glazier

Cellular Potts tissue simulation on the CPU and the GPU.

A sheet or a block of cells on a periodic lattice, under contact, volume, surface and length energies, with diffusing chemical fields, chemotaxis, persistent motility, division, death and a connectivity veto. One JSON description drives the engine here, CompuCell3D, and PhysiCell for comparison.

Named after James Glazier, whose work with Graner and Hogeweg applied the Potts lattice to tissue.

Install

cargo add glazier-cpm                   # library via `use glazier::...`
cargo install glazier-cpm --features cuda   # binary with CUDA features
pip install glazier                    # Python-side bindings

glazier --model blueprints/monolayer.json --out runs/gpu --engine gpu

Layout

The engine is Rust. The harness that drives the other engines and measures what they produce is Python. Both read the same descriptions.

Path	Contents
glazier-core/	lattice, energies, serial sweep, the Blueprint reader, the .npy writer
glazier-cuda/	the checkerboard sweep on the device
glazier/	the facade crate and the glazier binary
glazier-py/	PyO3 bindings, built as glazier._native
glazier-tests/	Rust integration tests
python/glazier/	descriptions, engine backends, measurement
blueprints/	model descriptions every engine reads
bench/	the round trip against mermin, and every engine on one description
sims/	CompuCell3D models written directly, outside the Blueprint path
tests/	Python tests, including the one that compares the two readers

A Blueprint is parsed twice, once by serde and once by glazier.blueprint. tests/test_blueprint_conformance.py compares them field by field over every description in the tree, through the compiled module and through the binary.

The Rust side builds with cargo and the Python side runs under pixi, since CompuCell3D installs from a conda channel and pins its interpreter. mermin, my related cell-image analysis software, keeps its own environment, so the measurement in bench/roundtrip.py reaches it through files.

Scope

A sheet or a block of cells on a periodic lattice under four energy terms: a contact energy per unlike-label bond, lambda (V - V_target)^2 per cell, lambda (S - S_target)^2 on the surface counted as bonds to any other label, and lambda (L - L_target)^2 on the major axis of the cell's second-moment ellipse. CompuCell3D writes the first three the same way.

One Monte Carlo step is as many copy attempts as there are sites, after which the chemical fields diffuse and decay, cells secrete into and take up from the sites they own, and cells past their division volume split while cells of a type with a death rate die. A copy that would empty a cell is refused, so a cell leaves the lattice only by dying.

A cell with a chemotactic sensitivity prices each copy against the field at the two sites it runs between.

Description	What it exercises
blueprints/monolayer.json	contact and volume on four engines
blueprints/monolayer-surface.json	the surface term against CompuCell3D's
blueprints/monolayer-physicell.json	the same model across a geometry class
blueprints/infection.json	two fields, secretion and uptake, death
blueprints/immune-chemotaxis.json	a cytokine, chemotaxis, the length term
blueprints/volume.json	the same engine on a three-dimensional lattice

Two hundred steps of infection.json leave 229 of 256 cells alive with 2914 of virus and 654 of interferon on the lattice.

GPU sweep

Two copy attempts are independent when neither target sits in the other's neighbourhood. Colouring sites by (x mod 2, y mod 2) puts same-colour targets two apart, outside a Moore neighbourhood, so one colour's attempts all run at once and a step is the four colours in turn.

Note two differences:

• The serial engine draws targets with replacement; the device visits every site once per step.
• A cell's volume is read before a copy is priced and written with an atomic after, so several accepted copies on one cell within a colour each price their move against the same volume.

They therefore agree in distribution. tests/gpu_matches_cpu.rs checks cell count, mean volume and the spread about target, and the spreads stay within a quarter of each other.

Benchmarks

100 Monte Carlo steps, cells of side 8, on an RTX 5060 Laptop GPU against one CPU thread.

Lattice	Cells	CPU (s)	GPU (s)	Speedup
128 x 128	256	0.043	0.002	25
256 x 256	1024	0.171	0.002	77
512 x 512	4096	0.772	0.007	116
1024 x 1024	16384	3.146	0.024	129
2048 x 2048	65536	14.402	0.097	148
4096 x 4096	262144	135.620	0.446	304

The kernel is f32 throughout, which suits a card whose fp64 runs at a seventy-first of its fp32. The CPU reference is f64.

The draws are counter-based: each one is a hash of site, step, colour and index, so no generator state is stored. A xoshiro256++ state per site instead moved 64 bytes per site per colour, more traffic than the lattice itself, and ran 1.7 times slower on a card that is bandwidth-bound.

A short run through the CLI reads slower on the device than these figures, because the first call compiles the kernel with nvrtc and uploads the lattice. The CLI reports that setup separately from the steps.

Against CompuCell3D on the same model, 512 by 512 with 4096 cells over 200 steps: CompuCell3D 5.650 s, the serial reference 1.605 s, the device 0.067 s. That is 84 times CompuCell3D's own wall clock, on a model written the same way for both.

A coordinate wraps by comparison, since every caller steps one neighbour offset from a coordinate already in range. A remainder is a division and it sits in the innermost loop of the sweep, so the change took the serial engine from 4.05 s to 3.15 s at 1024 by 1024.

Engine comparison

bench/engines.py runs blueprints/monolayer-physicell.json on CompuCell3D 4.10, on both glazier engines and on PhysiCell 1.14.2: 128 by 128, 256 cells, 200 steps.

Quantity	CompuCell3D	glazier CPU	glazier GPU	PhysiCell
Cells	256	256	256	256
Mean area	64.0	64.0	64.0	58.5
Area rms from target	1.57	1.60	1.83	5.73
Mean eccentricity	0.402	0.394	0.394	0.042
Nematic order	0.093	0.065	0.073	0.164
Disclinations	44	38	56	16
Net charge	0.000	0.000	0.000	0.000
Seconds	0.372	0.128	0.103	1.390

Cell count and net charge transfer across all four. Cell shape does not. A Potts cell is an irregular polygon at eccentricity 0.4 and a PhysiCell agent is a disc at 0.04, so the orientational quantities in the PhysiCell column describe its rasterisation.

Surface term across engines

blueprints/monolayer-surface.json adds lambda_surface 0.1 at a target of 34 bonds, and the three lattice engines round their cells by the same amount.

Quantity	CompuCell3D	glazier CPU	glazier GPU
Mean eccentricity, no surface term	0.402	0.394	0.394
Mean eccentricity, with it	0.378	0.371	0.359
Area rms from target	1.59	1.61	1.72
Disclinations	38	34	44

The global nematic order over 256 cells fluctuates by roughly 1/sqrt(N), so that column varies most between runs. Eccentricity and defect count are the steady ones.

Three dimensions

A description sets depth. A depth of one is a plane. The lattice, the neighbourhoods, the field solver, the moments and both engines all take the third axis. At depth one the arithmetic reduces to the two-dimensional case, since every offset with a nonzero z falls outside the neighbourhood and the third variance is zero.

Neighbour orders on a cubic lattice are the six faces, the eighteen faces and edges, and all twenty-six. In a plane, orders two and three are both the eight Moore neighbours, the usual meaning in a two-dimensional model.

Two effects come from the dimension itself. Explicit diffusion is stable to D dt / dx^2 of a quarter in a plane and a sixth in a volume, so the same diffusion constant takes half again as many sub-steps. The device checkerboard likewise needs eight colours where a plane needs four, since same-colour targets have to stay two apart on every axis.

blueprints/volume.json, a 64 by 64 by 32 lattice of 256 cells of 512 sites with a secreted field, runs 50 steps in 1.60 s on the serial engine and 0.092 s on the device. blueprints/infection-volume.json puts the whole stack in a slab: an epithelium shedding virus with a death rate, motile immune cells taking it up and following its gradient, 60 steps in 1.09 s against 0.081 s.

Two terms take different parameters in a volume. A drift moves a 512-site cell's centroid by a five-hundredth of a site per accepted copy, where a 64-site cell in a plane moves by a sixty-fourth. A site in a volume also has eighteen neighbours where one in a plane has eight, so the geometric mean the memory reads drops to zero far more readily: at max_activity 20, which moves a cell three times as far in a plane, a cell in a volume does not move at all, and it takes 100 before it does. Both are recorded in glazier-tests/tests/volume_terms.rs.

Device coverage

The device runs every term the serial engine does: contact, volume, surface and length, the field solver, chemotaxis, division and death. The engines agree on the tissue they produce, though never bit for bit, since the sweeps are different chains, and glazier-tests/tests/ records each agreement as a measured band.

The field solver is one thread per site over a double buffer, sub-stepped from the stated diffusion constant like the serial one, and the totals land within two percent of it on a secreting sheet.

Division and death need a decision per cell over a set of sites scattered across the lattice. The reductions and the relabelling run on the device: a circular-mean pass for the centroid, which is single-valued whatever the wrap so a cell straddling the edge still has one, a second pass for the moments against it, and a pass that cuts or clears. The decisions cross to the host, where one entry per cell is thousands of numbers instead of millions. Both counters are rebuilt from the lattice afterwards, since either event changes every neighbour's boundary.

The length term prices a copy against a cell's major axis, which no neighbourhood around the copy can see. The device keeps ten running sums per cell in a frame each cell's own centroid sets, rebuilt at the top of every step, and reads the axis from the largest eigenvalue of the three by three in closed form. Rebuilding every step bounds the f32 accumulation to one step of rounding instead of a whole run's.

On blueprints/infection-512.json, 4096 cells with two fields and a death rate, 200 steps take 9.72 s on the serial engine and 1.66 s on the device, and they finish within half a percent of each other on cell count. The margin is narrower than the bare sweep's because the field solve sub-steps and the population step synchronises once a step.

Connectivity

A cell that a copy would pinch in two refuses that copy, on either engine. The test is local. It reads the neighbourhood of the site being taken and asks whether the sites there belonging to the losing cell fall into one piece. CompuCell3D walks the cell's whole site graph instead, which is serial by construction, so the engines refuse different sets of copies while both keep cells whole, as the description asks.

The condition is sufficient and not necessary. It stops every local pinch, and a cell can still separate through a sequence of moves that is nowhere locally disconnecting. On the device the neighbourhood is at most twenty-six positions, so the test is a bitmask and a bit-wise search, with no array and no local memory.

blueprints/monolayer-connected.json is the worked case: a sheet at a temperature and a contact energy that tear cells apart when nothing stops them. Over 200 steps the unconstrained sheet fragments and the constrained one holds at one piece per cell, on both engines.

The three lattice engines agree there on cell count, mean area, the spread about target and a net disclination charge of zero, and they part company on shape. Mean eccentricity reads 0.512 under CompuCell3D's global rule against 0.404 and 0.405 under the local one. Refusing a different set of copies makes a different tissue, and that is one place where two engines realise one stated intent differently.

Polarity

Two ways for a cell to move. Both are priced along the move rather than as terms in a total energy.

Memory. Every site remembers how recently it was taken, and a copy is priced against the difference in that memory between the two sites it runs between. A cell that extends in one direction leaves a trail of recent sites behind its front, and the energy difference favours extending the same way over turning, so the cell polarises and travels. The neighbourhood average is a geometric mean, which is zero as soon as one neighbour has forgotten, so the memory acts as a front. This is the Act model of Niculescu, Textor and de Boer.

Over 400 steps a cell at max_activity 20 and lambda_activity 60 travels more than three times as far as the same cell without it, on either engine, and stays at its target volume.

Drift. A force per axis, whose work is the displacement along it. A cell with one travels that way whatever its neighbours do, and it needs "connected": true to stay whole while being dragged.

Both take their parameters from the volume constraint. The memory bonus is bounded by lambda_activity, so a value far past lambda_volume inflates the cell instead of moving it, and a cell driven hard enough can wrap medium into a ring that the connectivity veto then locks. blueprints/immune-motile.json gives a regime that works.

Adhesion molecules

A description can name adhesion molecules, say how much of each a type presents, and give a binding matrix over them, instead of writing out an energy per pair of types. What two types' molecules bind comes off the contact energy between them, and since that is a function of the two types alone, it folds into the contact matrix before an engine ever sees it. The test runs a molecule description and the matrix it folds to on the same seed, and both reach the same lattice.

blueprints/adhesion.json has an epithelial type presenting cadherin, a mesenchymal one presenting less cadherin and some integrin, and a two by two binding matrix. The contact matrix the engine reads comes out at 6.0 between epithelial cells, 9.5 across the pair and 9.1 between mesenchymal ones, from a stated 12.0 throughout.

Per-cell adhesion stays out, where two cells of one type present different amounts and the amounts change as the cell runs. That is state per cell rather than per type, and it moves during a run, so it belongs to a trajectory instead of a description.

Not implemented

• Focal point plasticity: explicit links between cell pairs, with their own creation and breaking rules. It is a mutable list per cell, which neither a declarative description nor a parallel sweep accommodates well.

Harness

python/glazier reads a description, drives CompuCell3D and PhysiCell through their own interfaces, and measures whatever any engine produces on one lattice.

Module	Contents
blueprint.py	the description, read the way the Rust engine reads it
cc3d_backend.py	CompuCell3D backend
physicell_backend.py	PhysiCell backend, and the rasterisation that makes it comparable
director.py	per-cell ellipse, Q tensor, director, nematic order, disclination charge
render.py	a label field as a two-channel fluorescence image at microscopy resolution
compare.py	angle residuals mod pi, and the axis-convention test

bench/roundtrip.py sends a simulated tissue through mermin and scores what it recovers against the director the model was given, at 0.34 degrees median over 121 cells.

Figures

pixi run figures regenerates every image above from runs it makes itself, so every figure here comes from a run in this repository. The sweep at the top end takes minutes and its table is cached under runs/figures.

Licence

MIT or Apache-2.0, at your option.