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

遗传时间序列数据的参数估计：理论与方法

• 批准号: FT200100928
• 负责人: Prof Matthew McKay
• 金额: $ 76.62万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-11-30 至 2025-11-29
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT200100928
• 关键词: Parameter; estimation; genetic; time; series

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.

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。通过引入信号处理的新技术和通常用于移动的无线通信的随机矩阵理论工具，它寻求设计可扩展的推理方法，用于解决复杂进化群体的遗传时间序列测量的突变适应度效应。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。