In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge while also making testable predictions that guide experimental work is of utmost importance. To model the dynamics of the networks underlying complex diseases we use network-based models with discrete dynamics, which have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial nature of the interactions and qualitative information on the desired/undesired states.

Here I present some recently developed analytical and computational methods for analyzing network-based models with discrete dynamics. The methods presented are based on a type of function-dependent subnetwork that stabilizes in a steady state regardless of the state of the rest of the network, and which we termed stable motif. Based on the concept of stable motif, we proposed a control method that identifies targets whose manipulation ensures the convergence of the model towards a dynamical attractor of interest (which are identifiable with the cell fates and cell behaviors of modeled organisms). We illustrate the potential of these methods by collaborating with wet-lab cancer biologists to construct and analyze a model for a process involved in the spread of cancer cells (epithelial-mesenchymal transition). These methods allowed us to identify the subnetworks responsible for the disease and the healthy cell states, and show that stabilizing the activity of a few select components can drive the cell towards a desired fate or away from an undesired fate, the validity of which is supported by experimental work.