Doctoral Course Syllabus

Title: Characterisation of Metabolic Phenotypes via Data Integration

Duration: 8 hours (January 23-24)

Teacher: Chiara Damiani

Course Overview:

Technological advancements in the determination of complete genomic sequences and their annotation, and the well-explored biochemistry of metabolic transformations have facilitated the thorough reconstruction, on a genome-scale, of the metabolic network of many target organisms. The conversion of metabolic networks into a mathematical representation has established a framework upon which a mechanistic understanding of the metabolic genotype–phenotype relationship can be articulated. While a metabolic reconstruction is unique to the target organism, one can derive many different condition-specific models from a single reconstruction. Mapping of ‘omics’ into these networks enables the analysis of the ‘omics’ in the context of the curated knowledge about the target organism.

This doctoral course aims to provide participants with the basic knowledge of the metabolic network reconstruction and analysis framework. The focus will be on the analysis of metabolic pathways through the integration of various data types, including gene expression, proteomics, and metabolomics. The course will cover theoretical concepts as well as practical applications using user-friendly web-based platforms such as Esher and Galaxy. 

By the end of the course, students will have acquired the ability to independently navigate and assess the strengths and limitations of the current state-of-the-art methods and the increasing list of accomplishments of the vibrant and growing field of metabolic network reconstruction and analysis.

Day 1: January 23

Morning Session (10:30-12:30): Introduction to Metabolic Networks

• Overview of Metabolic Networks
• Challenges in Analyzing Metabolic Phenotypes
• Importance of Data Integration
• From gene expression to metabolic Reaction Activity Scores

Afternoon Session (14:30-16:30): Practical Session on Galaxy Platform

• Introduction to Galaxy
• Hands-on Exercise: Gene Expression Data mapping into the human metabolic network

Day 2: January 24

Morning Session (10:30-12:30): Advanced Data Integration Techniques

• The constraint-based modeling framework
• Flux sampling and prediction of Metabolic Flux Differences
• Integration of Transcriptomics, Proteomics, and Exo-Metabolomics
• Identification of Transcriptionally Controlled Metabolic Reactions

Afternoon Session (14:30-16:30): Practical Session on Esher Platform

• Introduction to Esher
• Hands-on Exercise: In Silico Prediction of Robustness to Nutritional Perturbations

Assessment:

Participants will be evaluated based on their engagement in practical exercises and a final project. The project will involve applying the learned concepts to a relevant case study.

Prerequisites:

Recommended reading: Nielsen, J. (2017). Systems biology of metabolism. Annual review of biochemistry, 86, 245-275. https://www.annualreviews.org/doi/full/10.1146/annurev-biochem-061516-044757

Platform Requirements:

Participants should have access to a computer with internet connectivity for the practical sessions.

Note: The schedule and topics may be subject to slight adjustments based on the pace of the class and participants' background knowledge.