Aguilar-Hidalgo Daniel, Becerra-Alonso David, García-Morales Diana, Casares Fernando
CABD (Andalusian Centre for Developmental Biology), CSIC-UPO-JA, Campus Universidad Pablo de Olavide, 41013, Seville, Spain.
Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Straße 38, 01187, Dresden, Germany.
Dev Genes Evol. 2016 Jun;226(3):221-33. doi: 10.1007/s00427-016-0541-8. Epub 2016 Apr 1.
The morphology and function of organs depend on coordinated changes in gene expression during development. These changes are controlled by transcription factors, signaling pathways, and their regulatory interactions, which are represented by gene regulatory networks (GRNs). Therefore, the structure of an organ GRN restricts the morphological and functional variations that the organ can experience-its potential morphospace. Therefore, two important questions arise when studying any GRN: what is the predicted available morphospace and what are the regulatory linkages that contribute the most to control morphological variation within this space. Here, we explore these questions by analyzing a small "three-node" GRN model that captures the Hh-driven regulatory interactions controlling a simple visual structure: the ocellar region of Drosophila. Analysis of the model predicts that random variation of model parameters results in a specific non-random distribution of morphological variants. Study of a limited sample of drosophilids and other dipterans finds a correspondence between the predicted phenotypic range and that found in nature. As an alternative to simulations, we apply Bayesian networks methods in order to identify the set of parameters with the largest contribution to morphological variation. Our results predict the potential morphological space of the ocellar complex and identify likely candidate processes to be responsible for ocellar morphological evolution using Bayesian networks. We further discuss the assumptions that the approach we have taken entails and their validity.
器官的形态和功能取决于发育过程中基因表达的协调变化。这些变化由转录因子、信号通路及其调控相互作用控制，这些相互作用由基因调控网络（GRN）表示。因此，器官GRN的结构限制了器官可能经历的形态和功能变化——其潜在的形态空间。因此，在研究任何GRN时会出现两个重要问题：预测的可用形态空间是什么，以及在这个空间内对控制形态变化贡献最大的调控联系是什么。在这里，我们通过分析一个小型“三节点”GRN模型来探索这些问题，该模型捕捉了由Hh驱动的调控相互作用，控制着一个简单的视觉结构：果蝇的单眼区域。对该模型的分析预测，模型参数的随机变化会导致形态变体的特定非随机分布。对有限样本的果蝇和其他双翅目昆虫的研究发现，预测的表型范围与自然界中发现的表型范围相对应。作为模拟的替代方法，我们应用贝叶斯网络方法来识别对形态变化贡献最大的参数集。我们的结果预测了单眼复合体的潜在形态空间，并使用贝叶斯网络确定了可能负责单眼形态进化的候选过程。我们进一步讨论了我们所采用方法所涉及的假设及其有效性。
