Simulating heterogeneous populations using Boolean models

Certain biological processes such as cancer development and immune activation are controlled by rare cellular events that are difficult to capture computationally through simulations of individual cells. Here we show that when cellular states are described using a Boolean network model, one can exactly simulate the dynamics of non-interacting, highly heterogeneous populations directly, without having to model the various subpopulations. This strategy captures even the rarest outcomes of the model with no sampling error. Our method can incorporate heterogeneity in both cell state and, by augmenting the model, the underlying rules of the network as well (i.e. mutations). We demonstrate our method by using it to simulate a heterogeneous population of Boolean networks modeling the T-cell receptor, spanning ~ 1020 distinct cellular states and mutational profiles.