Abstract
Robust temporal and spatial patterns of cell types emerge in the course of normal development in multicellular organisms. The onset of degenerative diseases may result from altered cell fate decisions that give rise to pathological phenotypes. Complex networks of genetic and non-genetic components underlie such normal and altered morphogenetic patterns. Here we focus on the networks of regulatory interactions involved in cell-fate decisions. Such networks modeled as dynamical non-linear systems attain particular stable configurations on gene activity that have been interpreted as cell-fate states. The network structure also restricts the most probable transition patterns among such states. The so-called Epigenetic Landscape (EL), originally proposed by C. H. Waddington, was an early attempt to conceptually explain the emergence of developmental choices as the result of intrinsic constraints (regulatory interactions) shaped during evolution. Thanks to the wealth of molecular genetic and genomic studies, we are now able to postulate gene regulatory networks (GRN) grounded on experimental data, and to derive EL models for specific cases. This, in turn, has motivated several mathematical and computational modeling approaches inspired by the EL concept, that may be useful tools to understand and predict cell-fate decisions and emerging patterns. In order to distinguish between the classical metaphorical EL proposal of Waddington, we refer to the Epigenetic Attractors Landscape (EAL), a proposal that is formally framed in the context of GRNs and dynamical systems theory. In this review we discuss recent EAL modeling strategies, their conceptual basis and their application in studying the emergence of both normal and pathological developmental processes. In addition, we discuss how model predictions can shed light into rational strategies for cell fate regulation, and we point to challenges ahead.
Highlights
The progressive loss of potency from pluripotent stem cells to mature, differentiated cells, as well as the reproducible emergence of spatiotemporal patterns through the course of development has Modeling the epigenetic attractors landscape been always perceived as strong evidence of the robustness and deterministic nature of development
In this review we focused on the second step and presented examples of how steps (3) and (4) can be achieved once a Epigenetic Attractors Landscape (EAL) model is effectively constructed
There are several ways to implement an EAL model starting from a gene regulatory networks (GRN)
Summary
The progressive loss of potency from pluripotent stem cells to mature, differentiated cells, as well as the reproducible emergence of spatiotemporal patterns through the course of development has. The theory posits that attractor states correspond to the observable robust cell phenotypes, cell types, or cellular processes; and that these emerge as a natural consequence of the dynamical constraints imposed by the underlying GRN (Huang and Kauffman, 2009; Huang, 2013). This conventional analysis includes the identification and local characterization of attractor states, and the comparison of these predicted cell-type configurations with the ones that are observed in the corresponding biological system (Figures 1A,B). A dynamics defined by such a mapping is a special form of the map in Equation (1)
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