# Cloud laboratories Multi-tenant, remotely operated wet labs that execute user-submitted protocols via API and return structured data — the layer that turns biology into programmable infrastructure, decoupling experiment design from bench work and feeding the closed-loop AI-driven autonomous lab. Source: https://en.bioecon.ru/technology/cloud-laboratories/ Updated: 2026-08-18 ## Overview and value chain Markers: [EC: FDA 21 CFR Part 11 & GxP data integrity | OECD: Biotech & health | Regulator: FDA (USA), EMA (EU), NMPA (China)] 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 | Level | Description | Key inputs/outputs | |:---|:---|:---| | **Protocol-as-code** | writing the experiment in a high-level symbolic language (SBOL, Symbolic Language) | **In:** experimental intent. **Out:** compiled protocol. | | **Scheduling & execution** | dispatching runs to the shared robotic fleet (liquid handlers, readers, bioreactors) | **In:** compiled protocol. **Out:** executed run. | | **Inline analytics** | plate readers, cell counters, mass spec, inline bioprocess sensors generating raw data | **In:** executed run. **Out:** raw assay data. | | **Structured-data return** | normalising raw data to structured JSON delivered via API or dashboard | **In:** raw data. **Out:** structured JSON results. | | **AI planning (autonomous loop)** | machine-learning agent interpreting results and proposing the next run | **In:** structured results. **Out:** next protocol. | | **GxP archive & audit** | 21 CFR Part 11 electronic records and full run provenance for regulated work | **In:** run records. **Out:** released, traceable data package. | 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. --- ## US The United States is the centre of the cloud-laboratory category: three of the four leading full-stack platforms are US-based. Strateos (San Diego, formerly Transcriptic) pioneered the API-driven wet lab; Emerald Cloud Lab (Austin, TX and South San Francisco) operates the most fully automated single facility; Culture Biosciences (South San Francisco) leads the cloud-connected bioprocess segment. FDA's 21 CFR Part 11 data-integrity framework is what qualifies cloud-lab output for regulated pharmaceutical work. ### API-driven wet labs, cloud bioprocess, FDA data integrity - **Strateos:** the API-driven cloud lab with the Science Board marketplace; compiles user protocols to instrument commands on a shared robotic fleet. - **Emerald Cloud Lab:** the most fully automated single facility (ECL1), executing end-to-end protocols in the Emerald Cloud Lab Symbolic Language. - **Culture Biosciences — Nexxys:** cloud-connected bioprocess platform letting process-development teams run parallel fermentation campaigns remotely. - **FDA 21 CFR Part 11:** electronic-records and audit-trail standard that qualifies cloud-lab output for GxP pharmaceutical R&D. --- ## CN China is the demand-side growth market and home to the leading AI-integrated autonomous lab: XtalPi (晶泰科技, HKEX:2228, Shenzhen and Cambridge, MA) couples its XtalPi Science platform — Genius Agents (AI agents addressing long-workflow hallucination) and a physical autonomous lab — to close the loop between computational drug design and robotic wet-lab validation. Domestic pharmaceutical and CRO demand for remote, reproducible wet-lab capacity is the growth driver, with NMPA ICH-aligned data-integrity expectations framing the regulated route. ### AI-integrated autonomous lab, pharmaceutical R&D demand, NMPA data integrity - **XtalPi (晶泰科技):** XtalPi Science platform coupling Genius Agents (AI planning) with a physical autonomous lab for closed-loop drug-discovery validation, HKEX:2228. - **Domestic pharmaceutical R&D demand:** biotech and CRO adoption of remote, reproducible wet-lab capacity under cost and reproducibility pressure. - **NMPA ICH-aligned data integrity:** harmonised 21 CFR Part 11-equivalent electronic-records expectations qualifying cloud-lab output for regulated Chinese pharmaceutical R&D. --- ## EU Europe anchors the closed-loop autonomous drug-discovery segment: Arctoris (London, UK, with US operations) operates the Ulysses platform, which couples closed-loop robotics and machine learning to automate end-to-end drug-discovery workflows. EMA's GxP and ICH-aligned data-integrity framework define the regulated route for cloud-lab output in European pharmaceutical R&D. ### closed-loop autonomous labs, EMA GxP data integrity - **Arctoris — Ulysses platform:** closed-loop robotics + machine learning automating end-to-end drug-discovery workflows, with Boston and London operations. - **EMA GxP & ICH data integrity:** the EU electronic-records and audit-trail framework qualifying cloud-lab output for regulated pharmaceutical R&D. --- ## Leading companies and research institutes | Company / Institute | Country | Key products / platforms | Tech features | Status 2026 | |:---|:---|:---|:---|:---| | **Strateos** | 🇺🇸 USA | *Strateos Cloud Lab, Science Board* | API-driven wet lab; compiles protocols to instrument commands on a shared fleet | Commercial | | **Emerald Cloud Lab** | 🇺🇸 USA | *ECL1 facility, Symbolic Language* | Most fully automated single facility; full-stack protocol-as-code (founded 2010) | Commercial | | **Culture Biosciences** | 🇺🇸 USA | *Nexxys cloud bioreactor* | Cloud-connected bioprocess; remote parallel fermentation campaigns | Commercial | | **Arctoris** | 🇬🇧 United Kingdom | *Ulysses platform* | Closed-loop robotics + ML automating end-to-end drug discovery | Commercial | | **XtalPi** | 🇨🇳 China | *XtalPi Science, Genius Agents* | AI-integrated autonomous lab closing the drug-design/wet-lab loop (HKEX:2228) | Commercial | --- ## Tech stack and innovations 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. --- ## Value chains and production pipelines ### Industrial pipeline of a cloud-laboratory run (21 CFR Part 11) ``` ┌───────────────────────────┐ ┌───────────────────────────┐ │ 1. Protocol-as-code │ ───> │ 2. Compile & schedule │ └───────────────────────────┘ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ ┌───────────────────────────┐ │ 4. Structured JSON return │ <─── │ 3. Robotic execution │ └───────────────────────────┘ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ ┌───────────────────────────┐ │ 5. ML planning (next run) │ ───> │ 6. GxP archive & audit │ └───────────────────────────┘ └───────────────────────────┘ ``` #### Stage 1: Protocol-as-code The user writes the experiment in a high-level symbolic language (SBOL, Emerald Symbolic Language, Strateos Protocol) that specifies every step, reagent and instrument setting unambiguously. #### Stage 2: Compile and schedule The protocol compiles to instrument commands and is scheduled onto the shared robotic fleet, queued alongside other tenants' runs for capacity-optimal execution. #### Stage 3: Robotic execution Liquid handlers, incubators, plate readers, cell sorters or bioreactors execute the run with no human intervention between steps, capturing every action in an electronic run log. #### Stage 4: Structured-data return Inline analytics (plate readers, cell counters, mass spec, bioprocess sensors) generate raw data that is normalised to structured JSON and delivered via API or dashboard. #### Stage 5: ML planning For autonomous platforms, a machine-learning agent (Arctoris Ulysses, XtalPi Genius Agents) interprets the structured results and proposes the next run, closing the self-driving lab loop. #### Stage 6: GxP archive and audit Every run's electronic records, instrument telemetry and full provenance are archived under 21 CFR Part 11, producing a traceable, release-ready data package for regulated work.