Bacteriophage and Antibiotic Resistance: a Mathematical and Imaging Approach

噬菌体和抗生素耐药性：数学和成像方法

• 批准号: EP/I00503X/1
• 负责人: Robert Beardmore
• 金额: $ 160.73万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI00503X%2F1
• 关键词: Bacteriophage; Antibiotic; Resistance; Mathematical; Imaging

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.

人们普遍认为，医学正面临着一个影响人类健康未来的重大问题。由于抗生素耐药性细菌的进化和传播，以及合成新抗生素产品的难度越来越大，我们需要找到治疗细菌感染的新方法。当我们着手设计利用工程细菌及其病毒噬菌体的合成疗法时，我们需要更好地了解如何使用我们所拥有的抗菌剂。找到“最佳抗生素治疗”可能是一个遥远的目标，但研究人员最近开始考虑将抗生素结合起来的新方法，以最大限度地减少对抗生素的耐药性。这是该提案的重点：我们如何超越药代动力学的有效性措施，找到新的理论基础，为最佳的治疗？处理这一问题的办法必须包括不同的领域。我们需要来自系统生物学的工具，告诉我们如何模拟单个细胞内复杂过程的行为，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，我们需要进行实证研究，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，那么我们就应该在实验室中检验这一说法的真实性。该提案中所解决的研究问题类型的实验范式是“实验微生物系统”，即可以在体外创造并观察和重复其进化的进化微观世界。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。这种经验手段的效用在于可以迅速地检验假说，我们很快就会看到，理论上产生的观念在实践中是否有效，但是我们如何得出这样的理论预测呢？通过实验系统的数学模型，并要求一种形式的“可验证性”。也就是说，我们首先要问的是，在数学模型中是否可以实现一个特定的结果。这一结果可能意味着，例如，使用抗生素将细菌从其宿主中去除，使其密度最小化，同时防止细菌进化出抗生素耐药性;我们声称这类问题很好地符合系统和控制方法。尽管基因组技术发展非常迅速，众所周知，生物系统很难建模，数据也可能很稀少，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。这个想法是一种常见的数学技术，是寻找将不同系统识别为具有相同结构的原则，这些原则可以使用数学工具进行抽象处理。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是否总是比将抗生素混合成单一鸡尾酒的方法更好？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性问题。由于其中一些问题是困难和雄心勃勃的，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培养基中的生长，并预测和测量不同鸡尾酒的效力？这种实验本身是新颖的，并将为更多的理论部分的工作提供基础。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的最佳抗生素治疗方法，并了解这些治疗方法是否对更复杂的生物系统保持最佳。

项目成果

Testing the optimality properties of a dual antibiotic treatment in a two-locus, two-allele model.

在双基因座、双等位基因模型中测试双抗生素治疗的最优特性。

• DOI: 10.1098/rsif.2013.1035
• 发表时间: 2014
• 期刊: Journal of the Royal Society, Interface
• 作者: Peña-Miller R

The optimal deployment of synergistic antibiotics: a control-theoretic approach

协同抗生素的最佳部署：控制理论方法

• DOI: 10.1098/rsif.2012.0279
• 发表时间: 2012
• 期刊: Journal of The Royal Society Interface
• 影响因子: 3.9
• 作者: Peña-Miller R

Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?

• DOI: 10.1093/molbev/msw292
• 发表时间: 2017-04-01
• 期刊: Molecular biology and evolution
• 影响因子: 10.7
• 作者: Beardmore RE;Peña-Miller R;Gori F;Iredell J
• 通讯作者: Iredell J

Biophysical mechanisms that maintain biodiversity through trade-offs.

通过权衡维持生物多样性的生物物理机制。

• DOI: 10.1038/ncomms7278
• 发表时间: 2015
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Meyer JR

Using a sequential regimen to eliminate bacteria at sublethal antibiotic dosages.

• DOI: 10.1371/journal.pbio.1002104
• 发表时间: 2015-04
• 期刊: PLoS biology
• 影响因子: 9.8
• 作者: Fuentes-Hernandez A;Plucain J;Gori F;Pena-Miller R;Reding C;Jansen G;Schulenburg H;Gudelj I;Beardmore R
• 通讯作者: Beardmore R