Cloud laboratories

Cloud laboratories are multi-tenant, remotely operated wet-lab facilities that execute user-defined experiments submitted through an API or web portal and return structured, machine-readable data. A protocol written in a high-level language (Strateos SBOL/Protocol, Emerald Cloud Lab Symbolic Language) compiles down to instrument commands run on a shared robotic fleet — liquid handlers, incubators, plate readers, bioreactors, cell sorters — that many users rent by the run rather than own. The model decouples experiment design from physical bench work, lets a biotech run wet-lab campaigns without a lab, and is the substrate for the AI-driven autonomous (self-driving) lab, in which machine-learning planners close the loop between result and next experiment. The platform is mid-transformation along four axes: full-stack automation that takes a single facility from sample-in to structured-data-out with no human intervention between; API-as-a-service bioinformatics that lets computational teams call wet biology like a function; closed-loop autonomous labs (Arctoris Ulysses, XtalPi Science) that design and iterate their own experiments; and cloud-connected bioprocess (Culture Biosciences Nexxys) that extends the model from bench assays to upstream bioreactor operation.

The key directions of cloud laboratories are:

1. API-driven full-stack wet labs (Strateos, Emerald Cloud Lab): facilities whose entire protocol — from cell thaw to assay readout — is specified in code and executed on a robotic fleet, returning structured JSON data via API.
2. Closed-loop autonomous drug-discovery labs (Arctoris Ulysses, XtalPi Science): platforms that couple robotic execution with machine-learning planning to design, run and interpret their own assay cycles, with AI agents (XtalPi Genius Agents) driving the experiment loop.
3. Cloud-connected bioprocess (Culture Biosciences Nexxys): extending the cloud-lab model from bench assays to upstream bioreactor operation, letting process-development teams run parallel fermentation campaigns remotely.
4. Protocol-as-code and reproducibility (Symbolic Language, SBOL): unambiguous, version-controlled protocol descriptions that make every run exactly repeatable across sites — the software-engineering discipline that cloud labs bring to wet biology.

Sectoral value chain#

[protocol-as-code] ──> [compile to instrument commands] ──> [robotic execution on shared fleet]
                                                            │
                                                  (inline analytics + structured data)
                                                            │
                                                            ▼
[AI planning / next cycle] <─── [structured JSON results] <─────┘

Value chain levels#

Cross-cutting technologies of the sector:

• Cloud laboratory (Cloud Laboratory): remotely operated, API-driven, multi-tenant wet-lab facility returning structured data.
• Robotic autonomous lab (Robotic Autonomous Lab): closed-loop platform coupling robotics, analytics and ML planning for self-driving experiment cycles.
• High-throughput screening (High-Throughput Screening): the assay layer whose parallelisation makes cloud labs economical.

The stack combines protocol-as-code, a shared robotic fleet, structured data return and an ML planning layer into one programmable substrate.

1. Protocol-as-code (Protocol-as-Code):
   • high-level symbolic languages (Emerald Cloud Lab Symbolic Language, SBOL, Strateos Protocol) that compile unambiguously to instrument commands;
   • version-controlled protocols make every run exactly repeatable across sites and time.
2. Shared robotic fleet (Shared Robotic Fleet):
   • liquid handlers, incubators, plate readers, cell sorters and bioreactors scheduled across many tenants;
   • full-stack automation (Emerald ECL1) takes a facility from sample-in to structured-data-out with no human intervention between.
3. Structured-data return (Structured-Data Return):
   • raw assay data normalised to structured JSON and delivered via API or dashboard;
   • enables computational teams to call wet biology as a function and feed results straight into ML pipelines.
4. ML planning & the autonomous loop (ML Planning & Autonomous Loop):
   • machine-learning agents (Arctoris Ulysses, XtalPi Genius Agents) interpret results and propose the next run, closing the self-driving lab loop.