Integrative multi-omics approaches for exploring host-microbiome interaction and biomarker identification

The intricate relationship between the host and its microbiome plays a critical role in health and disease. With the rapid development and accumulation of high-throughput sequencing technology and omics data, recent years have seen a growing interest in understanding the host-microbiome interaction from a multi-omics perspective. Leveraging multi-omics strategies, this project aims to develop an innovative computational framework for comprehensive analysis of the relationships between microbiome, host and environment and their causal role in biological processes, which can be applied to either human health and/or animal science.  

This project will address the following research questions:

1. What is the impact of the microbiome and its functional activities on biological process?
2. How does the interplay between the microbiome and the host at a genetic level influence traits such as metabolism and gene expression?
3. Can artificial intelligence and machine learning provide insights and potential strategies to manipulate the microbiome-trait connections to achieve desired outcomes?

This studentship will benefit from a multidisciplinary supervision team – computer scientists with expertise on integrative data analytics,  biomedical informatics, complex network analysis & bioinformatics; biomedical scientist with expertise on nutrition and animal health and biologist with expertise on host microbiome systems.  The successful candidate will receive training on developing AI methods to uncover cause-effect relationships of host-microbiome interaction and to identify unique biomarkers from multi-omics data, as well as advancing the understanding of diet, genetic and health interactions. 

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