2015/10/14 by Jose Davila-Velderrain, José Dávila-Velderrain, Davila-Velderrain, Jose +8
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Gene Regulatory Network Analysis #Molecular Networks (q-bio.MN) #Pluripotent Stem Cells Research #Single-cell and spatial transcriptomics #q-bio.MN
paper · pdf · doi:10.48550/arxiv.1510.04230
15 pages, 8 figures
arxiv created 2015/10/14 · openalex publication_date 2015/10/14 · arxiv updated 2015/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Gene regulatory network (GRN) modeling is a well-established theoretical framework for the study of cell-fate specification during developmental processes. Recently, dynamical models of GRNs have been taken as a basis for formalizing the metaphorical model of Waddington's epigenetic landscape, providing a natural extension for the general protocol of GRN modeling. In this contribution we present in a coherent framework a novel implementation of two previously proposed general frameworks for modeling the Epigenetic Attractors Landscape associated with boolean GRNs: the inter-attractor and inter-state transition approaches. We implement novel algorithms for estimating inter-attractor transition probabilities without necessarily depending on intensive single-event simulations. We analyze the performance and sensibility to parameter choices of the algorithms for estimating inter-attractor transition probabilities using three real GRN models. Additionally, we present a side-by-side analysis of downstream analysis tools such as the attractors' temporal and global ordering in the EAL. Overall, we show how the methods complement each other using a real case study: a cellular-level GRN model for epithelial carcinogenesis. We expect the toolkit and comparative analyses put forward here to be a valuable additional re- source for the systems biology community interested in modeling cellular differentiation and reprogramming both in normal and pathological developmental processes.