Efficient reverse-engineering of a developmental gene regulatory network.
Efficient reverse-engineering of a developmental gene regulatory network.
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DOI：
10.1371/journal.pcbi.1002589
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发表时间：
2012
影响因子：
4.3
通讯作者：
Jaeger J
中科院分区：
文献类型：
--
作者：
Crombach A;Wotton KR;Cicin-Sain D;Ashyraliyev M;Jaeger J
了解多细胞生物体发育和进化背后的复杂调控网络是生物学中的一个主要问题。计算模型可以用作从基因表达数据中提取这种网络的调控结构和动力学的工具。这种方法被称为逆向工程。它已经成功地应用于各种生物系统中的许多基因网络。然而，在其空间背景下重建发育基因网络的结构和非线性动力学仍然是一个相当大的挑战。在这里，我们解决这一挑战，使用一个案例研究：差距基因网络参与部分决定在早期发展的果蝇。逆向工程模式形成网络的一个主要问题是获取和量化空间基因表达数据所需的大量时间和精力。我们已经开发了一个简化的数据处理管道，大大提高了该方法的吞吐量，但结果在数据的准确性降低相比，以前用于间隙基因网络推理。我们证明，我们可以推断出正确的网络结构，使用我们减少的数据集，并调查成功的逆向工程的最低数据要求。我们的研究结果表明，表达域边界的时间和位置是从数据中确定调控网络结构的关键特征，而精确测量表达水平则不那么重要。在此基础上，我们定义了间隙基因网络推理的最小数据要求。我们的研究结果证明了逆向工程的可行性，大大减少了实验工作。这使得该方法能够在不同的发育背景和生物体中更广泛地使用。将数据驱动模型系统地应用于现实世界的网络具有巨大的潜力。只有对大量发育基因调控网络进行定量研究，才能让我们发现复杂多细胞生物体的发育和进化是否存在规则或规律。为了更好地了解多细胞生物，我们需要更好、更系统地了解控制其发育和进化的复杂调控网络。然而，这个问题远非微不足道。调节网络涉及许多因素以非线性方式相互作用，这使得没有计算机的帮助很难研究它们。在这里，我们研究了一种计算方法，逆向工程，它使我们能够在硅片上重建真实世界的监管网络。作为一个案例研究，我们研究了果蝇早期发育过程中参与决定体节位置的差距基因网络。我们使用原位杂交和显微镜观察空间间隙基因表达模式。对所得胚胎图像进行定量以测量表达域边界的位置。然后，我们使用计算模型作为工具，从数据中提取监管信息。我们研究什么样的，以及需要多少数据成功的网络推理。我们的研究结果表明，逆向工程网络所需的工作量比以前认为的要少得多。这开启了使用这种方法研究大量发育网络的可能性，这反过来将导致对动物和植物发育的基本规则和原则的更普遍的理解。
Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms. To better understand multi-cellular organisms we need a better and more systematic understanding of the complex regulatory networks that govern their development and evolution. However, this problem is far from trivial. Regulatory networks involve many factors interacting in a non-linear manner, which makes it difficult to study them without the help of computers. Here, we investigate a computational method, reverse engineering, which allows us to reconstitute real-world regulatory networks in silico. As a case study, we investigate the gap gene network involved in determining the position of body segments during early development of Drosophila. We visualise spatial gap gene expression patterns using in situ hybridisation and microscopy. The resulting embryo images are quantified to measure the position of expression domain boundaries. We then use computational models as tools to extract regulatory information from the data. We investigate what kind, and how much data are required for successful network inference. Our results reveal that much less effort is required for reverse-engineering networks than previously thought. This opens the possibility of investigating a large number of developmental networks using this approach, which in turn will lead to a more general understanding of the rules and principles underlying development in animals and plants.
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影响因子：
64.5
作者：
Bonneau, Richard;Facciotti, Marc T.;Baliga, Nitin S.
通讯作者：
Baliga, Nitin S.
影响因子：
64.8
作者：
Clyde, DE;Corado, MSG;Small, S
通讯作者：
Small, S
影响因子：
--
作者：
Dewar, Michael A.;Kadirkamanathan, Visakan;Sanguinetti, Guido
通讯作者：
Sanguinetti, Guido
影响因子：
--
作者：
Ashyraliyev, Maksat;Jaeger, Johannes;Blom, Joke G.
通讯作者：
Blom, Joke G.
DOI：
10.1073/pnas.220392197
发表时间：
2000-10-24
影响因子：
11.1
作者：
Butte, AJ;Tamayo, P;Kohane, IS
通讯作者：
Kohane, IS