SYSTEMS / BIOLOGICAL
Biological Systems — Virtual Cell
Modeling cells as dynamic systems under constraints.
SYSTEM OVERVIEW
Cells compute state through constraints.
Biological systems are not static pathways. They are dynamic state machines shaped by regulation, metabolism, and conservation laws. Signals such as cfRNA are sparse and indirect; simulation is required to reconstruct hidden state, test perturbations, and separate mechanism from correlation.
CORE CAPABILITIES
From sparse signals to mechanistic state transitions.
Cellular State Reconstruction
Infer hidden biological state from partial observations.
- —Reconstruct latent cellular state from sparse molecular signals
- —Integrate cfRNA with metabolic dynamics and regulatory constraints
- —Maintain state continuity across time and conditions
Dynamic Simulation
Simulate state transitions over time under constraints.
- —Evolve cellular state in time with explicit transition dynamics
- —Stress stability under competing constraints and noise
- —Explore trajectories across regimes, not single-point outputs
Intervention Modeling
Model response to perturbations as controlled dynamics.
- —Represent drugs and perturbations as interventions on structure
- —Compare counterfactual responses across time horizons
- —Bound uncertainty and failure modes in the response surface
Mechanism Extraction
Identify causal structure behind observed effects.
- —Extract causal structure consistent with constraints and dynamics
- —Trace which mechanisms drive state transitions
- —Separate stable mechanism from regime-specific artifacts
Early Signal Detection
Detect weak shifts before phenotype changes.
- —Surface subtle state drift before macroscopic changes appear
- —Detect anomalies as deviations in dynamics, not thresholds
- —Provide early warning signals for intervention timing
SYSTEM LAYERING
A layered virtual cell system.
Data layer
Encode cfRNA and molecular signals as time-indexed observations.
Structure layer
Represent regulatory and metabolic constraints as system structure.
Simulation layer
Run cellular dynamics to explore trajectories and perturbations.
Decision layer
Translate mechanisms into intervention choices with traceability.
OUTCOME
Biology becomes operational.
Interventions can be evaluated as changes to mechanism: state reconstructed, transitions simulated, and responses compared without relying on static pathway assumptions.