Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration.
Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration.
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DOI：
10.1371/journal.pcbi.1004295
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发表时间：
2015-06
影响因子：
4.3
通讯作者：
Levin M
中科院分区：
文献类型：
--
作者：
Lobo D;Levin M
生物医学中的变革性应用需要发现复杂的调控网络，以解释解剖结构的发育和再生，并揭示哪些外部信号将触发大规模模式的预期变化。尽管最近在生物信息学方面取得了进展，但从实验形态学数据中提取机制途径模型是一个关键的开放性挑战，它抵制了自动化。人工预测即使是简单网络的涌现行为的根本困难限制了人类科学家发明的模型，这些模型显示了必要的亚基相互作用，但没有揭示足以使复杂的自我调节模式出现的动力学。为了最终弥合高分辨率遗传数据与理解和控制模式的能力之间的差距，开发计算工具以有效地从所得实验形状表型中提取调控途径至关重要。例如，对真涡虫再生的研究已经超过了一个世纪，但尽管对控制其干细胞的途径有了越来越多的了解，但人类科学家还没有发现任何建设性的机制模型来解释其在剧烈扰动后再生正确解剖模式的非凡能力的一两个关键特征。我们提出了一种方法来推断的分子产物，拓扑结构，和空间和时间的非线性动态的调控网络重演在硅片上的形态表型的遗传，手术和药理学实验产生的丰富的数据集。我们证明了我们的方法，通过推断完整的调节网络解释的主要功能再生实验的结果在涡虫文献;通过分析所有的数据集在一起，我们的系统推断出第一个系统-生物学综合动力学模型解释模式在涡虫再生。这种方法提供了一个自动化的，高度概括的框架，用于识别负责生长和形式的动态调节的基本控制机制。发育和再生生物学实验正在从功能扰动实验中产生大量的形态表型。然而，现有的通路模型一般不解释解剖形状的动态调节，由于推断和测试负责适当的形式，形状和模式的非线性调节网络的困难。我们提出了一种方法，自动化的发现和测试的监管网络解释形态结果直接从所得的表型，生产网络模型作为可测试的假设解释再生数据。我们的系统集成了一个正式的出版结果，在涡虫再生，在硅片模拟器中，调控网络的图案化特性可以在再生试验中进行定量测试，和一个机器学习模块，进化网络，其行为在此试验中最佳匹配的数据库的涡虫结果。我们应用我们的方法来解释的关键实验，在涡虫再生，并发现了第一个全面的模型的前-后模式的涡虫在手术，药理学和遗传操作。除了真涡虫的数据，我们的方法很容易推广，以促进可测试的监管网络在发育生物学和生物医学的发现，并代表了第一个发展模式发现从头形态的结果由自动化系统。
Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form. Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments. However, existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form, shape, and pattern. We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes, producing network models as testable hypotheses explaining regeneration data. Our system integrates a formalization of the published results in planarian regeneration, an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay, and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results. We applied our method to explain the key experiments in planarian regeneration, and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical, pharmacological, and genetic manipulations. Beyond the planarian data, our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine, and represents the first developmental model discovered de novo from morphological outcomes by an automated system.
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