Mathematical models and computational methods for molecular epidemiology

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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.