R&D, covering liquid handling, workflow scheduling, data engineering, and fully robotic assays
- SiLA Consortium (SiLA 2): open standards for instrument connectivity; runs talks and meetups on lab automation & interoperability
For early-career researchers, pairing programmable automation with model-driven design is reshaping how we plan projects: fewer one-off protocols, more reusable pipelines, fewer labor-intensive screens, more focused and data-guided iterations. Whether you join a biofoundry, launch a startup, or run remote experiments from a laptop, the tools are becoming increasingly accessible. We’re not fully there yet, but synthetic biology is moving from “pipetting” to “pipelines”—much like manufacturing shifted from craft work to assembly lines.
Text by Irem Ergenlioğlu, DC5. To find out more about Gökçe, visit her profile.