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NK Cell Missing-Self Recognition — Signal-Space Map

Interactive 2D signal-space simulation of natural killer cell decision-making: every target cell is plotted by its MHC-I (inhibitory) and stress-ligand (activating) level, a linear decision boundary separates the kill zone from the spare zone, and NK scanner agents traverse the map deciding each cell's fate.

Molecular Biology2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

Natural killer cells decide, target by target, whether to kill by weighing an inhibitory MHC-I signal against an activating stress-ligand signal. Rather than a rendered field of tissue, this view plots the whole target population directly in the two-dimensional space of those signals: MHC-I on one axis, stress ligand on the other. A straight decision-boundary line — the same rheostat rule the biology uses — separates the plane into a kill zone and a spare zone, and NK scanner agents sweep across this signal-space map, docking on the nearest cell and reading off its verdict. Downregulate MHC-I across the population, or carve out an infected patch, and watch points slide across the line: missing-self recognition made visible as pure geometry.

⚙ Under the hood

A 2D signal-space companion to the 3D NK cell scanner: every target cell is plotted by its MHC-I (inhibitory) and stress-ligand (activating) level, a linear decision boundary divides the plane into kill and spare zones, and NK scanner agents sweep the map itself, docking on the nearest cell by signal-space distance and reading off the missing-self verdict.

immunologyNK cellsMHC-Imissing-self hypothesiscell biologyimmune signalingdecision boundarysignal space

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install