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
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
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 Fabien PS : this answer was mostly writen by Clément Frainay, PhD candidate in my group.
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.