Parameter estimation for genetic time-series data: Theory and methods

该项目旨在开发一种新的计算框架，通过利用下一代测序技术测量的遗传时间序列数据来解决进化建模中的参数估计问题。它将促进跨学科的国际合作。通过引入新的信号处理技术和工具，从随机矩阵理论通常用于移动的无线通信，它旨在设计可扩展的推理方法，解决突变适应度的影响，从遗传时间序列测量复杂的不断发展的人口。这将使人们对复杂的适应系统有新的理解，如病原体进化、宿主免疫动力学和获得耐药性。

This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communications, it seeks to design scalable inference methods for resolving mutational fitness effects from genetic time-series measurements of complex evolving populations. This would enable new understanding of complex adaptive systems, such as pathogen evolution, host-immune dynamics, and acquisition of drug resistance.