Marie Beurton-Aimar

ACoM: A classification method for elementary flux modes based on motif finding

Elementary flux mode analysis is a powerful tool for the theoretical study of metabolic networks. However, when the networks are complex, the determination of elementary flux modes leads to combinatorial explosion of their number which prevents from drawing simple conclusions from their analysis. To deal with this problem we have developed a method based on the Agglomeration of Common Motifs (ACoM) for classifying elementary flux modes. We applied this algorithm to describe the decomposition into elementary flux modes of the central carbon metabolism in Bacillus subtilis and of the yeast mitochondrial energy metabolism. ACoM helps to give biological meaning to the different elementary flux modes and to the relatedness between reactions. ACoM, which can be viewed as a bi-clustering method, can be of general use for sets of vectors with values 0, +1 or -1.

Comparison between elementary flux modes analysis and 13C-metabolic fluxes measured in bacterial and plant cells

Background13C metabolic flux analysis is one of the pertinent ways to compare two or more physiological states. From a more theoretical standpoint, the structural properties of metabolic networks can be analysed to explore feasible metabolic behaviours and to define the boundaries of steady state flux distributions. Elementary flux mode analysis is one of the most efficient methods for performing this analysis. In this context, recent approaches have tended to compare experimental flux measurements with topological network analysis.ResultsMetabolic networks describing the main pathways of central carbon metabolism were set up for a bacteria species (Corynebacterium glutamicum) and a plant species (Brassica napus) for which experimental flux maps were available. The structural properties of each network were then studied using the concept of elementary flux modes. To do this, coefficients of flux efficiency were calculated for each reaction within the networks by using selected sets of elementary flux modes. Then the relative differences - reflecting the change of substrate i.e. a sugar source for C. glutamicum and a nitrogen source for B. napus - of both flux efficiency and flux measured experimentally were compared. For both organisms, there is a clear relationship between these parameters, thus indicating that the network structure described by the elementary flux modes had captured a significant part of the metabolic activity in both biological systems. In B. napus, the extension of the elementary flux mode analysis to an enlarged metabolic network still resulted in a clear relationship between the change in the coefficients and that of the measured fluxes. Nevertheless, the limitations of the method to fit some particular fluxes are discussed.ConclusionThis consistency between EFM analysis and experimental flux measurements, validated on two metabolic systems allows us to conclude that elementary flux mode analysis could be a useful tool to complement 13C metabolic flux analysis, by allowing the prediction of changes in internal fluxes before carbon labelling experiments.

Fast Computation of Minimal Cut Sets in Metabolic Networks with a Berge Algorithm that Utilizes Binary Bit Pattern Trees

Minimal cut sets are a valuable tool for analyzing metabolic networks and for identifying optimal gene intervention strategies by eliminating unwanted metabolic functions and keeping desired functionality. Minimal cut sets rely on the concept of elementary flux modes which are sets of indivisible metabolic pathways under steady state condition. However, the computation of minimal cut sets is non-trivial, as even medium sized metabolic networks with just 100 reactions easily have several hundred million elementary flux modes. We developed a minimal cut set tool that implements the well known Berge algorithm and utilizes a novel approach to significantly reduce the program run time by using binary bit pattern trees. By using the introduced tree approach the size of metabolic models that can be analyzed and optimized by minimal cut sets is pushed to new and considerably higher limits.

A hyperstructure approach to mitochondria

Several questions in our understanding of mitochondria are unanswered. These include how the ratio of mitochondrial (mt)DNA to mitochondria is maintained, how the accumulation of defective, rapidly replicating mitochondrial DNA is avoided, how the ratio of mitochondria to cells is adjusted to fit cellular needs, and why any proteins are synthesized in mitochondria rather than simply imported. In bacteria, large hyperstructures or assemblies of proteins, mRNA, lipids and ions have been proposed to constitute a level of organization intermediate between macromolecules and whole cells. Here, we suggest how the concept of hyperstructures together with other concepts developed for bacteria such as transcriptional sensing and spontaneous segregation may provide answers to mitochondrial problems. In doing this, we show how the problem of the very existence of mtDNA brings its own solution.

Metabolic Network Reconstruction and Their Topological Analysis

This chapter focuses on the way to build a metabolic network and how to analyze its structure. The first part of this chapter describes the methods of the network model reconstruction from biochemical data found in specialized databases and/or literature. The second part deals with metabolic pathway analysis as a useful tool for better understanding the complex architecture of intracellular metabolism. The graph analysis and the stoichiometric network analysis are important approaches for understanding the network topology and consequently the function of metabolic networks. Among the methods presented, the Elementary Flux Modes analysis will be more detailed. Finally, we illustrate in this chapter an example of network reconstruction from heterotrophic plant cells metabolism and its topological analysis leading to a huge number of Elementary Flux Modes.

Formal TCA cycle description based on elementary actions

Many databases propose their own structure and format to provide data describing biological processes. This heterogeneity contributes to the difficulty of large systematic and automatic functional comparisons. To overcome these problems, we have used the Bio formal description scheme which allows multi-level representations of biological process information. Applied to the description of the tricarboxylic acid cycle (TCA), we show that Bio allows the formal integration of functional information existing in current databases and make them available for further automated analysis. In addition such a formal TCA cycle process description leads to a more accurate biological process annotation which takes in account the biological context. This enables us to perform an automated comparison of the TCA cycles for seven different species based on processes rather than protein sequences. From current databases, Bio is able to unravel information that are already known by the biologists but are not available for automated analysis tools and simulation software, because of the lack of formal process descriptions. This use of the Bio description scheme to describe the TCA cycle was a key step of the MitoScop project that aims to describe and simulate mitochondrial metabolism in silico.

WPC test based on randomization for analyzing quantitative traits on simulated pedigrees

The weighted pairwise correlation (WPC) approach provides simple and flexible tests for genetic linkage which may be adapted to qualitative, quantitative or age‐dependent traits. These tests also seem to have good power. However, when working with large pedigrees, a disease susceptibility gene not linked to the marker studied induces correlations of the trait values, leading to inflated type I errors for these tests. We propose here a new approach for inference based on the randomization of the alleles following the Mendelian laws and conditioning on the alleles of the founders. This approach is applied to the analysis of the quantitative traits in a set of simulated pedigrees. The a posteriori comparison of the findings to the true model indicates directions for future work.

Study of CNN Based Classification for Small Specific Datasets

Recently, deep learning and particularly, Convolutional Neural Network (CNN), has become predominant in many application fields, including visual image classification. In an applicative context of detecting areas with hazard of dengue fever, we propose a classification framework using deep neural networks on a limited dataset of images showing urban sites. For this purpose, we have to face multiple research issues: (i) small number of training data; (ii) images belonging to multiple classes; (iii) non-mutually exclusive classes. Our framework overcomes those issues by combining different techniques including data augmentation and multi-scale/region-based classification, in order to extract the most discriminative information from the data. Experiment results present our framework performance using several CNN architectures with different parameter sets, without and with transfer learning. Then, we analyze the effect of data augmentation and multiscale region based classification. Finally, we show that adding a classification weighting scheme allows the global framework to obtain more than 90% average precision for our classification task.

TIM/WIM: a set of tools to interface modelling in biology

Modelling in biology is a complex task. Many types of information are used by biologists but there is a lack of tools for integrating heterogeneous data in a core interface. TIM (Tools to Input Models) is a tool which allows to put in the same interface data which describe biological objects like enzymes, metabolites, DNA, ... and information about biological process modelling like those coming from NMR (Nuclear Magnetic Resonance) experiments and used to simulate flux through metabolic networks. TIM is able to manage the widely used database format, PGDB (BioCyc format), and uses a large part of a biological ontology, BioPAX format, to store information about biological processes. To manage all these data, WIM (Web Interface for Modelling) provides a set of web pages. The WIM package generates dynamic html sources using CL-WHO library and integrates an Hunchen-toot web server. At present, the application is used by a biologist group to store data about the carbon metabolism of the tomato fruit. They have released the first version of the TomaCyc database and currently used the WIM interface to create automatically input files for softwares that simulate the activity of their metabolism network.

Classification des modes élémentaires.

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