Figure 1

An in silico combinatorial knockout screen in the EMT network model reveals specific node combinations that can suppress the TGFβ-driven EMT. A previously constructed network model of EMT was used to identify nodes whose knockout (sustained OFF state) blocked TGFβ-driven EMT. (a) Schematic demonstrating that epithelial and mesenchymal cellular states are stable unless an external signal (e.g., TGFβ) or perturbation is applied. Our goal is to identify inhibitory perturbations that block the transition between the epithelial and mesenchymal states even in the presence of TGFβ. (b) The effect of knocking out nodes individually and in combinations and their effect on the percentage of EMT in 1,000 simulations per knockout combination. The EMT Percentage is given by the percentage of simulations at the end of which the EMT node is found in the ON state. (c) A network control approach was employed to identify epithelial control sets, i.e., set of nodes that when their states are controlled lead to the epithelial steady state. The schematic illustrates the effect of applying the epithelial control set on the network states. (d) The stable motif associated with the epithelial steady state. The nodes and edges that are part of the epithelial stable motif have thick lines, while the nodes and edges in the EMT network that are not part of the epithelial stable motif have dashed lines. The epithelial control sets contain one node from each connected group of nodes highlighted by a yellow background. All nodes of the predicted knockout combinations (blue shadow) are part of the epithelial stable motif, and are contained within an epithelial control set or form a path of nodes directly upstream of an epithelial control set. Black or white background on the node symbol indicates that the node is OFF or ON in the epithelial stable motif, respectively. EMT, Epithelial-to-mesenchymal transition, TGFβ, transforming growth factor beta.