The dynamics of gene regulatory networks induced by Notch activation

细胞通过分子信号通路相互沟通并接收环境信息。其中一个信号通路涉及Notch受体。这种途径是高度保守的，对构建多细胞动物的许多方面都很重要，并且在包括癌症在内的几种人类疾病中被破坏。如果我们要了解Notch是如何改变细胞行为的，我们需要了解它的活性是如何改变细胞中基因的表达的。新技术，如DNA微阵列，使我们能够同时分析动物基因组编码的所有基因。因此，我们可以问，当细胞接收到Notch信号时，有多少基因被打开或关闭。通过观察Notch激活后的不同时间，我们可以进一步发现不同基因被激活的速度和顺序。我们建议用果蝇的细胞作为我们的模型来研究这些问题。这种动物不仅是Notch最早被发现的动物（基因活动的轻微缺陷导致苍蝇翅膀上的“缺口”），而且它的基因组比哺乳动物更小、更简单（例如，人类有多个Notch样受体，而果蝇只有一个）。然而，即使使用果蝇，我们的实验也会产生一组非常复杂的数据，为了全面分析和理解在不同时间被激活的基因之间的关系，我们需要使用数学方法。事实上，我们打算超越这一点，利用我们的数据建立Notch激活基因反应网络的数学模型。我们的希望是，随着模型变得更加复杂和精确，我们将能够从基本原理上预测这个生物系统的行为。因此，我们希望能够在计算机中模拟如果系统有缺陷（例如基因突变）或用特定药物治疗会发生什么。我们将能够通过实验验证这些预测，看看我们的模型有多准确，并改进它们。这种方法需要生物学家和数学家合作，汇集他们的专业知识，正如我们在这里提出的。因此，我们的长期目标是通过建立预测数学模型来了解Notch信号；通过分析我们在本研究中产生的数据来构建模型，这些模型随后可以用于进一步的研究，药物设计和其他信号通路的应用。

Cells communicate with each other and receive information about their environments through molecular signalling pathways. One such signalling pathway involves the Notch receptor. This pathway is highly conserved, is important for many aspects of building multicellular animals and is disrupted in several human diseases, including cancers. If we are to understand how Notch is able to alter the behaviour of cells we need to understand how its activity changes the expression of genes in the cell. New technologies, such as DNA microarrays, allow us to simultaneously analyse all of the genes encoded by an animal's genome. Thus we can ask how many genes are switched on or off when a cell receives the Notch signal. By looking at different times after Notch activation we can further find out how quickly and in what order different genes are switched on. We propose investigating these questions using cells from the fruit-fly Drosophila as our model. Not only was this the animal where Notch was first discovered (a slight defect in gene activity causes a 'notch' in the fly wing), but also it has a smaller and more simple genome than mammals (for example humans have multiple Notch-like receptors whereas Drosophila has one). However, even using Drosophila, our experiments will produce a very complicated set of data and to fully analyse and understand the relationship between the genes that are turned on at different times we will need to use mathematical approaches. Indeed we propose to go beyond this and use our data to build mathematical models of the network of gene responses to Notch activation. The hope is that, as the models become more sophisticated and accurate, we will be able to predict, from first principal, the behaviour of this biological system. Thus we hope to be able to simulate in the computer what would happen if the system is defective (for example if a gene is mutant) or if it is treated with a particular drug. We will be able to verify these predictions through experiments, to see how accurate our models are, and to improve them. This approach requires that biologists and mathematicians collaborate to bring together their expertise as we propose here. Our long term goal therefore is to develop an understanding of Notch signalling by building predictive mathematical models; models that will be constructed by analysing the data we generate in this study, and that can subsequently be useful for further studies, for drug design and for application to other signalling pathways.