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Gene regulatory network (GRN) modeling is a well-established theoreticalframework for the study of cell-fate specification during developmentalprocesses. Recently, dynamical models of GRNs have been taken as a basis forformalizing the metaphorical model of Waddington's epigenetic landscape,providing a natural extension for the general protocol of GRN modeling. In thiscontribution we present in a coherent framework a novel implementation of twopreviously proposed general frameworks for modeling the Epigenetic AttractorsLandscape associated with boolean GRNs: the inter-attractor and inter-statetransition approaches. We implement novel algorithms for estimatinginter-attractor transition probabilities without necessarily depending onintensive single-event simulations. We analyze the performance and sensibilityto parameter choices of the algorithms for estimating inter-attractortransition probabilities using three real GRN models. Additionally, we presenta side-by-side analysis of downstream analysis tools such as the attractors'temporal and global ordering in the EAL. Overall, we show how the methodscomplement each other using a real case study: a cellular-level GRN model forepithelial carcinogenesis. We expect the toolkit and comparative analyses putforward here to be a valuable additional re- source for the systems biologycommunity interested in modeling cellular differentiation and reprogrammingboth in normal and pathological developmental processes.

Methods for Characterizing the Epigenetic Attractors Landscape Associated with Boolean Gene Regulatory Networks