High throughput analysis of cell growth data from phenotype arrays

50人死于最近的E。德国爆发大肠杆菌疫情。四千人被感染。随着全球人口的不断增长，我们如何确保我们都能获得安全的食物？化石燃料将耗尽，最近的福岛灾难凸显了核能的风险。我们如何提供可持续的燃料来源，以满足我们在人口不仅在增长而且在发展的背景下的能源和运输需求？这些都是重大的挑战，而应对这些挑战的关键策略是对微生物的研究。在E.大肠杆菌疾病是由有害细菌引起的，我们需要了解有害细菌如何在农场，土壤，食品生产，储存和制备设施，以及动物和人类宿主中生存。就燃料而言，微生物为新一代生物燃料提供了机会。生物燃料是碳中性技术，但传统的生物燃料需要类似的材料或土地，否则可以用于粮食。我们现在正在寻求从不能用作食物和目前被浪费的植物物质中开发生物燃料。要做到这一点，我们需要找到新的酵母菌株，可以将这种植物物质转化为燃料。近年来，新技术的发展使我们能够在一天内读取微生物的全基因组序列。这确实是了不起的，但基因组序列是一组指令，用一种我们只能开始理解的语言。真正重要的是微生物在不同环境中的行为：它在什么食物上茁壮成长，在什么食物上挨饿？什么样的毒素能杀死它，什么样的毒素能杀死它？这些问题对于了解我们如何对抗有害的食源性细菌或开发新的生物能源生产剂至关重要。如果我们能将这些答案与基因组序列联系起来，我们就有了一种强大的方法来解码基因的语言。这项提议的重点是一种名为Biolog表型微阵列的技术，它可以精确测量微生物在数千种条件下的繁殖情况，包括不同的食物来源和潜在的毒素。这些阵列生成时间过程，在一个固定的时间点绘制每个条件，在实验过程中测量数百个细胞活性。每一个时间进程都编码了大量的信息：微生物开始活跃需要多长时间？它们生长得有多快？他们是否能够使用一种以上的食物来源，如果是的话，一种比另一种好吗？它们能长多少？值得注意的是，没有任何分析方法可以让Biolog阵列的用户从Biolog输出中获得这些信息：相反，用户通常使用单一数据，如终点或总生长，并丢弃大部分有价值的信息。为此，我们打算建立描述Biolog阵列中细胞活动的数学模型;这些模型需要反映技术的细节以及细胞生长条件的复杂性。我们建议开发自动化的方法来计算出哪种模型最适合任何给定的数据集，并确定描述微生物行为的关键参数。自动化是必不可少的，因为一个单一的实验可以产生2000个微生物的时间过程。这些方法必须能被更广泛的科学界所接受，而不仅仅是数学家，所以我们需要为我们开发的方法开发用户友好的界面，并为Biolog用户提供这些方法的培训。最后，在我们既定的研究计划中，我们已经生成了大量关于有害E.大肠杆菌菌株、微生物土壤污染和开发新的酵母菌株，用于从非食用植物材料生产生物燃料。我们将通过将我们的方法应用于这些数据来直接解决食品安全和生物能源挑战。

Fifty people died as a result of the recent E. coli outbreak in Germany. Four thousand people were infected. With a growing global human population, how do we ensure that we all have access to safe food? Fossil fuels will run out, and the recent Fukushima disaster highlighted the risks of nuclear energy. How do we provide sustainable sources of fuel to meet our energy and transport needs in the context of a population that is not just growing, but also developing?These are major challenges, and a key strategy for coming them is the study of microbes. In the case of E. coli the disease is caused by harmful bacteria, and we need to understand how harmful bacteria survive in farms, soil, food production, storage and preparation facilities, as well as in animal and human hosts. In the case of fuels, microbes provide an opportunity for a new generation of biofuels. Biofuels are carbon neutral technologies, but conventional biofuels need similar materials or land that could otherwise be used for food. We are now seeking to develop biofuels from plant matter that cannot be used for food and is currently wasted. To do this, we need to find new strains of yeast that can convert this plant matter into fuel. In recent years, new technologies have been developed that enable us to read the full genome sequence of a microbe in just a day. This is indeed remarkable, but the genome sequence is a set of instructions in a language that we can only begin to understand. What really matters is how a microbe behaves in different environments: on what foods does it thrive, on what foods does it starve? What potential toxins can it survive and what toxins kill it? These questions are essential for understanding how we can combat harmful food-borne bacteria, or develop new bioenergy producing agents. And if we can link these answers to the genome sequence, we have a powerful way of decoding the language of the genes.This proposal is focussed on a technology, called Biolog Phenotype Microarrays, that precisely measure how well microbes thrive in thousands of conditions, including different food sources and potential toxins. The arrays generate time courses that plot each condition at a regular point in time, with several hundred measurements of cell activity during the course of an experiment. Each time course encodes a wealth of information: how long does it take before the microbes start to become active? How quickly do they grow? Are they able to use more than one food source, and if so, is one better than the other? How much do they grow? Remarkably, there are no analysis methods available that allow users of Biolog arrays to obtain this information from the Biolog output: instead, users typically use a single datum, such as the end-point, or total growth, and discard most of the valuable information.The aim of this proposal is to bridge this gap. To do so, we intend to build mathematical models that describe cell activity in Biolog arrays; these need to reflect the details of the technology, as well as the complexity of the conditions in which the cells are grown. We propose to develop automated ways of working out which model best fits any given set of data, and identify the key parameters describing microbial behaviour. Automation is essential, because a single experiment can generate 2000 microbial time courses. The methods have to be accessible to the wider scientific community, not just mathematicians, so we need to develop user-friendly interfaces to the methods we develop, and provide training for Biolog users in these methods.Finally, in our established research programmes, we have generated vast quantities of Biolog data on survival of harmful E. coli strains, microbial soil contamination and the development of new yeast strains for producing biofuel from non-food plant material. We will directly address the food safety and bioenergy challenges by applying our methods to these data.