Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development.
Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development.
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DOI：
10.3389/fgene.2015.00160
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发表时间：
2015
影响因子：
3.7
通讯作者：
Alvarez-Buylla ER
中科院分区：
文献类型：
--
作者：
Davila-Velderrain J;Martinez-Garcia JC;Alvarez-Buylla ER
在多细胞生物体的正常发育过程中，细胞类型的时间和空间模式都很稳定。退行性疾病的发作可能是由于改变细胞命运决定而引起的，从而引起病理表型。遗传和非遗传成分的复杂网络构成了这种正常和改变的形态发生模式的基础。在这里，我们专注于参与细胞命运决定的调控相互作用的网络。这种网络建模为动态非线性系统，获得特定的稳定配置的基因活性，已被解释为细胞命运的状态。网络结构也限制了这些状态之间最可能的转换模式。表观遗传景观（Epigenetic Landscape，EL）是由C. H.沃丁顿，是一个早期的尝试，从概念上解释发展选择的出现，作为进化过程中形成的内在约束（调节相互作用）的结果。由于丰富的分子遗传学和基因组学研究，我们现在能够假设基因调控网络（GRN）的实验数据为基础，并推导出EL模型的特定情况下。这反过来又激发了几种受EL概念启发的数学和计算建模方法，这些方法可能是理解和预测细胞命运决定和新兴模式的有用工具。为了区分沃丁顿的经典隐喻EL建议，我们参考了表观遗传吸引景观（EAL），这是一个正式框架的GRN和动力系统理论的背景下的建议。在这篇综述中，我们讨论了最近的EAL建模策略，其概念基础和它们在研究正常和病理性发育过程中的应用。此外，我们还讨论了模型预测如何揭示细胞命运调控的合理策略，并指出了未来的挑战。
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.