Biological Navigation
We build computational frameworks that learn the geometry and dynamics of biological state space to identify the minimal interventions required for moving the system from one state to another.
Explore the Idea
We build computational frameworks that learn the geometry and dynamics of biological state space to identify the minimal interventions required for moving the system from one state to another.
Explore the IdeaStable biological states correspond to attractors in a high-dimensional space. We rank candidate control variables by their geometric leverage on transitions between these attractors.
Infers control variables from the geometry and dynamics of state space alone, without requiring pre-existing regulatory networks.
Defines "minimal intervention" as the perturbation that maximally redirects trajectory direction per unit of feature change.
Explicitly distinguishes between geometric candidates and causal drivers, acting as a high-precision hypothesis generation engine.
If biological states are navigable, then cellular and organismal states are theoretically redirectable toward desirable states. Our core mission is to determine if this is possible and if so, solve for the minimal set of variables needed to move a cell or organism from state A to state B.
Proof of Concept
We are currently using high quality rich single-cell transcriptomic data sets from Pancreatic Endocrinogenesis & Dentate Gyrus Neurogenesis to establish proof of concept. Come back soon to see updates on our progress.