Towards predictive biology: using stress responses in a bacterial pathogen to link molecular state to phenotype.

生物学中的“圣杯”是从生物的基因组成和基因如何表达的知识中推断出生物在不同条件下的行为(其表型)。这还不可能，但这项提议将推动我们实现这一目标，使用细菌作为模型系统。我们之所以能够做到这一点，有几个原因。首先，我们想要更好地了解致病细菌，以便比目前更好地保护我们自己和我们的食物免受它们的有害影响。其次，我们在工业中经常使用细菌，如果我们能够详细预测它们在工业条件下的表现，我们做到这一点的能力将会提高。第三，随着生物学朝着更加综合的方法发展，通过工程使有机体具有特定的功能，我们需要了解它们如何在不同的条件下生存和茁壮成长。这个项目专注于致病细菌，但我们将开发的方法将适用于许多其他情况。动物，包括人类，对细菌感染有许多屏障，但细菌具有弹性和适应性，可以逃避其中的一些或全部，并继续导致疾病。为了了解它们是如何做到这一点的，我们需要更详细地了解这些生物在我们肠道中存在的条件下的潜在生物学。幸运的是，已经设计出了允许我们做到这一点的新方法，这项提议将应用这些方法。几年来，我们已经能够制造出阻止特定基因发挥作用的突变，并使用携带这些突变的细菌来研究当细菌暴露在压力下时，哪些基因是生存所必需的。我们还知道如何研究特定基因随着外部条件的变化而上调或下调的方式。但现在，有可能将含有数十万种不同突变的大量细菌混合物，将所有这些细菌暴露在许多不同的压力下，并观察每个突变体在每种压力下的生存情况。这可以在几个实验中完成。我们还可以研究细菌中的每一个基因是如何随着时间的推移对压力做出反应的，同样是在几个实验中。此外，我们可以利用这些信息来构建计算机模型，说明细菌中所有对不同压力做出反应的基因是如何联系在一起的。这就像从电话簿上的地址列表到一个城镇的街道和房屋的完整地图。我们使用这种方法构建的第一张地图可能并不完全正确，但我们可以使用实验来详细检查地图，细化每个区域，直到它真正代表细菌细胞内发生的事情。这就是我们在这个项目中要做的。我们将使用构建的模型来预测细菌在不同条件下如何生存，比如在肠道的特定部分，以及哪些基因将在帮助它们做到这一点方面发挥重要作用。作为这个项目的一部分，我们将专门测试我们做出准确预测的能力。最终，这应该有助于我们预测任何病原体的脆弱性，并利用这一知识设计新的策略，以保护我们免受其潜在的致命影响。

A "Holy Grail" in biology is to deduce how an organism will behave under different conditions (its phenotype) from knowledge of its genetic make-up and how its genes are expressed. This is not yet possible, but this proposal will move us towards this goal, using bacteria as a model system. There are several reasons why we want to be able to do this. First, we want to understand disease-causing bacteria better, so as to protect both ourselves and our food against their harmful effects better than we can do at the moment. Second, we use bacteria a lot in industry and our ability to do this will improve if we can predict in detail how they will behave under industrial conditions. Third, as biology moves towards a more synthetic approach where organisms are engineered to have specific functions, we need to understand how they will survive and thrive in different conditions. This project focusses on bacteria that cause disease, but the methods that we will develop will be applicable in many other situations. Animals, including humans, have many barriers against bacterial infection, but bacteria are resilient and adaptable and can evade some or all of these, and go on to cause disease. To understand how they are able to do this, we need to understand in much more detail the underlying biology of these organisms under the conditions that exist in our gut. Fortunately, novel methods have been devised that allow us to do this, and this proposal will apply these. For some years, we have been able to make mutations which prevent particular genes from working and use bacteria carrying these mutations to study which genes are needed for survival when bacteria are exposed to stress. We've also known how to study the way in which a particular gene is turned up or down as the external conditions change. But now, it is possible to take a very large mixture of bacteria, containing hundreds of thousands of different mutations, expose all these bacteria to many different stresses, and see how well each mutant survives each stress. This can be done in just a few experiments. We can also study how every single gene in the bacterium is responding to the stress over time, again in a few experiments. Furthermore, we can use this information to construct computer models of how all the genes which respond to the different stresses in the bacteria are connected together. This is like going from a list of addresses in a phone book to a complete map of the streets and houses in a town. The first maps that we construct using this method may not be completely correct, but we can use experiments to check the maps in detail, refining each region until it truly represents what goes on inside the bacterial cell. This is what we will do in this project. We will use the models constructed to make predictions about how bacteria will survive under different conditions, like in a particular part of the gut, and which genes will be important in helping them do this. We will specifically test our ability to make accurate predictions as part of this project. Ultimately, this should help us to predict the vulnerabilities of any pathogenic bacterium, and to use this knowledge to devise novel strategies to protect us from their potentially lethal effects.