Michael Defoin-Platel

Glycosyl transferases in family 61 mediate arabinofuranosyl transfer onto xylan in grasses

Xylan, a hemicellulosic component of the plant cell wall, is one of the most abundant polysaccharides in nature. In contrast to dicots, xylan in grasses is extensively modified by α-(1,2)– and α-(1,3)–linked arabinofuranose. Despite the importance of grass arabinoxylan in human and animal nutrition and for bioenergy, the enzymes adding the arabinosyl substitutions are unknown. Here we demonstrate that knocking-down glycosyltransferase (GT) 61 expression in wheat endosperm strongly decreases α-(1,3)–linked arabinosyl substitution of xylan. Moreover, heterologous expression of wheat and rice GT61s in Arabidopsis leads to arabinosylation of the xylan, and therefore provides gain-of-function evidence for α-(1,3)-arabinosyltransferase activity. Thus, GT61 proteins play a key role in arabinoxylan biosynthesis and therefore in the evolutionary divergence of grass cell walls.

2009 Special Issue: Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models

This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naïve Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

New perspectives on glutamine synthetase in grasses

Members of the glutamine synthetase (GS) gene family have now been characterized in many crop species such as wheat, rice, and maize. Studies have shown that cytosolic GS isoforms are involved in nitrogen remobilization during leaf senescence and emphasized a role in seed production particularly in small grain crop species. Data from the sequencing of genomes for model crops and expressed sequence tag (EST) libraries from non-model species have strengthened the idea that the cytosolic GS genes are organized in three functionally and phylogenetically conserved subfamilies. Using a bioinformatic approach, the considerable publicly available information on high throughput gene expression was mined to search for genes having patterns of expression similar to GS. Interesting new hypotheses have emerged from searching for co-expressed genes across multiple unfiltered experimental data sets in rice. This approach should inform new experimental designs and studies to explore the regulation of the GS gene family further. It is expected that understanding the regulation of GS under varied climatic conditions will emerge as an important new area considering the results from recent studies that have shown nitrogen assimilation to be critical to plant acclimation to high CO(2) concentrations.

ON THE PROBABILISTIC OPTIMIZATION OF SPIKING NEURAL NETWORKS

The construction of a Spiking Neural Network (SNN), i.e. the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary Algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces simultaneously appear to be the most promising ones. A variety of such heterogeneous optimization algorithms have emerged recently, in particular in the field of probabilistic optimization. In this paper, a literature review on heterogeneous optimization algorithms is presented and an example of probabilistic optimization of SNN is discussed in detail. The paper provides an experimental analysis of a novel Heterogeneous Multi-Model Estimation of Distribution Algorithm (hMM-EDA). First, practical guidelines for configuring the method are derived and then the performance of hMM-EDA is compared to state-of-the-art optimization algorithms. Results show hMM-EDA as a light-weight, fast and reliable optimization method that requires the configuration of only very few parameters. Its performance on a synthetic heterogeneous benchmark problem is highly competitive and suggests its suitability for the optimization of SNN.

Integrated feature and parameter optimization for an evolving spiking neural network

This study extends the recently proposed Evolving Spiking Neural Network (ESNN) architecture by combining it with an optimization algorithm, namely the Versatile Quantum-inspired Evolutionary Algorithm (vQEA). Following the wrapper approach, the method is used to identify relevant feature subsets and simultaneously evolve an optimal ESNN parameter setting. Applied to carefully designed benchmark data, containing irrelevant and redundant features of varying information quality, the ESNN-based feature selection procedure lead to excellent classification results and an accurate detection of relevant information in the dataset. Redundant and irrelevant features were rejected successively and in the order of the degree of information they contained.

Systems Responses to Progressive Water Stress in Durum Wheat

Durum wheat is susceptible to terminal drought which can greatly decrease grain yield. Breeding to improve crop yield is hampered by inadequate knowledge of how the physiological and metabolic changes caused by drought are related to gene expression. To gain better insight into mechanisms defining resistance to water stress we studied the physiological and transcriptome responses of three durum breeding lines varying for yield stability under drought. Parents of a mapping population (Lahn x Cham1) and a recombinant inbred line (RIL2219) showed lowered flag leaf relative water content, water potential and photosynthesis when subjected to controlled water stress time transient experiments over a six-day period. RIL2219 lost less water and showed constitutively higher stomatal conductance, photosynthesis, transpiration, abscisic acid content and enhanced osmotic adjustment at equivalent leaf water compared to parents, thus defining a physiological strategy for high yield stability under water stress. Parallel analysis of the flag leaf transcriptome under stress uncovered global trends of early changes in regulatory pathways, reconfiguration of primary and secondary metabolism and lowered expression of transcripts in photosynthesis in all three lines. Differences in the number of genes, magnitude and profile of their expression response were also established amongst the lines with a high number belonging to regulatory pathways. In addition, we documented a large number of genes showing constitutive differences in leaf transcript expression between the genotypes at control non-stress conditions. Principal Coordinates Analysis uncovered a high level of structure in the transcriptome response to water stress in each wheat line suggesting genome-wide co-ordination of transcription. Utilising a systems-based approach of analysing the integrated wheat’s response to water stress, in terms of biological robustness theory, the findings suggest that each durum line transcriptome responded to water stress in a genome-specific manner which contributes to an overall different strategy of resistance to water stress.

Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis

BackgroundCombining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems.ResultsWe have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in Arabidopsis thaliana. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters.ConclusionsRelationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.

Clock-constrained tree proposal operators in Bayesian phylogenetic inference

Bayesian Markov chain Monte Carlo (MCMC) has become one of the principle methods of performing inference of phylogenetic trees. The MCMC algorithm requires the definition of a transition kernel over the state space, which depends on tree proposal operators. So, the precise form of these operators has a large impact on the computational efficiency of the algorithm. In this paper we investigate the efficiency of different tree proposals specialized on clock-constrained phylogenetic trees. Two new operators are developed and their efficiency is compared to five standard operators. Each of the seven operators is tested individually on three synthetic datasets and eleven real datasets. In addition, the single operators are compared to different mixtures of operators. Results show that our new operators perform better than their standard counterparts, but no operator alone achieved a high efficiency on the full panel of data sets tested. Finally, our new proposed mixture using all operators together provides better performance than current techniques.

Handling missing features with boosting algorithms for protein-protein interaction prediction

Combining information from multiple heterogeneous data sources can aid prediction of protein-protein interaction. This information can be arranged into a feature vector for classification. However, missing values in the data can impact on the prediction accuracy. Boosting has emerged as a powerful tool for feature selection and classification. Bayesian methods have traditionally been used to cope with missing data, with boosting being applied to the output of Bayesian classifiers. We explore a variation of Adaboost that deals with the missing values at the level of the boosting algorithm itself, without the need for any density estimation step. Experiments on a publicly available PPI dataset suggest this overall simpler and mathematically coherent approach may be more accurate.

Analyzing the dynamics of the simultaneous feature and parameter optimization of an evolving Spiking Neural Network

This study investigates the characteristics of the Quantum-inspired Spiking Neural Network (QiSNN) feature selection and classification framework. The self-adapting nature of QiSNN due to the simultaneous optimization of network parameters and feature subsets represents a highly desirable characteristic in the context of machine learning and knowledge discovery. In this paper, the evolution of the parameters and feature subsets is studied in detail. The goal of this analysis is a comprehensive understanding of all parameters involved in QiSNN and some practical guidelines for using the method in future research and applications. We also highlight the role of the employed neural encoding technique along with its impact on the classification abilities of QiSNN.