Research

Machine learning and systems biology approaches to study the gut microbiome

Developing a machine learning-based analytical pipeline using systems biology approaches to identify microbiome-related features implicated in ageing.

Project Summary.

Led by: Korcsmáros Group

Start date: Oct 2018

Duration: Oct 2022

Grants: NPIF-BBSRC-CASE project

BB/S50743X/1

As life expectancy increases, developed countries have an increasingly elderly population. The gut microbiome primes the immune system during early development and contributes to lifelong homeostasis. Several disorders, such as dementia and Inflammatory Bowel Disease (IBD), are associated with gut microbiome alterations. Despite developments in generating microbiome data, the key obstacle remains - which is to extract predictive biomarkers. This problem not only consists of the processing of these large datasets, but also the fact that microbiome studies are often correlative, and therefore lack host-microbiota interaction mechanisms. Furthermore, there are a large number of unknown microbes (mostly commensals) whose health-contributing potential continues to be unclear. This, in turn, means these datasets remain very noisy, thus requiring complex analytical methods.

To address these issues, in partnership with BenevolentAI, this project aims to develop an integrated machine learning-based systems biology workflow, which can be applied to the gut microbiome data for identifying prognostic indicators of healthy ageing and age-related disorders. The approach is based on metagenomics and metatranscriptomics data, which captures the composition and functional potential of the microbiome in modulating host processes.

Impact statement.

This project will identify dynamic changes in the gut microbiome during healthy and unhealthy (disease-state) ageing. The gut microbiota is considered an essential companion of human cells, as microbes have been found to interact with nearly all human cells, therefore dynamic changes in these communities can play a fundamental role in the ageing process.

It follows, therefore, that understanding these complex interplay between the host and the microbial communities within the microbiota is an important step in understanding the ageing process.