FIGURE 1.

MC-Boomer workflow. The MC-Boomer workflow consists of three steps. The first is to gather data of the steady state expression levels of the genes of interest. Additional prior knowledge about known relationships between genes can guide and constrain the second step, model generation. We use Monte Carlo Tree Search to generate models (Section 3.3; Figure 3). The objective of search is to find models that have simulated attractor states that are similar to data, as measured by an edit distance, described in Section 3.2. We test this algorithm’s ability to recover random models in Section 3.4. We further apply the method to a more biologically realistic model: the Drosophila segment polarity network, described in Section 4.1. We analyze the generated segment polarity models further in Sections 4.2 to extract structural features.