The Broad Institute is our primary academic research partner for large-scale genomics and CRISPR development. This collaboration provides access to the NIH's All of Us genomic database — over 1 million participant genomes — for PreviGen™ validation, as well as licensing of next-generation CRISPR base-editing tools.

Our European bioinformatics backbone. EMBL-EBI provides API federation with global biological databases — UniProt, Ensembl, and the Human Cell Atlas — enabling our Digital Twin platform to draw from the world's most comprehensive proteomics and transcriptomics reference sets. Critical for our Digital Twin validation against 10,000+ retrospective patient datasets.

Ginkgo Bioworks operates the world's largest cell programming infrastructure. Our collaboration leverages their automated foundry for the development and scale-up of TerraBacteria organisms and synthetic pollinators. Ginkgo's high-throughput strain design platform accelerates our in-house directed evolution cycles from months to days.

Pasqal builds neutral-atom quantum processors capable of simulating molecular interactions at scales that surpass classical hardware. Our collaboration integrates Pasqal QPU nodes directly into the Bio-Compute Cloud for drug-protein binding simulations, enzyme reaction modelling and quantum-enhanced protein folding predictions. A cornerstone of the quantum biology domain.

Owkin pioneered federated learning for medical AI — training models on patient data across institutions without the data ever leaving the hospital. This is the privacy architecture underlying our Bio-Compute Cloud and PreviGen™ model training. Owkin's framework ensures GDPR compliance and enables cross-continental AI training without genomic data sovereignty risks.

Revive & Restore is the world's leading wildlife conservation organisation specialising in genomic technologies for de-extinction. Together, we are developing the methodological framework for eventual species revival using Phoenix Genome Bank material — defining viability criteria, ecological suitability scoring, and reintroduction protocols for CRISPR-reconstructed genomes.

Oxford Nanopore's PromethION platform is central to our real-time sequencing pipeline in the SynBio Quantum Lab, and their MinION devices power our Bio-FabLab Mobile field operations. Their nanopore technology enables direct, label-free DNA and RNA sequencing at single-molecule resolution — the data feed for our Digital Twin platform's continuous recalibration engine.

Twist Bioscience manufactures high-fidelity synthetic DNA on silicon at a scale and accuracy no other platform matches. They are the synthesis arm of our AI Evolutionary Engine — translating AI-designed protein sequences into physical oligo pools and gene constructs within 48 hours. This closes the in silico → wet lab loop at unprecedented speed.

Toulouse Hub – AI for Good is the ethical and responsible AI cornerstone of the Noah Ark ecosystem. Co-founded by Adam Kouadri, Noah Kouadri Khazar and Sarah Kouadri, it provides the ethical review framework, federated learning governance, and community AI deployment expertise that underpins every Noah Ark AI system. Officially recognised by the ITU (United Nations) and represented at Davos 2026.

SomaLogic's SomaScan platform measures 7,000+ proteins simultaneously from a single blood sample — the most comprehensive proteomics panel in clinical use. This multi-omic data layer complements our epigenomic biomarker profiles in PreviGen™, creating a combined genomic + proteomic early-warning signal with substantially higher predictive accuracy than either alone.

The Earthshot Prize is the world's most prestigious environmental award — rewarding and accelerating solutions to the planet's greatest environmental challenges. Noah Ark is pursuing Earthshot candidacy for the TerraBacteria Climate Platform and Phoenix Biodiversity Programme. The Prize's network provides not only funding but global deployment infrastructure and regulatory facilitation across 50+ countries.

AWS HealthOmics is the cloud backbone of Noah Ark's Bio-Compute infrastructure — providing petabyte-scale genomic data storage, variant calling pipelines, and HIPAA-compliant data exchange. It serves as the classical compute layer beneath our Pasqal QPU integration, enabling seamless orchestration of hybrid quantum-classical workflows at clinical data scale.

A diagnostic method employing a novel multi-locus CpG methylation signature panel — identified via machine learning analysis of 1M+ genome cohort — for predicting onset risk of Alzheimer's disease, breast cancer, and type 1 diabetes 10–20 years before clinical manifestation.

A computational platform for designing novel protein sequences using generative adversarial networks trained on evolutionary fitness landscapes — producing enzymes, antibodies, and functional biomolecules with user-defined activity profiles. Validated against 3,000+ experimentally characterised protein variants.

Novel engineered PETase enzyme variants (FAST-PETase v3 series) exhibiting 40× greater catalytic activity at ambient water temperatures (15–25°C) compared to wild-type, enabling practical ocean and freshwater microplastic degradation without energy input. Produced via our AI Evolutionary Engine and validated in controlled marine microcosm studies.

A system and method for constructing and maintaining a continuously-updated patient-specific biological digital twin — integrating real-time long-read nanopore sequencing data, 3D organoid experimental validation, and AI-based state-space modelling for simulating physiological responses to therapeutic interventions before clinical administration.

A fully automated, AI-indexed cryogenic biological archive system designed for long-term storage of multi-species genetic material. Combines robotic liquid handling, barcode-tracked cryo-vials at −196°C, and a machine learning catalogue system enabling retrieval of specific genomic material with 99.99% accuracy from archives of 150,000+ samples.

A cryptographic framework applying Zero-Knowledge Proof (ZKP) protocols to genomic data federation — enabling AI models to be trained, validated and audited across institutional genomic databases without any raw sequence data leaving the originating institution. Solves the fundamental tension between AI data hunger and genomic privacy rights.