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      Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development

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          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.

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          Most cited references54

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          p53 dynamics control cell fate.

          Cells transmit information through molecular signals that often show complex dynamical patterns. The dynamic behavior of the tumor suppressor p53 varies depending on the stimulus; in response to double-strand DNA breaks, it shows a series of repeated pulses. Using a computational model, we identified a sequence of precisely timed drug additions that alter p53 pulses to instead produce a sustained p53 response. This leads to the expression of a different set of downstream genes and also alters cell fate: Cells that experience p53 pulses recover from DNA damage, whereas cells exposed to sustained p53 signaling frequently undergo senescence. Our results show that protein dynamics can be an important part of a signal, directly influencing cellular fate decisions.
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            Bifurcation dynamics in lineage-commitment in bipotent progenitor cells.

            Lineage specification of multipotent progenitor cells is governed by a balance of lineage-affiliated transcription factors, such as GATA1 and PU.1, which regulate the choice between erythroid and myelomonocytic fates. But how ratios of lineage-determining transcription factors stabilize progenitor cells and resolve their indeterminacy to commit them to discrete, mutually exclusive fates remains unexplained. We used a simple model and experimental measurements to analyze the dynamics of a binary fate decision governed by a gene-circuit containing auto-stimulation and cross-inhibition, as embodied by the GATA1-PU.1 paradigm. This circuit generates stable attractors corresponding to erythroid and myelomonocytic fates, as well as an uncommitted metastable state characterized by coexpression of both regulators, explaining the phenomenon of "multilineage priming". GATA1 and PU.1 mRNA and transcriptome dynamics of differentiating progenitor cells confirm that commitment occurs in two stages, as suggested by the model: first, the progenitor state is destabilized in an almost symmetrical bifurcation event, resulting in a poised state at the boundary between the two lineage-specific attractors; second, the cell is driven to the respective, now accessible attractors. This minimal model captures fundamental features of binary cell fate decisions, uniting the concepts of stochastic (selective) and deterministic (instructive) regulation, and hence, may apply to a wider range of binary fate decision points.
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              The Yeast Cell-Cycle Network Is Robustly Designed

              , , (2003)
              The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the cell-cycle regulatory network of the budding yeast. With the use of a simple dynamical model, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state--the G1 state--is a global attractor of the dynamics. The biological pathway--the cell-cycle sequence of protein states--is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.
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                Author and article information

                Contributors
                URI : http://community.frontiersin.org/people/u/79995
                URI : http://community.frontiersin.org/people/u/186638
                URI : http://community.frontiersin.org/people/u/22585
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                23 April 2015
                2015
                : 6
                : 160
                Affiliations
                [1] 1Departamento de Ecología Funcional, Instituto de Ecología, Universidad Nacional Autónoma de México Mexico City, Mexico
                [2] 2Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México Mexico City, Mexico
                [3] 3Departamento de Control Automático, Cinvestav-Instituto Politécnico Nacional Mexico City, Mexico
                Author notes

                Edited by: Moisés Santillán, Centro de Investigación y Estudios Avanzados del IPN, Mexico

                Reviewed by: David McMillen, University of Toronto Mississauga, Canada; Enrique Hernandez-Lemus, National Institute of Genomic Medicine, Mexico; Edgardo Ugalde, Universidad Autónoma de San Luis Potosí, Mexico

                *Correspondence: Elena R. Alvarez-Buylla, Instituto de Ecología, Universidad Nacional Autónoma de México, 3er Circuito Exterior, Junto a Jardín Botánico, Mexico City, D.F. 04510, México eabuylla@ 123456gmail.com

                This article was submitted to Systems Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2015.00160
                4407578
                25954305
                3f960c7f-13a1-4c2b-a81d-1d0cc3ed6176
                Copyright © 2015 Davila-Velderrain, Martinez-Garcia and Alvarez-Buylla.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 March 2015
                : 08 April 2015
                Page count
                Figures: 4, Tables: 0, Equations: 15, References: 97, Pages: 14, Words: 11399
                Categories
                Physiology
                Review

                Genetics
                grn,epigenetic landscape,attractors,cell-fate,morphogenesis,stem-cells,cancer
                Genetics
                grn, epigenetic landscape, attractors, cell-fate, morphogenesis, stem-cells, cancer

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