Modelling microbial evolution

细菌、病毒和其他微生物的进化速度比人类快得多。这使得研究人员可以在实验室里观察进化的发生，这一技术被称为“实验进化”。当这些微生物适应试管中的生活时，研究人员在他们的眼前观察进化的展开。这些技术已经将进化变成了一门实验科学，使科学家能够在实验室里解决一些关于进化的最基本的问题。然而，这个领域扩展得如此之快，以至于描述实验的数学框架还没有出现。目前，实验已经超越了理论，因此，实验者必须解释和检验他们的结果的权力被低估了。这项提议的第一个目标是为实验进化的分析开发一个数学框架。在一个极其新的和活跃的研究领域，这是一个几乎没有触及的问题；对加拿大来说，这是一个在基层介入并开展真正创新和重要工作的机会。微生物的快速进化也是严重公共卫生问题的一个来源。尤其是艾滋病毒等病毒对药物的反应迅速适应，直到药物对病毒不再有效。当药物不完全按处方服用时，即患者对药物治疗的依从性较低时，这尤其是问题。由于目前最好的艾滋病毒治疗方法可能涉及每天多达17粒，完美的坚持是极其困难的。这项提议的第二个目标是使用数学模型来估计防止耐药性出现所需的依从性程度。这是一个使用临床研究很难回答的问题。除了检查随机错过剂量的影响外，我们还将考虑有意的“药物假期”。*一些研究人员建议，这种假期可能会短暂地增加患者体内的病毒水平，从而以类似疫苗的方式增强免疫系统。*我们将对特定的抗艾滋病毒药物使用谨慎的数学模型来预测药物假期的最佳时间。

Bacteria, viruses and other microbes can evolve much more rapidly than humans.  This allows researchers to watch evolution occur in the laboratory, a technique called "experimental evolution".   As these microbes adapt to life in the test tube, researchers watch evolution unfold before their eyes.  These techniques have turned evolution into an experimental science, allowing scientists to address some of our most fundamental questions about evolution in the laboratory.  This field has expanded so quickly, however, that a mathematical framework describing the experiments hasn't yet emerged.  The experiments have, at the moment, out-stripped the theory, and as a result the power that experimentalists have to interpret and test their results has been short-changed.  The first goal of this proposal is to develop a mathematical framework for the analysis of experimental evolution.  This is an almost untouched question in an extremely new and active research area; it is a chance for Canada to step in at the ground level and produce truly innovative and important work. Rapid microbial evolution is also a source of serious public health concern.  In particular, viruses such as HIV rapidly adapt in response to pharmaceuticals until the drug in no longer effective against the virus.  This is especially problematic when drugs are not taken exactly as prescribed, that is, patient "adherence" to drug therapy is low.  Since the best current therapy for HIV may involve up to 17 pills per day, perfect adherence is extremely difficult.  The second goal of this proposal is to use mathematical models to estimate the degree of adherence that is necessary to prevent drug resistance from emerging.  This is a question that is very difficult to answer using clinical studies.  As well as examining the effects of random missed doses, we will also consider intentional "drug holidays".  Some researchers have suggested that such holidays could briefly increase the levels of virus in the patient, thus boosting the immune system in a way similar to a vaccine.  We will use careful mathematical models for specific anti-HIV drugs to predict the best timing for drug holidays.