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Agence de moyen
Année de démarrage - Année de fin de projet
Année de soumission
Jean-Loup Falon
Date de fin du projet
Nom de l'appel d'offre
ANR Appel à Projets Générique
Partenaires (hors MIA-PS)
MICALIS (Jouy-en-Josas), MaIAGE (Jouy-en-Josas), TIMC (Grenoble)
Participants de MIA-PS
Antoine Cornuéjols, Evelyne Lutton, Alberto Tonda
Titre du projet
Artificial Metabolic Networks
Our main objective is to demonstrate that the metabolism of microorganisms can serve as a device for solving computational problems. The primary role of metabolism is to process food and it is not a priori viewed as an information processing apparatus. Yet, organisms have evolved to cope with different environments and metabolism necessarily played a role in processing and transducing environmental signals to the genetic layer.
It is often assumed that biochemical networks serve either for chemical production (metabolism, synthesis of macromolecules) or signaling (MAPK pathways, gene regulation networks,..). This separation ignores the fact that metabolism is also involved in cell signaling and that metabolic fluctuations may allow signals to spread. There is indeed some evidence that metabolism contributes to quite sophisticated signal transmission and decision-making tasks in microorganisms [1][2] plants [3] and of course with the production of neurotransmitters involved in the chemical synapses of our brains.
Here we hypothesize that this ability - the processing and transduction of signals in metabolism - can be diverted to tackle problems that are typically solved by artificial intelligence and in particular by artificial neural networks (ANNs). Since metabolic networks link inputs (concentrations of external metabolites) to outputs (fluxes, concentrations, cell growth), they can be considered as "programs", comparable to ANNs. By adding entries or changing their dynamics (for example by gene deletions), these programs could be shaped to perform classification or regression tasks.
Our objective is to formalize this new paradigm and to establish a relationship between metabolic models and ANNs. A formal connection will help us (i) understand in WP1 the information processing capacities of metabolism (e.g. transducing information about external conditions or aggregating information about metabolic demands) and how evolution may create "informative” metabolites, such as flux sensors [4], (ii) investigate in WP2 metabolism-inspired artificial networks and their potential for machine learning, and (iii) engineer in WPs 3 and 4 in vivo "metabo-genetic devices" that can detect biochemical signals and convert these into informative outputs in the context of two biotechnologically relevant problems: bioprocess optimization and medical diagnostics.

[1] J. Lee and L. Zhang, “The hierarchy quorum sensing network in Pseudomonas aeruginosa,” Protein Cell, vol. 6, no. 1, pp. 26–41, 2015, doi: 10.1007/s13238-014-0100-x.
[2] K. Alim, N. Andrew, A. Pringle, and M. P. Brenner, “Mechanism of signal propagation in Physarum polycephalum,” PNAS, vol. 114, no. 20, pp. 5136–5141, May 2017, doi: 10.1073/pnas.1618114114.
[3] B. Scheres and W. H. van der Putten, “The plant perceptron connects environment to development,” Nature, vol. 543, no. 7645, pp. 337–345, Mar. 2017, doi: 10.1038/nature22010.
[4] K. Kochanowski, et al., “Functioning of a metabolic flux sensor in Escherichia coli,” Proceedings of the National Academy of Sciences, vol. 110, no. 3, pp. 1130–1135, Jan. 2013, doi: 10.1073/pnas.1202582110.