Network approaches for modelling the Human Microbiome
Lead Research Organisation:
University of Liverpool
Department Name: Institute of Integrative Biology
Abstract
Through technological advances in DNA sequencing, functional genomics and computational biology, it is becoming increasingly evident that the human microbiome plays a pivotal role in the maintenance of health and the pathogenesis of disease.
Recent developments, have highlighted the potential of network biology to "reverse engineer" the ecology of microbial community from such microbiome data. However, so far these approaches have lead to descriptive models that do not allow in silico simulation of the population dynamics following interventions.
Therefore, the overarching objective of this project is to assess novel inference methods that overcome this issue. We propose to apply these methodologies to the analysis of a large body of microbiome datasets developed by Unilever to investigate the role of bacterial communities in maintaining the homeostasis of body sites such as the skin or oral cavity.
Recent developments, have highlighted the potential of network biology to "reverse engineer" the ecology of microbial community from such microbiome data. However, so far these approaches have lead to descriptive models that do not allow in silico simulation of the population dynamics following interventions.
Therefore, the overarching objective of this project is to assess novel inference methods that overcome this issue. We propose to apply these methodologies to the analysis of a large body of microbiome datasets developed by Unilever to investigate the role of bacterial communities in maintaining the homeostasis of body sites such as the skin or oral cavity.
Organisations
People |
ORCID iD |
Francesco Falciani (Primary Supervisor) | |
Daniel Warren (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/M011186/1 | 30/09/2015 | 31/03/2024 | |||
2277278 | Studentship | BB/M011186/1 | 30/09/2019 | 31/12/2023 | Daniel Warren |
BB/T508937/1 | 30/09/2019 | 29/09/2023 | |||
2277278 | Studentship | BB/T508937/1 | 30/09/2019 | 31/12/2023 | Daniel Warren |