High throughput analysis of cell growth data from phenotype arrays

德国最近爆发的大肠杆菌疫情导致50人死亡。4000人被感染。随着全球人口不断增长，我们如何确保我们所有人都能获得安全食品？化石燃料将会耗尽，最近的福岛灾难突显了核能的风险。在人口不仅在增长，而且在发展的背景下，我们如何提供可持续的燃料来源，以满足我们的能源和交通需求？这些都是重大挑战，应对这些挑战的一个关键战略是对微生物的研究。就大肠杆菌而言，这种疾病是由有害细菌引起的，我们需要了解有害细菌是如何在农场、土壤、食品生产、储存和准备设施以及动物和人类宿主中生存的。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于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，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完整基因组序列。这确实是了不起的，但基因组序列是一套我们才能开始理解的语言的指令。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，什么食物让它挨饿？它能存活下来的潜在毒素是什么？是什么毒素杀死了它？这些问题对于理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破译基因语言的方法。这项提议集中在一项名为Biolog表型微阵列的技术上，该技术可以精确测量微生物在数千种条件下的生长情况，包括不同的食物来源和潜在的毒素。这些阵列生成时间进程，在固定的时间点绘制每种情况的曲线图，并在实验过程中对细胞活动进行数百次测量。每个时间进程都编码了丰富的信息：微生物需要多长时间才能开始活跃？它们长得有多快？它们能够使用不止一种食物来源吗？如果是，其中一种比另一种更好吗？他们种了多少？值得注意的是，没有可用的分析方法允许Biolog阵列的用户从Biolog输出中获得这些信息：相反，用户通常使用单个数据，如终点或总增长，并丢弃大多数有价值的信息。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。我们建议开发自动方法来计算出哪个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，因为一次实验可以产生2000个微生物时间进程。这些方法必须为更广泛的科学界所使用，而不仅仅是数学家，因此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，我们生成了大量关于有害大肠杆菌菌株的生存、微生物土壤污染以及开发用于从非食用植物材料生产生物燃料的新酵母菌株的Biolog数据。通过将我们的方法应用于这些数据，我们将直接解决食品安全和生物能源方面的挑战。

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.