Mathematical models and computational methods for molecular epidemiology

分子流行病学数学模型和计算方法

• 批准号: RGPIN-2016-04622
• 负责人: Chindelevitch, Leonid
• 金额: $ 2.26万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615190
• 关键词: Mathematical; models; computational; methods; molecular

In the early 1940s, Alan Turing’s team designed the first prototype of a computing device that was able to break the code used by Nazi Germans and learn about their war plans. Today, we have an unprecedented opportunity to design algorithmic approaches using modern computers to break the genetic code of pathogens and learn about their evolution. Traditionally, scientists studied the spread of infections in populations by interviewing people who were diagnosed with the disease, and testing their recent or close contacts for its symptoms. Now, instead of interviewing the patients diagnosed with the disease, we can “interview” the genetic material of the infectious microbes causing the disease. This genetic material typically provides a more reliable record of events than human memory, by helping identify short-lived contacts that result in disease transmission, or by ruling out the transmission of disease between long-term contacts whose microbes look genetically different. An emerging area of research called molecular epidemiology studies the information about infectious disease ecology and evolution that can be gleaned from the analysis of pathogens' genetic material. My program’s long-term objective is to develop the mathematical models and computational methods relevant for molecular epidemiology, which would help us to understand the way infectious agents evolve to successfully invade and maintain themselves in a population. I am particularly interested in three aspects of molecular epidemiology. They are: the analysis of infections of a single person by multiple types (strains) of infectious microbes, which lifts the veil on mechanisms of competition and cooperation between these strains; the identification of the origin of infections (usually related to the geographic region where they were transmitted), which reveals their local adaptation; and the elucidation of drug resistance (the process used by microbes to avoid responding to the usual drugs used to treat them), which provides information about short-term evolution. Since models and algorithms currently used to study these aspects lack methodological sophistication, and therefore frequently result in inaccurate conclusions, I plan to fill this gap by developing more flexible and more powerful models and algorithms for analyzing them. My research program will contribute to advances in computational biology, mathematical modelling, and evolutionary theory, ultimately helping solve the mystery of the continued success of microbes since the dawn of life.

在20世纪40年代初，艾伦·图灵的团队设计了第一个计算设备的原型，能够破解纳粹德国人使用的代码并了解他们的战争计划。今天，我们有一个前所未有的机会来设计算法方法，使用现代计算机来破解病原体的遗传密码并了解它们的进化。 传统上，科学家通过采访被诊断患有这种疾病的人，并测试他们最近或密切接触者的症状来研究感染在人群中的传播。现在，我们可以“采访”引起疾病的传染性微生物的遗传物质，而不是采访被诊断患有这种疾病的患者。这种遗传物质通常提供比人类记忆更可靠的事件记录，帮助识别导致疾病传播的短暂接触，或者排除微生物在基因上不同的长期接触者之间的疾病传播。一个新兴的研究领域称为分子流行病学，研究有关传染病生态学和进化的信息，这些信息可以从病原体遗传物质的分析中收集。 我的计划的长期目标是开发与分子流行病学相关的数学模型和计算方法，这将有助于我们了解传染性病原体如何进化以成功入侵并在人群中维持自己。我对分子流行病学的三个方面特别感兴趣。它们是：通过多种类型（菌株）的传染性微生物对单个人的感染进行分析，从而揭开这些菌株之间竞争和合作机制的面纱;（通常与传播的地理区域有关），这表明它们在当地的适应情况;以及阐明耐药性（微生物用来避免对用于治疗它们的常用药物产生反应的过程），这提供了有关短期进化的信息。由于目前用于研究这些方面的模型和算法缺乏方法上的复杂性，因此经常导致不准确的结论，我计划通过开发更灵活，更强大的模型和算法来填补这一空白。 我的研究计划将有助于计算生物学，数学建模和进化理论的进步，最终帮助解决微生物自生命诞生以来持续成功的奥秘。