Boolean Pigmentation Network Simulator

265 nodes · 429 typed edges Raghunath et al., BMC Res. Notes (2015) · Boolean dynamics

1 · Set inputs

Turn ON receptor or environmental nodes to simulate a stimulus. UV and growth-factor inputs propagate through the network.

2 · Perturb nodes

Click any node in the graph to choose perturbation. Or search:

3 · Run simulation

Asynchronous update (slower, catches oscillations) Highlight active edges

Display filter

Legend

ON (active)

OFF (inactive)

Knocked out (clamped 0)

Forced ON (clamped 1)

Phenotype readout

Activates

Inhibits

Complex formation

Set inputs and perturbations, then run simulation.

Simulation status

State Not run

Attractor —

Steps to converge —

Phenotype readout

Final state of biological-process nodes. Compare baseline (inputs only, no perturbation) to perturbed.

About this simulation

Update rule: for each node, score = (# active activators) − (# active inhibitors). Score > 0 → ON; < 0 → OFF; = 0 → hold previous. Complex-formation edges count as activating. Knockouts clamp to 0, forced nodes clamp to 1.

Known limitations of this network for this purpose: the Raghunath network is built around UV-induced acute signalling, not constitutive pigmentation. Several phenotype nodes (e.g. Eumelanin) have redundant upstream paths that bypass canonical bottlenecks like TYR — so single knockouts often fail to abolish output even when biology says they should. This is a property of the underlying network, not a simulator bug. The model is appropriate for exploring signalling cross-talk; quantitative phenotype prediction will require the multi-layer extension.