Cell-cell communication orchestrates tissue homeostasis and disease progression through dynamic ligand-receptor signaling. Yet no method exists to infer executable multicellular dynamical models from temporal data. We introduce here a Boolean network inference framework that enables the reconstruction of such models. With this approach, multiple distinct cell types are modeled jointly, each represented through its receptor and ligand nodes, with a bipartite dependency structure reflecting the two processes of intercellular communication: receptors respond to ligands secreted by any cell in the system, while ligands encode the intracellular response to a cell's own receptors. The timescale separation between fast receptor activation and slow ligand secretion is formalized as pseudo-steady-state constraints, chaining successive observed states into a dynamical sequence. From this sequence, the framework utilizes BoNesis to exhaustively infer all Boolean networks whose dynamics are consistent with the observations, producing an ensemble that explicitly identifies which regulatory interactions are determined by the inputs and which reflect the intrinsic redundancy of biological signaling. On a controlled two-cell toy model with known ground truth, we show that the inferred ensemble contains the ground-truth network, that the interaction structure is preserved under partial observations, and that receptor states are recoverable even when unobserved. Applied to a 50-node three-cell-type model of the CLL tumor microenvironment, the framework recovers the expected sequential niche establishment order and generates biologically coherent predictions, including for perturbations.

Co-authors: Laurence Calzone, Loïc Paulevé