AI-POWERED INSIGHTS
Using a GPT-5-driven autonomous lab to optimize the cost and titer of cell-free protein synthesis
We used an autonomous lab, comprising a large language model (LLM) and a fully automated cloud laboratory, to optimize the cost efficiency of cell-free protein synthesis (CFPS). By conducting iterative optimization, the LLM-driven autonomous lab was able to achieve a 40% reduction in the specific cost ($/g protein) of CFPS relative to the state of the art (SOTA). This cost reduction was accompanied by a 27% increase in protein production titer (g/L). Iterative experimental design, experiment execution, data capture and analysis, data interpretation, and new hypothesis generation were all handled by the LLM-driven autonomous lab. The interface between OpenAI's GPT-5 LLM and Ginkgo Bioworks' cloud laboratory incorporated built-in validation checks via a Pydantic schema to ensure that AI-designed experiments were properly specified. Experimental designs were translated into programmatic specification of multi-instrument biological workflows by Ginkgo's Catalyst software and executed on Ginkgo's Reconfigurable Automation Cart (RAC) laboratory automation platform, with human intervention largely limited to reagent and consumables preparation, loading and unloading. By integrating LLMs with programmatic control of a cloud lab, we demonstrate that an LLM-driven autonomous lab can successfully perform a real-world scientific task, highlighting the potential of AI-driven autonomous labs for scientific advancement.
Executive Impact
This research demonstrates a groundbreaking advancement in scientific discovery, leveraging an AI-driven autonomous lab to significantly improve cell-free protein synthesis (CFPS). The project not Pre-ownedly achieved substantial cost reductions and increased protein yields but also showcased the power of large language models (LLMs) in complex experimental design and scientific reasoning.
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Cost Reduction in CFPS
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Protein Production Titer Increase
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Unique Compositions Tested
Deep Analysis & Enterprise Applications
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The GPT-5 Driven Autonomous Lab
The autonomous lab seamlessly integrates OpenAI's GPT-5 with Ginkgo Bioworks' Reconfigurable Automation Cart (RAC)-based cloud laboratory. This fusion enables AI-driven experimental design, automated execution, and iterative learning, minimizing human intervention to reagent handling.
Enterprise Process Flow
GPT-5 Proposes Experimental Designs
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Pydantic Validates Designs
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Catalyst Generates Protocols
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RACs Execute Experiments
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Data Returned to GPT-5
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Analysis & New Hypothesis Generation
This iterative loop, supported by Pydantic's validation schema, ensures scientific validity and physical executability of AI-designed experiments, allowing for rapid and scalable discovery.
Groundbreaking CFPS Optimization
The autonomous lab successfully optimized cell-free protein synthesis (CFPS) for superfolder green fluorescent protein (sfGFP), achieving results significantly surpassing the current state of the art (SOTA).
$422/g
Achieved Specific Cost (40% Reduction vs. SOTA)
| Metric | SOTA (Olsen et al. 2025) | Autonomous Lab (This Work) |
|---|---|---|
| Specific Cost ($/g) | $698/g | $422/g |
| Protein Titer (g/L) (2-mL tubes) | 2.39 ± 0.28 | 3.04 ± 0.30 (Composition 1777863_77) |
| Protein Titer (g/L) (384-well plates) | ~0.25 | 0.72 ± 0.38 (Composition 1793122_35) |
These results demonstrate a simultaneous 40% reduction in specific cost and a 27% increase in protein production titer, proving that both efficiency and yield can be drastically improved through AI-driven experimentation.
GPT-5's Advanced Scientific Reasoning and Discovery
GPT-5 exhibited sophisticated scientific reasoning, acting as a "principal investigator" by analyzing data, interpreting results, and generating novel hypotheses, documented in human-readable lab notebook entries.
GPT-5's Reagent Discovery & Hypotheses
Before being explicitly provided the SOTA preprint, GPT-5 demonstrated remarkable foresight by identifying key reagents for NTP regeneration and synthesis. It prioritized compounds like polyphosphate, acetyl phosphate, nucleotide monophosphates (NMPs), nucleosides, bases, and ribose. These suggestions proved critical for the observed reagent cost advances, aligning with findings later revealed in the Olsen et al. (2025) paper. This highlights the LLM's capacity for fundamental biochemical reasoning and proactive hypothesis generation. GPT-5 also documented its analysis of data, observations of experimental trends, surprising or confusing results, and new hypotheses it formulated for testing, serving as an invaluable automated lab notebook.
The integration of the Pydantic schema was crucial in ensuring the scientific validity of GPT-5's experimental designs, effectively mitigating "hallucinations" and enabling robust, executable protocols for the cloud lab.
The model's ability to combine insights from SOTA, prior experimental data, and online publications, coupled with access to computational tools, further enhanced its performance and discovery capabilities.
Key Reaction Component Insights from AI-Driven Optimization
The iterative experimentation by the autonomous lab yielded several critical insights into CFPS reaction components and their impact on specific cost and titer:
- HEPES Buffer: Adding cheap HEPES buffer significantly improved specific cost by preventing yield collapse due to pH changes, leading to higher titers.
- Phosphate Buffering: Maintaining optimal concentration and pH of mono- and dibasic potassium phosphates is crucial, as too little or too much severely reduced titer.
- NMPs vs. NTPs: The LLM concluded that using Nucleoside Monophosphates (NMPs) is more cost-effective than Nucleoside Triphosphates (NTPs) for achieving high protein titers. Partial replacement of NMPs with corresponding nucleosides or bases was also found to reduce costs while preserving titers.
- Spermidine: This low-cost component correlated with increased protein titers, likely by stabilizing nucleic acids and ribosomal function, enhancing transcription and translation efficiency.
- Sodium Hexametaphosphate: No beneficial effect on titers was observed from the addition of this component.
- Cost Dominance: Reaction costs are primarily driven by cell lysate and DNA template, making titer improvements from buffer/pH control or spermidine additions particularly impactful for overall specific cost reduction.
These findings provide actionable insights for further optimizing cell-free protein synthesis processes.
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Your AI Implementation Roadmap
A phased approach to integrate autonomous lab capabilities and AI-driven scientific discovery into your enterprise.
Phase 1: Discovery & Strategy (2-4 Weeks)
Conduct a deep dive into your current R&D workflows, identify automation opportunities, and define key performance indicators (KPIs) for AI integration. Develop a tailored strategy aligning AI capabilities with your scientific objectives.
Phase 2: Pilot Program & Integration (8-12 Weeks)
Implement a small-scale pilot project, integrating LLM-driven experimental design with a subset of your lab automation infrastructure. Establish data pipelines and Pydantic validation schemas to ensure robust AI-human collaboration.
Phase 3: Scalable Deployment & Optimization (Ongoing)
Expand AI integration across relevant R&D functions, leveraging iterative optimization loops to continuously improve efficiency, cost-effectiveness, and discovery rates. Implement continuous monitoring and adaptive learning for sustained impact.
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