Bacteriophage and Antibiotic Resistance: a Mathematical and Imaging Approach

人们普遍认为，医学科学正面临一个严重影响人类未来健康的问题。由于抗药性细菌的进化和传播，以及合成新抗生素产品的难度增加，我们需要找到治疗细菌感染的新方法。随着我们开始设计利用工程细菌及其病毒噬菌体的合成疗法，我们需要更好地了解如何使用我们所拥有的抗菌剂。定位“最佳抗生素治疗”可能是一个遥远的目标，但研究人员最近已经开始考虑将抗生素结合起来的新方法，以最大限度地减少抗生素耐药性的演变。这就是提案的重点：韦戈如何超越药代动力学有效性的衡量标准，为最佳治疗找到新的理由？解决这个问题的方法必须涵盖不同的领域。我们需要系统生物学的工具来告诉我们如何对单个细胞内复杂过程的行为进行建模，但我们也需要描述抗生素如何抑制这些细胞过程并导致细菌死亡的模型：抗生素的系统生物学。为了检验理论，我们需要经验性的工作，因为如果我们声称不同抗生素的组合可以制成一种有效的鸡尾酒，那么我们应该在实验室测试这一说法的准确性。提案中解决的这类研究问题的实验范式是“实验微生物系统”，即可以在体外创造的进化的微观世界，并观察和重复它们的进化。事实上，抗生素耐药性的演变可以如此之快，以至于可以在持续几天的实验中观察到。这一经验性工具的效用在于假设可以被快速检验，我们很快就会看到在理论上创造的想法在实践中是否具有任何有效性。但我们如何得出这样的理论预测？通过建立实验系统的数学模型，并要求一种形式的“可控性”。也就是说，我们首先问是否可以在数学模型内实现特定的结果。例如，这一结果可能意味着使用抗生素将细菌从宿主中移除，方法是将细菌的密度降至最低，同时防止该细菌产生抗生素耐药性；我们声称，这种问题非常适合系统和控制方法。尽管基因组技术进步非常迅速，但生物系统很难建模，数据可能稀少，因此我们需要努力控制它们。然而，我们提出的工作的一个基本特征是一般性原则，这可能有助于超越数据。这一想法是一种常见的数学技术，旨在寻找将不同系统识别为具有相同结构的原理，这些结构可以使用数学工具抽象地处理。例如，在治疗大肠杆菌或假单胞菌感染时，最好的抗生素鸡尾酒有什么共同的原则吗？及时循环使用不同抗生素的治疗方法总是比将抗生素混合成单一鸡尾酒的治疗方法更好吗？细胞内特定的抗生素蛋白靶点重要吗？数学可以帮助阐明像这样的一般问题。由于这些问题中的一些是困难的和雄心勃勃的，因此提出了更可行的目标。例如，我们是否可以使用成像技术来观察细菌菌落在不同抗生素介质中的生长，并预测和测量不同鸡尾酒的效力？这种实验本身是新颖的，将为工作的更多理论部分提供基础。简而言之，结合数学、生物学和物理学的工具，我们的目标是了解简单系统中最佳的抗生素治疗方法，并了解这些治疗方法是否对更复杂的生物系统仍然是最佳的。

There is general agreement that medical science is facing a problem of grave importance with implications for the future of human health.Due to the evolution and spread of antibiotic resistant bacteria and the increasing difficulty of synthesising new antibiotic products,we need to find new ways of treating bacterial infections. As we embark upon the design of synthetictherapies that exploit engineered bacteria and their viral bacteriophages, we need to better understand how to use the antimicrobial agents in our possession.Locating 'the optimal antibiotic treatment' may be a distant goal, but researchers have recently begun to consider new ways in whichantibiotics should be combined to minimise the evolution of resistance to antibiotics. This is the focus of the proposal: how do wego beyond pharmacokinetic measures of efficacy to find new rationales for the optimal treatment?An approach to this question must encompass different fields. We need tools from systems biology thattell us how to model the behaviour of the complex processes within a single cell, but we also need modelsdescribing how antibiotics inhibit those cellular processes and lead to death in bacteria: the systems biology of antibiotics. To test theory we need empirical work, for if we claim that a combination of different antibiotics makes a potent cocktail, we should then test the veracity of this claim in the lab.The experimental paradigm for the type of research questions tackled in the proposal are 'experimental microbial systems', evolving microcosms that can be created in vitro and their evolution observed and repeated. Indeed, the evolution of antibiotic resistance can be so rapid that it may be observed in experiments lasting a handful of days. The utility of this empirical device is the rapidity with which hypotheses can be tested, we will soon see whether ideas created in theory have any validity in practise.But how do we derive such theoretical predictions? By taking mathematical models of experimental systems and asking fora form of 'controllability'. That is, we first ask whether a particular outcome can be achieved within the mathematical model. This outcome might mean, for example, using antibiotics to removal a bacterium from its host by minimising its density while, at the same time, preventing that bacterium from evolving antibiotic resistance; we claim that this kind of problem fits nicely into a systems and control approach.Despite very rapid advances in genomic technologies, biological systems are notoriously hard to model and data can be sparse so we will need to work hard to control them. However, a fundemental feature of the work we propose is the principle of generality that may help see beyond data. The idea, a common mathematical technique, is to look for principles that identify different systems as having identical structures that can be dealt with abstractly using mathematical tools. For example, are there any principles common to the best antibiotic cocktails when treating both E.coli or Pseudomonas infections? Are treatments that cycle different antibiotics in time always better than ones that mix antibiotics into a single cocktail? Is the particular antibiotic protein target within the cell important? Mathematics can help elucidate general problems like these.As some of these problems are difficult and ambitious, more feasible goals are presented. For example, can we use imaging to watch bacterial colonies grow in different antibiotic media and predict and measure the potency of different cocktails? This kind of experiment is novel in itself and will provide a foundation for more theoretical parts of the work.In short, with a combination of tools from mathematics, biology and physics our aim is to understand what the optimalantibiotic treatments are in simple systems and to understand whether those treatments remain optimal for more complex biological systems.