Logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g. stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity.
In [1], it is presented an extended version of a published logical model of T-helper cell differentiation and plasticity, which accounts for novel cellular subtypes. The model encompasses 20 signaling pathways, a dozen of transcription factors, and about 30 cytokines, amounting to 101 components in total.
Computational methods recently developed to efficiently analyze large models [1] are first used to study static properties of the model (i.e. stables states). Symbolic model checking is then applied to get further insights into reachability properties between Th canonical subtypes upon changes of specific prototypic environmental cues.
The model reproduces novel reported Th subtypes (Tfh, Th9, Th22) and predicts additional Th hybrid subtypes in term of stables states. Using the model checker NuSMV-ARCTL, an abstract view of the dynamics, called reprograming graph, is produced providing a global and synthetic view of Th plasticity. The model is consistent with experimental data showing the polarization of naïve Th cells into the canonical Th subtypes. The model further predicts substancial plasticity of Th subtypes depending on the signalling environment.