Reverse engineering the gap gene network of Drosophila melanogaster.
Reverse engineering the gap gene network of Drosophila melanogaster.
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DOI：
10.1371/journal.pcbi.0020051
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发表时间：
2006-05
影响因子：
4.3
通讯作者：
Glass L
中科院分区：
文献类型：
--
作者：
Perkins TJ;Jaeger J;Reinitz J;Glass L
功能基因组学的一个基本问题是基于表达数据确定遗传网络的结构和动力学。我们描述了一种新的策略来解决这个问题，并将其应用到最近发表的数据早期果蝇的发展。我们的方法比目前的拟合方法快了几个数量级，并允许我们拟合不同类型的规则来表达监管关系。具体来说，我们使用我们的方法来拟合模型，使用平滑的非线性形式主义建模基因调控（基因电路），以及使用基于转录因子激活和抑制阈值的逻辑规则的模型。我们的技术还允许我们从头推断监管关系或测试文献建议的网络结构。我们拟合了一系列模型来测试几个关于gap基因调控的突出问题，包括驼背的调控和自激活的作用。基于我们的建模结果和实验文献的验证，我们提出了一个修正的网络结构的差距基因系统。有趣的是，在标准教科书模型的差距基因调控的关系似乎是不必要的，甚至不一致的野生型发展过程中的差距基因表达的细节。对动态系统建模涉及确定系统的哪些元素与哪些元素相互作用，以及相互作用的性质是什么。在建模基因表达动力学的背景下，这个问题等同于确定基因之间的调控关系。Perkins及其同事提出了一种新的计算方法，用于拟合时间序列数据的微分方程模型，并将其应用于著名的果蝇分割网络的表达数据。该方法比其他产生类似质量拟合的方法（如模拟退火）快几个数量级。作者表明，这是可能的，以检测从头相互作用，以及测试现有的监管假设，他们提出了一个修订的网络结构的差距基因系统，基于他们的建模工作和其他实验文献。
A fundamental problem in functional genomics is to determine the structure and dynamics of genetic networks based on expression data. We describe a new strategy for solving this problem and apply it to recently published data on early Drosophila melanogaster development. Our method is orders of magnitude faster than current fitting methods and allows us to fit different types of rules for expressing regulatory relationships. Specifically, we use our approach to fit models using a smooth nonlinear formalism for modeling gene regulation (gene circuits) as well as models using logical rules based on activation and repression thresholds for transcription factors. Our technique also allows us to infer regulatory relationships de novo or to test network structures suggested by the literature. We fit a series of models to test several outstanding questions about gap gene regulation, including regulation of and by hunchback and the role of autoactivation. Based on our modeling results and validation against the experimental literature, we propose a revised network structure for the gap gene system. Interestingly, some relationships in standard textbook models of gap gene regulation appear to be unnecessary for or even inconsistent with the details of gap gene expression during wild-type development. Modeling dynamical systems involves determining which elements of the system interact with which, and what is the nature of the interaction. In the context of modeling gene expression dynamics, this question equates to determining regulatory relationships between genes. Perkins and colleagues present a new computational method for fitting differential equation models of time series data, and apply it to expression data from the well-known segmentation network of Drosophila melanogaster. The method is orders of magnitude faster than other approaches that produce fits of comparable quality, such as Simulated Annealing. The authors show that it is possible to detect interactions de novo as well as to test existing regulatory hypotheses, and they propose a revised network structure for the gap gene system, based on their modeling efforts and on other experimental literature.
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影响因子：
11.4
作者：
HOCH, M;SCHRODER, C;JACKLE, H
通讯作者：
JACKLE, H
影响因子：
56.9
作者：
HOCH, M;GERWIN, N;JACKLE, H
通讯作者：
JACKLE, H
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2.4
作者：
Janssens, H;Kosman, D;Reinitz, J
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Reinitz, J
影响因子：
64.8
作者：
HULSKAMP, M;PFEIFLE, C;TAUTZ, D
通讯作者：
TAUTZ, D
影响因子：
2.4
作者：
Kosman, D;Small, S;Reinitz, J
通讯作者：
Reinitz, J