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Messages - Fabien Jourdan

Conferences and seminars / European RFMF Metabomeeting 2020

Registrations for #MetaboEU2020 (early bird deadline Novembre 8th 2019)

20 travel grants available for early carreer scientists

Many talks opportunities (selection on abstracts):
2 keynotes
14 talks
6 early career talks
2 flash sessions

6 invited speakers
Pr. Warwick (Rick) Dunn, University of Birmingham, UK. MS - Human Health
Dr. Maria Fedorova, Universität Leipzig, Germany. Lipidomics - Human Health
Dr. Ingela Lanekoff, University of Uppsala, Sweden. MS-Imaging
Pr. Zoran Nikoloski, Max Planck Institute, Germany. Modelling - Plant metabolism
Dr. Emma Schymanski,University of Luxembourg, Luxembourg. Chemoinformatics - Environement
Pr. Marcel Utz, University of Southampton, UK. NMR - Methdological and technological developments

Several Workshops
"Meet the editor" (Roy Goodacre, J. Griffin et al.)
"Toulouse school of lipids" (J. Bertrand Michel et al.)
"Workflow4Metabolomics" (M. Tremblay Franco, Yann Guitton et al.)
And more to come!

Organised by European affiliated societies:
French-speaking Metabolomics and Fluxomics Network (RFMF)
German Society for Metabolomics Research
Italian Metabolomics Network
Metabolic Profiling Forum (MPF)
Netherland Metabolomics Centre (NMC)
Nordic Metabolomics Society
Scottish Metabolomics Network
Spanish Metabolomics society
Swiss Metabolomics Society

Job opportunities / PhD position at L'Oréal & INRA (France)
We propose a PhD position available within a collaborative project between L'Oréal and INRA in Toulouse (France).

Title of the thesis is "Development of new systems biology approaches for the evaluation of the toxicity of cosmetic raw materials."

Aim is to use metabolic network modelling to perform preidctive toxicology in the context of in vitro studies.

More information at this URL:
Job opportunities / PostDoc (2 years) position in Bioinformatics and Computational Biology
Please find attached a 2 year PostDoc position in Bioinformatics and Computational Biology to study p53 metabolic functions.

Institution: French National Institute for Agricultural Research INRA Toxalim Research Unit
Location: France, Toulouse, 180 chemin de Tournefeuille 31000 Toulouse
Supervision: Dr Fabien JOURDAN and Dr Nathalie POUPIN
Starting date: Flexible from November 2017 to June 2018
Type: Full Time
Application deadline: September 29th 2017

More details in the attached file.

[attachment deleted by admin]
Genome scale network analysis / Re: Avoiding connection on side compounds
Dear Maria,

Happy the paper was helpful and sorry if some parts were not clear enough. Please find below some elements of answer.

The last method is more dedicated to compound networks, were reactions are splitted into several substrate-product transitions. By saying that no atoms are exchanged between glucose and ADP during the glucokinase reaction we mean that atoms from the ADP product only came from the ATP substrate, so it may not be relevant to connect glucose to ADP based on that reaction. It is useful when you want to found paths between target compounds, without having those "side compounds shortcuts", even if side compounds remain in the network, as well as transition like ADP to ATP. Only links between "main" compounds and side compound will be removed or at least omitted during path search. So strictly speaking chemistry based method doesn't identify side compounds but it help to avoid irrelevant links between non-related compound.

However, I agree that using similarity weighting at the reaction level may leads to mistakes. It can help to extract principal transitions (both GLC-G6P and ADP-ATP as you said), which can be enough for reactions like "maltose + H2O -> glucose", but require manual curation for most cases.
This work has been done for many reactions in the Kegg database. They provided in the RPAIRs database tags for substrate to product transitions, allowing to distinguish "side" transitions from main ones in a reactions. Unfortunately this service has been discontinued this year, and the "side" annotation are no longer available on their website, but I'm pretty sure those data can be found elsewhere.

RPAIRs tags or manually annotated set combined with atom mapping data might be used to train an AI, I've never tried myself but I guess it will be tough work. If found, mapping kegg tags on your network might be a good start (even if not straightforward, but tools for converting identifier from different sources exists).
If the coverage of kegg data on your reaction list is good enough, you can consider a compound not involved in at least one main transition as a side compound. To my opinion it is better to define side compounds in a context of a particular reaction, as for example ATP can be considered as "main" compound in reactions from the nucleotide biosynthesis pathway, despite being a side compound in most reaction.

I hope it answers your questions and the paper came out after your thesis, so no worry ;-)

Best wishes
PS : this answer was mostly writen by Clément Frainay, PhD candidate in my group.
Genome scale network analysis / Re: Avoiding connection on side compounds
Hi Maria,
Thanks for initiating discussion :-)
There are several options to avoid side compounds but unfortunately no gold standard ...

- pre-difining a list of compounds that will be considered as side compounds (a bit like what you do)
PROS : the most reliable since it is manual
CONS : it is arbitrary and, you may want some flexibility since this definition is not obvious for some compounds (e.g. CoenzymeA...). Moreover, if you work on genome scale network it can be quite time consuming !!

- computing node degree of all compounds and removing automatically the highly connected compounds. The assumption is that metabolites involved in many reactions are more likely to be side compounds
PROS : it is automatic
CONS : it requires a threshold which is hard to define automatically. You may have highly connected compounds which are of interest (like glutamate)

- Using chemistry. You can use the chemical similarity (using fingerprints) to establish a connection between a given substrate and the product which is more likely to be connected to it.
PROS : automatic and take chemistry into account
CONS : you need to have the chemical structure (eg encoded in Inchi) for all substrates and products. Many models and database don't have them unfortunately...

To my mind, best (automatic) solution is the latest. But it requires that the modelling community makes the effort to add chemical information on metabolites.

Finally, we wrote a review including this topic. Maybe it can help: (sorry it is not free access but drop me a message and I will send privately).

Metabolic Networks and and Pathways / Re: KEGG- map searching
KEGG also propose a REST-Style API described in this page:
You will have to got the response from the URL.
For instance following rest address:
Code: [Select]
will provide the list of metabolites belonging to this map :
Code: [Select]
path:map00010	cpd:C00022
path:map00010 cpd:C00024
path:map00010 cpd:C00031
path:map00010 cpd:C00033
path:map00010 cpd:C00036
path:map00010 cpd:C00068
path:map00010 cpd:C00074
path:map00010 cpd:C00084
path:map00010 cpd:C00103
path:map00010 cpd:C00111
path:map00010 cpd:C00118
path:map00010 cpd:C00186
path:map00010 cpd:C00197
path:map00010 cpd:C00221
path:map00010 cpd:C00236
path:map00010 cpd:C00267
path:map00010 cpd:C00469
path:map00010 cpd:C00631
path:map00010 cpd:C00668
path:map00010 cpd:C01159
path:map00010 cpd:C01172
path:map00010 cpd:C01451
path:map00010 cpd:C05125
path:map00010 cpd:C05345
path:map00010 cpd:C05378
path:map00010 cpd:C06186
path:map00010 cpd:C06187
path:map00010 cpd:C06188
path:map00010 cpd:C15972
path:map00010 cpd:C15973
path:map00010 cpd:C16255