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Printable Handouts
Navigable Slide Index
- Introduction
- Bioinformatics: science of recommendation
- Interpreting metabolomics data
- Example: yeast cadmium exposure study
- Expertise based analysis
- Untargeted observations require holistic analysis
- Gather metabolic knowledge - reconstruction
- Genome scale models
- Metabolic networks for multi-OMIC interpretation
- Integrate metabolomics data - mapping
- Mapping metabolites in genome scale networks
- Pyruvate in the different files and databases
- From metabolic profiles to sub-networks
- Using Inchi and InChiKeys as identifiers
- From names to identifiers to networks
- Pathway enrichment (1)
- Pathway enrichment (2)
- Pathway enrichment - bright side and dark side
- Limits of pathway mapping: connectivity
- Pathway boundary is versatile in databases
- Taking into account full complexity
- Sytesms biology markup language
- Model global metabolism - graphs
- Genome-scale metabolic networks
- From knowledge to model (1)
- From knowledge to model (2)
- Graph modelling(s) of metabolic networks
- The network jungle
- Suggest interpretation - algorithms
- From glucose to pyruvate
- Problem complexity: from glucose to pyruvate
- Shortest path
- Using the topology to avoid side compounds
- Lightest path and lightest path²
- Using the chemistry to improve path search
- Connecting metabolites from a fingerprint
- Union of lightest paths
- Sub-network extraction: union of all paths
- Metexplore: server for Omics network analysis
- Next step: getting into dynamics
- Take home messages
- Acknowledgements
Topics Covered
- Metabolic networks
- Reconstructing metabolic networks
- Mapping metabolites in genome-scale networks
- The challenge of identifiers
- Structure and algorithms of metabolic networks
- Using biomarkers in biochemical networks
- Building subnetworks based on metabolic profiles
Links
Series:
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Talk Citation
Jourdan, F. (2018, June 28). Metabolomics data analysis in the context of metabolic networks [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved November 21, 2024, from https://doi.org/10.69645/LSNO2850.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Fabien Jourdan has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Other Talks in the Series: Bioinformatics for Metabolomics
Transcript
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0:00
Hello. My name is Fabien Jourdan.
I'm a Senior Research Scientist at
the French Agronomic Research Institute in the South-West of France, Toulouse.
Today, I'm going to talk about
Metabolomics Data Analysis in the context of Metabolic Networks.
More generally, you will see that these data,
they can also be also kind of omics data that you can put in these networks.
So, it would be a brief overview just to give you
a broad idea about what you can do with these networks.
0:34
I will start with a little station about
Isaac Asimov saying the most exciting phrase to hear in science,
the one that heralds new discoveries,
is not Eureka but that's funny.
The point here is that the bioinformatics,
what we are doing in bioinformatics is just suggesting solutions.
We don't give final answers.
We provide some clues to the biologist about potential solution based on their data.
My point here and I will say that again is that we rely on the data and we cannot
100 percent sure that we provide
the perfect answer but we try to give good directions to that.
So, bioinformatics is basically a science of
recommendation and that's what I'm going to exemplify with the networks.
1:28
So, the first question I'm going to address and the main question is how can we
help in interpreting the metabolomics data in the context of metabolic networks?