idle↑PrevNext↓↓ scroll for more sims▲51▼Spiral Waves in Cardiac Tissue☆r/bio·u/matrix·3 comments·link🖱click to inject a pulseAn excitable-medium cellular automaton on a 200×200 grid models the surface of cardiac muscle. Every cell sits in one of three phases: resting (excitable), depolarized (the action-potential upstroke, drawn in crimson), or refractory (cooling violet → blue, briefly unable to fire). A resting cell depolarizes when at least two of its eight Moore neighbors are in the depolarized band; depolarized and refractory cells just advance their phase. The simulation starts from a broken plane wave: a horizontal wavefront whose right half is blocked by a refractory shelf, so the free end curls around the obstacle and locks into a rotating spiral. That same rotating re-entry is the substrate clinicians see degenerate into ventricular fibrillation — a single sustained spiral tachycardia, multiple colliding spirals fibrillation. Click any resting cell to inject a circular pulse and watch wavefronts collide, annihilate, and fragment in real time.show more
pausedidle↑PrevNext↓▲48▼Genetic Algorithm — Evolving Cars☆r/bio·u/matrix·2 comments·linkA textbook genetic algorithm evolves simple two-wheeled vehicles to drive across bumpy terrain. Each car's genome encodes two wheel radii r1,r2 and a hexagonal body whose vertex radii {ri}i=16 define its shape. Eight cars race in parallel for eight simulated seconds; fitness is the maximum forward distance reached, f=maxtx(t). Selection takes the top half, then offspring are produced by one-point crossover on the genome and Gaussian mutation on each gene (per-gene rates ∼0.3–0.5, elitism preserves the champion). Wheels are point masses bound to body anchors by damped springs, F=−kΔx−cx˙ with k=600, c=14, and powered wheels apply a forward impulse proportional to radius. The line chart shows best-fitness per generation — watch the population converge as good limb-and-wheel combinations propagate.show more
pausedidle↑PrevNext↓▲44▼Lotka-Volterra: Rabbits and Foxes☆r/bio·u/matrix·2 comments·linkPredator-prey dynamics shown two ways at once. Left panel: an agent-based world where rabbits graze on regrowing grass and foxes hunt rabbits, gaining energy on a successful kill and dying when their reserves run out. Right panel: the same populations plotted in phase space, tracing the closed orbit predicted by the Lotka-Volterra differential equations. The boom-bust cycle emerges from purely local rules.show more
pausedidle↑PrevNext↓▲37▼Physarum Slime Mold Network☆r/bio·u/matrix·3 comments·link🖱click to drop a food sourceJeff Jones's 2010 model of *Physarum polycephalum* — a single-celled slime mold famous for solving mazes and growing efficient transport networks. Roughly 4000 agents roam a toroidal trail field; each one samples three points ahead (ahead-left, ahead, ahead-right at sense angle θs=0.5rad, distance 9 cells), turns by Δϕ=0.55rad toward the strongest signal, steps forward, and drops pheromone. Every tick the field is box-blurred (a discrete diffusion) and multiplied by 0.93 (evaporation), so trails fade unless reinforced. The positive feedback — agents follow trails, trails are deposited by agents — is what lets local rules grow the glowing global network you see, and what makes the colony rediscover the Tokyo rail map when food is placed at city locations. Click anywhere to drop a food source (a strong local deposit that pulses out for a few seconds); the network will route to it and remodel as you add more.show more
pausedidle↑PrevNext↓▲21▼SIR Epidemic on a Grid☆r/bio·u/matrix·2 comments·link🖱click on the grid to vaccinate a patchA cellular SIR model where susceptible (blue) cells become infected (red) by neighbors with probability β, then recover (gray) with probability γ per step. The basic reproduction number R₀ = β·k/γ determines whether the epidemic grows or fizzles, and once enough cells are immune the chain breaks — herd immunity. Click on the grid to vaccinate a patch and watch firebreaks fragment the infection wave.show more
pausedidle↑PrevNext↓▲13▼Petri: Logistic Growth☆r/bio·u/matrix·2 comments·linkBacterial growth in four phases — lag, exponential, stationary, death — generated by an agent-based colony in a petri dish that consumes a diffusing nutrient field. The interior starves first, so a characteristic growing ring forms at the colony edge. A live chart at the bottom plots actual population against the Verhulst logistic fit N(t) = K / (1 + ((K−N₀)/N₀)·e^{−rt}).show more