Engineering Methods for Resolving Complex Mutational Networks in Proteins

该项目旨在开发一种新的计算框架，用于解决复杂的突变网络，这些突变网络支撑着蛋白质的功能和进化。它寻求发展统计推理方法是稳健的，高效的，并广泛适用。该项目将通过引入源自信号处理、移动无线通信和随机矩阵理论的参数估计和优化技术，促进国际合作并催生多学科研究。该项目的成果可用于了解不同的蛋白质系统，并有可能应用于从蛋白质工程到脑信号分析到疫苗设计的广泛应用。

This project aims to develop a novel computational framework for resolving complex mutational networks that underpin how proteins function and evolve over time. It seeks to develop statistical inference methods that are robust, efficient, and widely applicable. The project will promote international collaboration and spawn multidisciplinary research by introducing parameter estimation and optimisation techniques that stem from signal processing, mobile wireless communications, and random matrix theory. The project's outputs can be used to understand diverse protein systems and have the potential to be applied to wide-ranging applications from protein engineering to brain signal analysis to vaccine design.