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TeraCyte
Predict Drug Toxicity in Clinical Trials.

This is the promise of Predictive Biology.

A future where AI can forecast biological outcomes before they occur.

The Foundation

Building the Data Layer for Predictive Biology

AI can predict words, images, and proteins, yet it still struggles to predict biological outcomes. The missing ingredient is dynamic biological data.

The Gap

Biology's AI Revolution Is Missing Its Data Layer

Despite remarkable advances in genomics, proteomics, and AI, biology remains difficult to predict. Most biological datasets capture a single moment in time, while biology itself is dynamic. Without dynamic data, AI can describe biology, but it struggles to predict it.

Static Data

  • Snapshot
  • Current State
  • Description

Dynamic Biology

  • Trajectory
  • Behavior
  • Change Over Time

Prediction

  • Outcome
  • Response
  • Forecast

The Shift

From Biological Snapshots
to Biological Trajectories

Traditional biological measurements capture isolated snapshots of cells at a single point in time. Yet biological outcomes are determined not by a cell's current state, but by how it behaves and evolves over time. TeraCyte captures the behavior of millions of living cells, revealing dynamic cellular trajectories that contain predictive information invisible to conventional approaches.

Visualization showing the transformation from static cellular snapshots to dynamic biological trajectories that reveal predictive patterns

Snapshot

Single point in time

Trajectory

Behavior over time

Prediction

Forecasted outcome

The Innovation

BioTokens™: Making Living Biology
Learnable by AI

BioTokens™ are the language layer that makes living biology learnable by AI. TeraCyte transforms dynamic live-cell behavior into compact, AI-ready representations that capture how cells change, respond, adapt, and evolve over time.

These BioTokens allow predictive models to learn from cellular behavior, not just molecular snapshots, enabling a new generation of biological AI trained on the signals that drive biological outcomes.

Abstract visualization of BioTokens - compact geometric data representations encoding dynamic cellular behavior patterns
Living Cells
Dynamic Behavior
BioTokens™
Predictive AI
Biological Outcomes

Applications

Predictive Biology Across
Multiple Applications

Triptych showing immunology, bioproduction, and clinical applications of predictive biology through cellular visualization
First Focus

Immunology

Predict immune response, disease progression, and therapeutic outcomes by learning directly from dynamic immune-cell behavior. Immunology is our first focus and the foundation of our growing BioToken™ dataset.

Active

Bioproduction

Predict clone performance, potency, and manufacturing yield, enabling earlier decisions and more efficient biologics development and manufacturing.

Expanding

Future Opportunities

Extend predictive biology across additional diseases, therapies, and cellular systems as the BioToken™ platform continues to grow.

One platform. One data layer. Multiple predictive applications.

The Platform

The Platform Behind
Predictive Biology

Proprietary Silicon Imaging Arrays

Custom-designed silicon imaging arrays enable scalable, high-throughput observation of millions of living cells.

Temporal Cytometry™

Continuous morphological and functional characterization of cellular behavior over time, capturing the dynamics that drive biological outcomes.

BioTokens™

AI-ready representations that encode how cells change, respond, adapt, and evolve.

Predictive AI Models

Models trained on dynamic cellular behavior to predict biological outcomes across immunology, bioproduction, and future clinical applications.

Vertical pipeline visualization showing Silicon Imaging Arrays flowing through Temporal Cytometry to BioTokens and Predictive AI Models

Ecosystem

Building the Ecosystem for
Predictive Biology

Building a new data layer for biology requires expertise spanning life sciences, artificial intelligence, semiconductor technology, and clinical medicine. TeraCyte is bringing together leading scientists, clinicians, technology partners, and research institutions to advance the future of predictive biology.

Network visualization showing TeraCyte ecosystem of scientific, clinical, and technology partners

Scientific Leadership

Prof. Roger D. Kornberg

Prof. Roger D. Kornberg

Nobel Laureate

Stanford

Prof. Galit Lahav

Prof. Galit Lahav

Chair, Systems Biology

Harvard Medical School

Prof. Michael B. Elowitz

Prof. Michael B. Elowitz

Caltech/HHMI

Clinical Ecosystem

IBFI Consortium (pending final IIA approval)

A multi-institution initiative generating large-scale clinical datasets and computational models that enable predictive understanding of immune behavior and therapeutic response. Official launch is expected in summer 2026.

Ecosystem Partners

NVIDIA
Teva
Sheba Medical Center
Technion

The Future

The Future of Biology
Is Predictive

Traditional biology measures what is happening.

Predictive biology forecasts what happens next.

The move from measurement to prediction represents the next frontier of biology, medicine, and AI.

This is the future we are building at TeraCyte: a world where biology is not only measured, but predicted.