Simulate how genetic and drug interventions affect cell states and functions to identify the key genes, pathways, and regulatory mechanisms driving disease phenotypes, accelerating the discovery, prioritization, and validation of candidate targets.
Build virtual cells that respond to genetic, pharmacological, and environmental interventions, simulating state changes, functional responses, and fate transitions across cell types and biological contexts to support cell research and engineering design.
Integrate multicellular, multi-omics, spatial microenvironment, and longitudinal disease data to reconstruct disease states and their progression, understand cell–cell interactions and disease heterogeneity, and predict the systemic impact of different intervention strategies.
Drug Perturbation and Virtual Clinical Trials in the Tumor Microenvironment (TME)
AI-Optimized Directed Differentiation Process for Stem Cells
Genetic Engineering and Functional Optimization of CAR-T Cells
AI-Driven Discovery of Skin Anti-Aging Targets
Tumor heterogeneity is hard to fully resolve
Cellular heterogeneity can exist both between patients with the same cancer type and within a single tumor; conventional population-average analyses struggle to reveal key cell subsets, functional states, and their differences
Differences in treatment response are hard to fully explain
Even with the same pathological diagnosis, patients can respond very differently to the same treatment. The effects of tumor cell states, the immune microenvironment, and key molecular pathways on treatment sensitivity and resistance are difficult to analyze systematically.
Stratification biomarkers are hard to validate consistently
Candidate biomarkers are easily affected by sample composition, assay platforms, and cohort differences; some lack a clear functional mechanism, creating high barriers to cross-cohort validation and clinical translation.
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Built on the CellOS foundation model, fully simulating cell differentiation state changes across multiple time points to accurately reproduce the dynamics of cell development.
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Simulate different combinations of transcription factors, signaling pathways, and culture conditions to compare their potential effects on cell fate and target phenotypes.
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Evaluate and rank candidate conditions in silico to deliver higher-potential validation plans for wet-lab testing.
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Assess the stability, interpretability, and generalizability of candidate stratification features using independent cohorts, retrospective data, and experimental validation where needed.
See Inside Tumor Heterogeneity
Form Stratification Hypotheses
Screen Candidate Biomarkers
Support Translational Study Design
