Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Supervised enzyme network inference from the integration of genomic data and chemical information

Abstract: Motivation: The metabolic network is an important biological network which relates enzyme proteins and chemical compounds. A large number of metabolic pathways remain unknown nowadays, and many enzymes are missing even in known metabolic pathways. There is, therefore, an incentive to develop methods to reconstruct the unknown parts of the metabolic network and to identify genes coding for missing enzymes. Results: This paper presents new methods to infer enzyme networks from the integration of multiple genomic data and chemical information, in the framework of supervised graph inference. The originality of the methods is the introduction of chemical compatibility as a constraint for refining the network predicted by the network inference engine. The chemical compatibility between two enzymes is obtained automatically from the information encoded by their Enzyme Commission (EC) numbers. The proposed methods are tested and compared on their ability to infer the enzyme network of the yeast Saccharomyces cerevisiae from four datasets for enzymes with assigned EC numbers: gene expression data, protein localization data, phylogenetic profiles and chemical compatibility information. It is shown that the prediction accuracy of the network reconstruction consistently improves owing to the introduction of chemical constraints, the use of a supervised approach and the weighted integration of multiple datasets. Finally, we conduct a comprehensive prediction of a global enzyme network consisting of all enzyme candidate proteins of the yeast to obtain new biological findings. Availability: Softwares are available upon request. Contact: yoshi@kuicr.kyoto-u.ac.jp

Combined Use of Hydraulic and Electrical Properties of an Aquifer in a Geostatistical Estimation of Transmissivity

Abstract: Many previous attempts have been made to establish an empirical relation between the electrical and hydraulic properties of aquifers. However, only regression models between transmissivity or permeability and a few electrical parameters have been used on the basis of the available pairs of data. Kriging, a geostatistical technique, estimates a regionalized variable at any point in space, and multivariate geostatistical techniques allow one to use several variables together to estimate any ‘spatial parameter. One such method, cokriging, is used to estimate the transmissivity based not only on measurements of transmissivity, but also on measurements of specific capacity and electrical transverse resistance. The studied aquifer is situated in the Medjerda Valley in Tunisia where very few data on transmissivity and specific capacity are available, but resistivity data are relatively abundant. It is shown that with the geostatistical technique, one can: (1) use several electrical or elastic properties, which are easily measured, in the estimation of the desired parameter without establishing any empirical relation; and (2) make the estimation at any point where none of these properties has been sampled and, at the same time obtain a variance of the estimation error. The method is also compared with the usual regression method.