Genome-scale metabolic models (GSMMs) are mathematical models designed to represent the whole metabolic network of a given organism. They are used to predict growth rates and metabolic fluxes under different conditions, and they can be exploited to identify genetic engineering strategies (Gu et al., 2019).
Multi-omics data refers to the data generated by different omics such as genomics, transcriptomics and metabolomics. The data obtained through these molecular profiling experiments provide extremely useful information on dynamic cellular processes governing an organism and can be used to validate and refine GSMMs. At the same time, well-curated GSMMs provide the perfect framework for the integration and interpretation of multi-omics datasets (Del Carratore et al., 2021).
This project aims to develop the computational methods needed to gain a system-level understanding of the metabolism of specific bacterial strains, by combining GSMMs with multi-omics data.
You will be based in Liverpool in the Institute of Systems, Molecular and Integrative Biology, host of some of the world’s best facilities for metabolomics, genomics, proteomics and computational biology (https://www.liverpool.ac.uk/health-and-life-sciences/research/liverpool-shared-research-facilities/multi-omics/).
This research project comes with a multi-disciplinary training revolving around computational biology and data analysis of big biological data. This will provide you highly transferrable skills and a wide choice of career options.