Project properties
Title Modelling individual differences at the metabolic level using genome-scale models.
Group Systems and Synthetic Biology
Project type thesis
Credits 36
Supervisor(s) Edoardo Saccenti, Maria Suarez Diez
Examiner(s) Edoardo Saccenti, Maria Suarez Diez
Contact info robert1.smith@wur.nl
Begin date 2024/05/15
End date
Description A genome scale metabolic (GSM) model is a detailed representation of an organism's metabolism, encompassing all known chemical reactions and the genes associated with them. Each enzyme-associated reaction within a GSM model is described by an gene-protein-reaction (GPR) rule, which outlines the relationship between the genes that encode the enzyme catalyzing the reaction. These GPR associations allow GSM models to predict the phenotypic effects of genetic changes. In multicellular organisms, these rules facilitate the integration of gene or protein expression data with the GSM model, enabling the reconstruction of cell- and tissue specific metabolic models.1.

In this project we attempt to reconstructs a GSM (or a part of it) for a population of mice (>300), profiting of the availability of a multiomics data sets that encompass genetic, transcriptomics, metabolomics, proteomics and phenotypic data that can be used to contrain the models.

The ultimate goal is to understand if these individual models can be used to extract information explaining different characteristics of the mice, like phenotypic traits or different response to dietary treatment.

References

1. Khodaee, S., Asgari, Y., Totonchi, M., and Kariami-Jafari, M.H. (2020). iMM1865: A new reconstruction of mouse genome-scale metabolic model. Scientific Reports 10, 6177.

2. Williams, E.G., Pfister, N., Roy, S., Statzer, C., Haverty, J., Ingels, J., Bohl, C., Hasan, M., Cuklina, J., and Bühlman, P. (2022). Mulitomic profiling of the liver across diets and age in a diverse mouse population. Cell Systems 13, 43-57. e46.
Used skills Processing and analysis of large omics data sets; building and constraining metabolic models. Advanced programming skills in R and Python.
Requirements Command line computer tools. Basic understanding of methods to model metabolism such as constraint-based modelling or ordinary differential equations. Understanding of omics data (transcriptomics, proteomics, metabolomics) characteristics and knowledge of Machine learning and biological networks at the level of Molecular Systems Biology course (SSB-30306). Programming in Python and/or R.