Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.This work was supported by the Portuguese Science Foundation [grant numbers MIT-Pt/BS-BB/0082/2008, SFRH/BPD/44180/2008 to ZS] (http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Reconnaissance d'émotions à partir de la posture par LSTM

National audienceL'objectif de ce travail est de développer des méthodes d'apprentissage profond pour l'analyse et l'interprétation automatique des émotions à partir des mouvements et de la posture du corps humain. A partir du jeu de données BOLD nous avons crée un modèle basé sur les LSTM afin de détecter les émotions

Investigating Oxidoreduction Kinetics using Protein Dynamics.

International audienceFor twenty years, there are more and more crystallographic structures of enzymatic macromolecular complexes available in international data banks. At the same time, there is always an interest in better understanding enzyme functionalities. The movements of large protein structures are key components in ligand docking and enzymatic catalysis. Hence, simulations of enzymatic reactions must take into account such structural movements. Our aim is to combine modeling of the redox reactions and modeling of the conformational changes of enzymes structures in order to describe the dynamical functioning of redox enzymes. An agent based system has been developed to simulate internal enzymatic movements and redox reactions. We applied our approach to the complex II and III of the mitochondrial respiratory chain. Using this model, we are able to assess quantitative and qualitative enzymatic kinetic behaviors such as conversion rate of the overall reaction, individual electrons path within the complexes and potentially pathological short-circuits

PaLaDeM: a Pattern Language for Decision Making systems in Medicine

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3D-SiameseNet to Analyze Brain MRI

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Multi-Agent Design to Reduce Complexity of Biological Processes

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