Built on Google Cloud
Biology transformed with Google Cloud

Bioptimus Universal Biological Foundation Model on GCP

Bioptimus provides a universal AI foundation model for biology, integrating multimodal and multiscale data—from molecules and cells to whole-organism context. On Google Cloud Platform (GCP), the model is trained, governed, and deployed using cloud-native services that make advanced biomedical AI accessible to research, clinical, and industry partners worldwide.

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Solution overview

How Bioptimus runs on Google Cloud

The Bioptimus Universal Biological Foundation Model integrates genomics, microscopy, digital pathology, and clinical metadata into a unified representation that captures patterns across scales. On Google Cloud, Bioptimus uses managed AI, compute, and data services to train large models, orchestrate reproducible pipelines, and serve low-latency inference APIs to partners.

GCP provides secure, compliance-aligned infrastructure for processing sensitive biomedical data while enabling high-throughput experimentation. Research groups and clinical teams interact with the platform through well-defined APIs and workflows, without needing to manage low-level infrastructure.

Key capabilities

  • Multimodal, multiscale modeling – jointly represents molecular, tissue, and patient-level data.
  • Foundation model backbone – pre-trained on large-scale biological datasets, then specialized for tasks such as biomarker discovery, survival prediction, and patient stratification.
  • Cloud-native deployment – autoscaled, containerized model services running across GKE and Vertex AI.
  • Research-to-production path – shared pipelines and artifacts from experimentation to clinical validation.

High-level GCP architecture

                        ┌──────────────────────────────┐
                        │   Biomedical data sources    │
                        │  (omics, H&E, slides, EHR)   │
                        └──────────────┬───────────────┘
                                       │
                           Ingestion & preprocessing
                                       │
                     ┌─────────────────▼─────────────────┐
                     │    Cloud Storage / BigQuery       │
                     │  Raw & curated multimodal data    │
                     └─────────────────┬─────────────────┘
                                       │
                              Feature generation
                                       │
         ┌─────────────────────────────▼─────────────────────────────┐
         │             Vertex AI Pipelines / GKE jobs                │
         │  - Large-scale training on GPU / TPU (Compute Engine)    │
         │  - Hyperparameter search, evaluation, model selection    │
         └─────────────────────────────┬─────────────────────────────┘
                                       │
                                Model registry
                                       │
                    ┌──────────────────▼─────────────────┐
                    │      Artifact Registry / Vertex    │
                    │      AI Model Registry             │
                    └──────────────────┬─────────────────┘
                                       │
                                   Deployment
                                       │
         ┌─────────────────────────────▼─────────────────────────────┐
         │         GKE + Vertex AI Endpoints (online / batch)       │
         │  - REST/gRPC APIs for research & clinical partners       │
         │  - Autoscaling, canary rollout, A/B testing              │
         └─────────────────────────────┬─────────────────────────────┘
                                       │
                           Monitoring & governance
                                       │
                        Cloud Logging / Monitoring / IAM
Applications

What the foundation model enables on GCP

Built on Google Cloud, the Bioptimus Universal Biological Foundation Model supports diverse scientific, clinical, and industrial use cases while keeping the core infrastructure consistent and maintainable.

Translational & basic research

Research labs leverage pretrained representations hosted on Vertex AI to accelerate discovery across oncology, immunology, and rare disease research. Multimodal embeddings can be exported to BigQuery for downstream analytics, clustering, and hypothesis generation without handling raw PHI.

  • Self-supervised learning from large biological corpora
  • Cross-modal retrieval between images, omics, and text
  • Foundation features for new tasks with minimal labeling

Pharma & biotech pipelines

Pharmaceutical and biotech companies connect to GCP-hosted inference APIs for biomarker discovery, cohort selection, and mechanism-of-action studies. Autoscaled GKE deployments allow teams to run large retrospective analyses without managing GPU clusters.

  • Biomarker and pathway enrichment predictions
  • Patient stratification across trial arms
  • Integration with existing GCP-based R&D data lakes

Clinical prediction & decision support

In collaborations like the MIT clinical cancer prediction study, Bioptimus models deployed on Vertex AI Endpoints generate progression-free survival and risk predictions from digital pathology and clinical covariates, with strict access control and auditing managed through IAM and Cloud Logging.

  • Prognostic models for survival and response
  • Embedding-based patient similarity search
  • Explainability tooling integrated via custom services
Trust, security, and governance

Operating sensitive biological workloads responsibly

Biomedical data is among the most sensitive information handled in modern research and healthcare. Bioptimus uses Google Cloud’s security and governance capabilities to ensure that its foundation-model platform is ready for real-world deployments.

Data is logically isolated by tenant using separate projects and VPCs. IAM roles and service accounts govern access to storage, compute, and model endpoints. When partners require full control, Bioptimus can deploy dedicated stacks in their own GCP projects while preserving the same architecture and best practices.

Security & compliance features

  • Encryption in transit & at rest using default GCP mechanisms for Cloud Storage, BigQuery, and databases.
  • Fine-grained access control via IAM, service accounts, and per-tenant projects.
  • Network isolation with private VPCs, private service connect, and restricted egress patterns.
  • Secrets management through Secret Manager for credentials and key material.
  • Observability & audit trails using Cloud Logging, Cloud Monitoring, and organization-level policy controls.

Reliability & operations

  • Blue/green and canary rollouts for model versions.
  • Autoscaling for GPU-backed deployments on GKE.
  • SLOs around latency, availability, and throughput.
  • Unified dashboards for data pipeline health and model performance drift.
Getting started

Adopting Bioptimus on Google Cloud

Bioptimus works with partners across research, pharma, and healthcare to deploy tailored versions of the Universal Biological Foundation Model on top of their existing or new GCP environments. Engagements typically follow a staged approach that validates value quickly while building a sustainable platform.

Typical engagement phases

  1. Discovery & data review – understand available modalities (slides, omics, EHR), governance requirements, and existing GCP footprint.
  2. Pilot deployment – provision a dedicated GCP project, ingest a limited dataset, and deploy pre-trained Bioptimus models for a focused use case (e.g., one cancer indication).
  3. Model adaptation – fine-tune or specialize the foundation model on partner data using Vertex AI pipelines, with evaluation metrics validated by domain experts.
  4. Production rollout – integrate APIs into downstream research or clinical systems and harden SLOs, security controls, and monitoring.

Example GCP project layout

bioptimus-root-org/
  ├─ proj-bioptimus-shared-tools
  │    ├─ Shared CI/CD, Artifact Registry, observability
  ├─ proj-bioptimus-ref-arch
  │    ├─ Reference pipelines & sample datasets
  ├─ proj-partner-xyz-sandbox
  │    ├─ Initial PoC, limited access data
  └─ proj-partner-xyz-prod
       ├─ Hardened VPC, private GKE & Vertex endpoints
       ├─ Cloud Storage buckets for curated datasets
       └─ BigQuery datasets for analytics & monitoring

To explore Bioptimus on Google Cloud or discuss a collaboration, reach out via the contact details on bioptimus.com or integrate this page into your GitHub repository as a public overview of your GCP-powered foundation model.