Can metabolic control analysis be used to control epidemics?

我们最近的数学模型表明，在英国家禽业中，针对鸭和鹅养殖场的H5N1禽流感比对所有养殖场采用更普遍的干预策略更有效地控制（成本）。事实上，这些有针对性的干预措施已被证明完全降低了大规模暴发的可能性。目前的研究是一项可行性研究，旨在为设计预防和控制流行病的有针对性的干预措施制定一个总体框架。它是基于流行病在网络上表示的最新数学发展，使系统生物学的思想能够应用于这个新领域。这些方法与畜牧业中的流行病特别相关，因为畜牧业的结构通常由由潜在感染途径的接触网络连接的单个农场很好地描述。目前在这些工业中控制流行病的措施往往依赖于加强生物安全、接种疫苗、扑杀或禁止流动的一般政策。虽然这些方法显然是有效的，但这些方法的更有针对性的应用可能对工业操作的干扰较小，而且实施成本更低。流行病是复杂系统的例子，其规模、可能性和控制等全系统因素是许多单独组成部分相互作用产生的紧急行为。除了其他许多其他领域，有关复杂系统控制的问题也发生在系统生物学中，我们也希望通过有针对性的干预来改变网络功能，以帮助我们获得理解并产生医疗和工业应用。例如，这些可能是最大限度地产生用于工业目的的特定化学物质（例如酵母中的乙醇）或用于医疗应用（例如确定在不伤害健康细胞的情况下摧毁肿瘤细胞的关键干预措施）。也就是说，我们希望以最小的针对性干预达到最大的积极效果。在系统生物学中，一种称为代谢控制分析（MCA）的控制理论形式被用来确定特定干预对活生物体（通常是单细胞）的影响。该项目将发展相关构想，设计有针对性的干预措施，以预防或控制流行病。在这种情况下，类似MCA的理论的应用需要一些新的数学发展，但在可能的情况下，它的发展将受到与MCA的比较的启发。控制牲畜感染的有针对性的干预显然具有重要意义，不仅对动物的福利，而且对工业和更广泛的经济产生影响。这种分析还可以确定流行病的关键地点或危险因素，以便采取预防措施，使流行病很少发生。这项研究的一个更微妙的好处是在系统生物学中使用的方法和流行病学中使用的方法之间建立了更强的联系。目前，这些学科的发展相当独立，但在方法论上有显著的相似之处。预计这项研究将有助于建立更紧密的联系，在一个领域开发和实施的想法将启发另一个领域的想法。

Our recent mathematical modelling has shown that H5N1 avian influenza in the British poultry industry can be controlled more (cost) effectively by targeting duck and goose farms than by more general intervention strategies applied equally to all farms. Indeed, these targeted interventions were shown to mitigate the chance of large outbreaks altogether.The present research is a feasibility study to develop a general framework for designing targeted interventions to prevent and control epidemics. It is based on recent mathematical developments in the representation of epidemics on networks which enable ideas from systems biology to be applied in this new area. These methods are particularly relevant for epidemics in livestock industries where the structure is often well described by individual farms connected by a contact network of potential infection routes. Current measures for controlling epidemics in these industries often rely on general policies of increased biosecurity, vaccination, culling or movement bans. While these approaches are demonstrably effective, more targeted applications of these methods are likely to be less intrusive to the operation of industry as well as being cheaper to implement.Epidemics are examples of complex systems where system-wide factors such as their size, likelihood, and control are emergent behaviours resulting result from the interaction of many individual components. In addition to other many other areas, problems concerning the control of complex systems also occur in systems biology where we also wish to alter network functionality by targeted interventions to help us gain understanding and to generate medical and industrial applications. For example, these could be to maximise the generation of a particular chemical for industrial purposes (e.g. ethanol in yeast) or for medical applications (e.g. determining key interventions which will destroy a tumour cell while leaving healthy cells unharmed). That is, we want to achieve the maximum positive effect with the minimum targeted intervention.In systems biology, a form of control theory known as Metabolic Control Analysis (MCA) is used to determine the impact of specific interventions on living (typically single-cell) organisms. This project will develop related ideas to design targeted interventions to prevent or control epidemics. The application of an MCA-like theory in this context requires some novel mathematical developments but, where possible, its development will be inspired by comparison with MCA. Targeted intervention to control infection in livestock is clearly of significant importance, not only for the welfare of the animals but also for the economic impact on industry and the wider economy. This analysis could also identify key sites or risk factors for epidemics enabling preventative measures to be put into place such that an epidemic rarely occurs.A more subtle benefit from this research is to establish a stronger link between the methods used in systems biology and the methods used in epidemiology. Presently these subjects evolve quite independently but have notable methodological similarities. It is expected that this research will contribute to closer links, with ideas developed and implemented in one area inspiring ideas in the other.