Nabil Semmar

University of Tunis El Manar, Tunis-Ville, Tūnis, Tunisia

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Publications (28)46.67 Total impact

  • Nabil Semmar · Jacques Artaud
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    ABSTRACT: Olive oils represent complex matrices varying from pure to heterogeneous varietal contents. Quantitative analysis of co-occurring components is fundamental for conformity checking and adulteration alerting (fighting) of commercial oils. Proportions of co-occurring components are governed by additive-dilutive processes which obey to simplex rule. Using simplex rule, we developed an original computational approach to predict proportions of different co-occurring oil varieties from quantitative chemical features of blends. The approach consisted in applying a complete set of N mixtures between different olive oil varieties by gradually varying their proportions. The N simulated mixtures were characterized by N average fatty acid (FA) profiles calculated from N combinations of randomly sampled individual profiles. After k iterations of the mixture design, the k sets of N FA average profiles were used as input in a discriminant analysis to predict proportions of co-occurring olive oil varieties in different blends. Illustrative application concerned blends made by three main French mono-varietal virgin olive oils (Aglandau, Grossane and Salonenque) and benefiting from Protected Designation of Origin label. Predictive model was validated on outside blends and showed prediction errors with an order of 10% susceptible of reduction by applying a larger mixture design.
    No preview · Article · Nov 2015 · Journal of Food Composition and Analysis
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    ABSTRACT: A total of 54 lactic acid bacteria (LAB) were isolated from stored wheat samples sourced from grain silos in North Tunisia. Fifteen representative isolates were identified by 16S rDNA sequencing as Pediococcus pentosaceus, Lactobacillus plantarum, Lactobacillus graminis, Lactobacillus coryniformis and Weissella cibaria. These isolates were screened for antifungal activity in dual culture agar plate assay against eight post-harvest moulds (Penicillium expansum, Penicillium chrysogenum, Penicillium glabrum, Aspergillus flavus, Aspergillus niger, Aspergillus carbonarius, Fusarium graminearum and Alternaria alternata). All LAB showed inhibitory activity against moulds, especially strains of L. plantarum which exhibited a large antifungal spectrum. Moreover, LAB species such as L plantarum LabN10, L. graminis LabN11 and P. pentosaceus LabN12 showed high inhibitory effects against the ochratoxigenic strain A. carbonarius ANC89. These LAB were also investigated for their ability to reduce A. carbonarius ANC89 biomass and its ochratoxin A (OTA) production on liquid medium at 28 and 37 degrees C and varied pH conditions. The results indicated that factors such as temperature, pH and bacterial biomass on mixed cultures, has a significant effect on fungal inhibition and OTA production. High percentage of OTA reduction was obtained by L. plantarum and L. graminis (>97%) followed by P. pentosaceus (>81.5%). These findings suggest that in addition to L. plantarum, L graminis and P. pentosaceus strains may be exploited as a potential OTA detoxifying agent to protect humans and animals health against this toxic metabolite.
    Full-text · Article · Sep 2014 · Biological Control
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    ABSTRACT: Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals with different chemical shifts and intensities representing different metabolites' types and levels, respectively. Relevant predictive NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The different PCs representing the (n - 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation). From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA) were used to build final population and sub-population regression models predicting metabolite levels in BM-B.
    Full-text · Article · May 2014 · Current Drug Metabolism
  • Nabil Semmar · Maurice Roux
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    ABSTRACT: Stomach contents represent complex mixture systems which depend on feeding mode of forager species (carnivores, herbivores) as well as on natural availability/distribution of food resources (preys, plants). Such mixture systems can be considered as small black boxes condensing wide ecological information on (i) feeding behaviors of predator (or herbivore) and (ii) local diversity of preys (or host plants). Feeding behaviors of a hunter species toward different prey taxa represent a complex variability system whose investigation requires multivariate statistical tools. This paper presents a new computational approach which statistically analyzes stomach contents' variability in a predator population leading to graphically highlight different feeding behaviors. This simulation approach is based on iterated combinations between different diet patterns by using a simplex mixture design. Average combinatorial results are graphically visualized to highlight scale-dependent relationships between consumption rates of different food types found in the stomachs. The simplex approach was applied on different subpopulations of Phrynosoma douglassi brevirostre, an insectivore lizard species. These subpopulations were initially defined by different criteria including statistical clusters, gender and sampling periods. Results highlighted successive trade-offs over months of captured potential preys switching from small and less mobile preys to large and flying ones. In these dietary transitions, P. douglassi manifested a systematic memorization of previous preys and a gradual foraging learning of the next ones. This highlighted lightness on dietary flexibility helping this specialist predator to switch between different potential preys-based diets. Adult male and adult female lizards showed different feeding behaviors due to some predation lag-time between them and different dietary ratios toward the same considered preys.
    No preview · Article · Mar 2014 · Bio Systems
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    ABSTRACT: Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals with different chemical shifts and intensities representing different metabolites' types and levels, respectively. Relevant predictive NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The different PCs representing the (n - 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation). From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA) were used to build final population and sub-population regression models predicting metabolite levels in BM-B
    Full-text · Article · Jan 2014
  • Nabil Semmar · Maurice Roux

    No preview · Article · Jan 2014
  • Nabil Semmar
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    ABSTRACT: The metabolism is a complex system interacting with several intrinsic and extrinsic factors of biological organisms, viz. genome expressions, physiological states, environmental conditions, etc. This multifactorial interaction means that the metabolism works as a reactive and flexible system providing reliable biochemical pictures on the effects of different governing factors. Metabolic flexibility and reliability are linked to conservation laws, constraining the metabolism to a close system linking input (resources) to output (products) signals: any entering signal will be decomposed into weighted parts through different metabolic pathways. This gives to metabolic trends different functional degrees highlighted by different relative levels of metabolites. Sharing the same unit resource, the different metabolic pathways are statistically constrained to be regulated within a simplex space characterized by a unit sum of its components. Output metabolic responses and their inside regulatory processes can be analyzed by using two simplex-based approaches: correspondence analysis (CA) and weighted metabolic profiles analysis (WMPA), respectively. These two approaches are based on two opposite (complementary) principles consisting of decomposition and combination of metabolic variability. In CA, metabolic datasets are decomposed into extreme trends representing elementary components of metabolic polymorphism called metabotypes. In WMPA, iterated combinations between different metabolic components help to extract functional information on their generator backbone system.
    No preview · Chapter · Dec 2013
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    ABSTRACT: Wheat represents a principal ingredient in traditional Tunisian diet including couscous, bread, pasta and biscuits. Northen Tunisia is an important growing area of wheat which after harvest is stored in silos and on farm. The cereal grains can become contaminated by post-harvest moulds during storage in silos under unfavorable conditions leading to a decrease in quality, packing and marketing of wheat. In this study, a mycological survey was undertaken to determine the biodiversity of post-harvest moulds on durum wheat stored in silos localized in five regions of Northern Tunisia and to investigate changes during the storage period. A total of 127 samples were obtained from Oued Mliz, Jendouba, Ksar Mezouar, Mateur and Ghezala silos during 2010–2011 and 2011–2012 wheat seasons. After sampling, seeds were placed on Potato Dextrose Agar medium (PDA) for 7 days of incubation at 28 °C. A total of 6035 strains of filamentous fungi were isolated. The quantitative and qualitative changes on wheat mycoflora during storage were statistically explored by multivariate methods including correspondence and hierarchical cluster analysis. The most predominant post-harvest moulds genera isolated were Alternaria (28%), Fusarium (19%), Penicillium (19%), Aspergillus (14%), Mucor (8%) and Rhizopus (7%). Various genera of fungi imperfecti, including Ulocladium, Geotrichum, Chaetomium, Trichothecium, Paecilomyces, Aureobasidium and Chrysonilia (anamorphic Neurospora), and the Mucorales genera Lichtheiia and Syncephalastrum accounted for the remainder of about 6% of the total. Statistical data analysis revealed six mycological patterns corresponding to six distinct communities as characterized by the prevalence of different moulds. Such patterns clearly showed different spatio-temporal variability indicating that distribution and evolution of moulds during storage was sensitive to geographic location, year of sampling and short or long-term storage.
    Full-text · Article · Oct 2013 · Journal of Stored Products Research
  • N. Semmar
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    ABSTRACT: "Native Statistics for Natural Sciences" is a book which presents step-by-step several complementary and chained statistical tools. These tools are applied to analyze structures and variability of natural systems helping to gradually understand and control their complexity. The book is organized into a serial of chapters which are extensively illustrated by intuitive figures and simple numerical examples. Statistics represent a large field of applied mathematics aiming to extract and analyze information from sampled data issued from complex systems or populations. Extraction of reliable information on systems requires a priori the application of strategic rules by which intrinsic variability and extrinsic limits are considered. Such strategic rules are given by sampling designs and experimental designs which are applied for open and close systems, respectively. Sampling designs presented in this book include simple random, systematic and stratified designs which are applied to estimate and control variability in open systems having different organizations or distributions. Moreover, sampling designs are appropriate tools for later biodiversity and spatiotemporal analyzes of natural systems. Experimental designs include factorial, response surface and mixture designs which are specifically applied to control systems defined by different geometrical structures. Such geometrical structures have different dimension defined by strategic values of experimental factors which could have potential effects on the studied system. After collect of reliable experimental data by means of sampling or experimental designs, population structures, variability and working will be analyzed in different methodological steps aiming at description, comparison and prediction of quantitative or qualitative states of the studied system. Descriptive statistics aim at estimation of the unknown central and peripheral characteristics of studied system. This is carried out by means of several calculated parameters including position, dispersion, precision and shape parameters. In addition to the numerical parameters, complex system structure can be described by means of different types of graphics helping to visualize the distribution shape of the whole population. Graphical representations of sampled data include histogram, box-plot, bar, pie and stacked columns charts. After the descriptive step, the summarized information of studied system can be compared to some reference values or to other systems. For statistical comparisons, hypothesis tests are applied using theoretical probability distributions from which the states of sampled data are concluded to be original or ordinary with well-defined error risks (or doubt levels). Hypothesis tests can be of parametric or nonparametric type and give conclusions about significant and not significant differences by reference to cut-off values provided by appropriate probability distributions. Theoretical probability distributions presented in this book include the normal, Student, Chi-2, Fisher, binomial, Poisson, hypergeometric and Pascal laws. These reference laws are used according to the quantitative or qualitative types of the sample data. In a next statistical step, system controllability can be reached by applying link analysis between different descriptive variables. Such analyzes help to (i) detect significant factor(s) influencing system states, and (ii) formulate relationships predicting system variables in relation to intrinsic or extrinsic factors. Link analyses presented in this book include analysis of variance, linear regression and independency Chi-2 test.
    No preview · Article · Jan 2013
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    Nabil Semmar

    Preview · Chapter · Feb 2012
  • N. Semmar · A.H. Semmar
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    ABSTRACT: Laboratory experiments need to be organized and applied by ways helping (i) to control studied system variability, and (ii) to reach some desired system characteristics by (iii) determining their favoring experimental conditions. These different objectives can be reliably reached by applying appropriate experiment designs (EDs). Statistically, EDs consist of matrices giving well defined numbers (n) of experimental points characterized by some strategic coordinates of k control factors. The type of factors influences directly the choose of appropriate ED. Strategic coordinates of the n points result in complementarity between points leading to cover a whole experimental field that is well characterized under geometric and numeric aspects. Geometric and numeric characteristics of EDs are fundamental to experiment control, modeling and optimization. ED Geometries are well structured spaces (with well-shaped hulls) covering the whole studied field and defining its reachability (validation) limits. Numerically, an experimental design can be characterized and compared to other EDs by means of experiment matrix. From experiment matrix, several calculations can be carried out to (i) extract more properties of experimental system a priori (before laboratory experiments), and to (ii) mathematically model a system response in relation to control factors a posteriori (after laboratory experiments). Beyond the development of predictive response models, EDs can be applied to analyze different variation-trajectories leading to different desired values of a studied response. These trajectories can be visualized by means of iso-response curves given by mathematical models of measured response in relation to the factors taken into account by the experiment matrix. The diversity of EDs makes possible to use different appropriate experiment matrices in relation to (i) the type of studied system and (ii) study aim. Study aims can be classified into three complementary ways consisting in screening, modeling and optimizing a system response in relation to some considered factors. Thus, EDs provide strong tools to sequential system analysis. Also, ED diversity covers different approaches to more or less precise system analyses by (i) considering different factor types (e.g. independent or dependent, variable or fixed, continuous or discrete), and by (ii) bringing solutions to different constraints including management of high factor number, reduction of high experiment costs and consideration of filed reachability conditions. This chapter illustrates the geometric and numeric principles as well as the applications of several EDs including factorial, fractional, composite, Box-Behnken, Doehlert and Scheffé's simplex designs.
    No preview · Article · Jan 2012
  • N. Semmar
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    ABSTRACT: Metabolism represents a complex system characterized by a high variability in metabolites' structure, concentration and regulation ratio. Such a variability is observed at different metabolic scales going from metabolites to metabolic profiles via chemical reactions and metabolic pathways, as well as under static or dynamic aspects. Variations in these components are due to apparition-disappearance, level increase-decrease and/or relative changes in weights or contributions leading to different structural, functional and evolutive states of the metabolic system. This book presents a variety of different computational approaches of the variability in metabolic systems.
    No preview · Article · Jan 2011
  • Yassine Mrabet · Nabil Semmar
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    ABSTRACT: Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.
    No preview · Article · May 2010 · Current Drug Metabolism
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    ABSTRACT: Two new tridesmosidic glycosides of (3β,6α,16β,20R,24S)-20,24-epoxycycloartane-3,6,16,25-tetrol (=cycloastragenol), armatosides I and II (1 and 2, resp.), were isolated from the roots of Astragalus armatus (Fabaceae) as well as the known bidesmosidic glycosides of cycloastragenol, trigonoside II (3) and trojanoside H (4). Their structures were elucidated as (3β,6α,16β,20R,24S)-3-O-(2,3-di-O-acetyl-β-D-xylopyranosyl)-20,24-epoxy-25-O-β-D-glucopyranosyl-6-O-β-D-xylopyranosylcycloartane-3,6,16,25-tetrol (1), and (3β,6α,16β,20R,24S)-3-O-(2-O-acetyl-β-D-xylopyranosyl)-20,24-epoxy-25-O-β-D-glucopyranosyl-6-O-β-D-xylopyranosylcycloartane-3,6,16,25-tetrol (2). These structures were established by extensive NMR and MS analyses and by comparison with literature data.
    No preview · Article · May 2010 · Helvetica Chimica Acta
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    Nabil Semmar
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    ABSTRACT: The flexibility of metabolic systems implies a high variability of metabolic profiles linked to different regulation ratios between metabolites. Such regulations are controlled by several interactive metabolic pathways resulting in multidirectional continuums of metabolic profiles. This article presents a new metabolomic approach helping to graphically analyse the flexibility of metabolic regulation systems. Its principle consists in extracting a metabolic backbone from iterative combinations of metabolic profiles representing different metabolic trends. The iterated combinations were performed on the basis of Scheffe matrix then averaged to calculate a response matrix of smoothed metabolic profiles. From such a smoothed matrix, a graphical analysis of relationships between metabolites highlighted different scale-dependent variation paths responsible for the observed metabolic trends. Such a flexibility favouring some metabolites at the expense of others was indirectly checked by a single kinetic approach by considering both the variation of maximal concentrations and the metabolic trends in time. This kinetic approach highlighted a succession of metabolic trends linked to the variation of maximal concentrations in time. Finally, a delayed regulation of a metabolite was highlighted both by the kinetic approach and by a dynamic application of the metabolomic approach. This new approach was illustrated on a dataset of blood concentrations of levodopa and its metabolites analysed in 34 patients at different times.
    Preview · Article · Jan 2010 · Chemical Biology & Drug Design
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    ABSTRACT: Population pharmacokinetic (PK) (or pharmacodynamic (PD)) modelling aims to analyse the variability of drug kinetics (or dynamics) between numerous subjects belonging to a population. Such variability includes inter- and intra-individual sources leading to important differences between the variation ranges, the relative concentrations and the global shapes of PK profiles. These various sources of variability suggest that the distance metrics between the subjects can be examined under different aspects. Some subjects are so distant from the majority that they tend to be atypical or outliers. This paper presents three multivariate statistical methods to diagnose the outliers within a full population PK dataset, prior to any modelling step. Each method combined all the concentration-time variables to analyse the differences between patients by referring to a distance criterion: (a) Correspondence analysis (CA) used the chi-square distance to highlight the most atypical profiles in terms of relative concentrations; (b) Mahalanobis distance was calculated to extract PK profiles showing atypical shapes due to atypical variations in concentration; (c) Andrews method combined all the concentration variables into a Fourier transformation to give sine-cosine curves showing the clustering behaviours of subjects under the Euclidean distance criterion. After identification of outlier subjects, these methods can also be used to extract the concentration values which cause the atypical states of the patients. Therefore, the outliers will incorporate different variability sources of the PK dataset according to each method and independently of any PK modelling. Finally, a significant positive trend was found between the number of times outlier concentrations were detected (by one, two or three diagnostics) and the NPDE metrics of these concentrations (after a PK modelling): NPDE were highest when the corresponding concentration was detected by more diagnostics a priori. The application of a priori outlier diagnostics is illustrated here on two PK datasets: stimulated cortisol by synacthen and capecitabine administrated orally.
    No preview · Article · May 2008 · Journal of Pharmacokinetics and Pharmacodynamics
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    Nabil Semmar · Maurice Jay · Muhammad Farman · Maurice Roux
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    ABSTRACT: The quantitative assessment of plant diversity and its monitoring with time represent a key environmental issue for management and conservation of natural resources. Assessment of plant diversity could be based on chemical analyses of secondary metabolites (e.g. flavonoids, terpenoids), because of the substantial quantitative and qualitative between-individual variability in such compounds. At a geographical scale, the plant populations become widely dispersed, and their monitoring from numerous routine individual analyses could become restricting. To overcome such constraint, this study develops a multivariate calibration model giving the relative frequency of a particular taxon from a simple high-performance liquid chromatography (HPLC) analysis of a plant mixture. The model was built from a complete set of mixtures combining different taxons, according to an experimental design (Scheffé’s matrix). For each mixture, a reference HPLC pattern was simulated by averaging the individual HPLC profiles of the constitutive taxons. The calibration models, based on Bayesian discriminant analysis (BDA), gave statistical relationships between the contributions of each taxon in mixtures and reference HPLC patterns of these mixtures. Finally, these models were validated on new mixtures by using outside plants. This new biodiversity survey approach is illustrated on four chemical taxons (four chemotypes) of Astragalus caprinus (Fabaceae). The more differentiated the taxon, the better predicted its contributions (in mixtures) were by BDA calibration model. This new approach could be very useful for a global routine survey of plant diversity.
    Preview · Article · Feb 2008 · Environmental Modeling and Assessment
  • Nabil Semmar · Maurice Jay · Saïd Nouira
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    ABSTRACT: HPLC analysis of secondary metabolites represents an efficient tool for the studying of plant chemical diversity under different aspects: chemotaxonomy, metabolomics, adaptative responses to ecological factors, etc. Statistical analyses of HPLC databases, e.g. correlation analysis between HPLC peaks, can reliably provide information on the similarity/dissimilarity degrees between the chemical compounds. The similarities, corresponding to positive correlations, can be interpreted in terms of analogies between chemical structures, synchronic metabolisms or co-evolution of two compounds under certain environment conditions, etc. . In terms of metabolism, positive correlations can translate precursor-product relationships between compounds; negative correlations can be indicative of competitive processes between two compounds for a common precursor(s), enzyme(s) or substrate(s). Furthermore, the correlation analysis under a metabolic aspect can help to understand the biochemical origins of an observed polymorphism in a plant species. With the aim of showing this, we present a new approach based on a simplex mixture design, Scheffé matrix, which provides a correlation network making it possible to graphically visualise and to numerically model the metabolic trends between HPLC peaks. The principle of the approach consisted in mixing individual HPLC profiles representative of different phenotypes, then from a complete mixture set, a series of average profiles were calculated to provide a new database with a small variability. Several iterations of the mixture design provided a smoothed final database from which the relationships between the secondary metabolites were graphically and numerically analysed. These relationships were scale-dependent, namely either deterministic or systematic: the first consisted of a monotonic global trend covering the whole variation field of each metabolites’ pair; the second consisted of repetitive monotonic variations which gradually attenuated or intensified along a global trend. This new metabolomic approach was illustrated from 404 individual plants of Astragalus caprinus (Leguminoseae), belonging to four chemical phenotypes (chemotypes) on the basis of flavonoids analysed in their leaves. After smoothing, the relationships between flavonoids were numerically fitted using linear or polynomial models; therefore the co-response coefficients were easily interpreted in terms of metabolic affinities or competitions between flavonoids which would be responsible of the observed chemical polymorphism (the four chemotypes). The statistical validation of the approach was carried out by comparing Pearson correlations to Spearman correlations calculated from the smoothed and the crude HPLC database, respectively. Moreover, the signs of the smoothed relationships were finely supported by analogies and differences between the chemical structures of flavonoids, leading to fluent interpretation in relation to the pathway architecture.
    No preview · Article · Aug 2007 · Chemoecology
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    ABSTRACT: A novel oleanane-type triterpene saponin (1) together with two known molecules, soyasapogenol B and astragaloside VIII were isolated from the roots of Astragalus caprinus. Their structural elucidation was performed mainly by 2D NMR techniques (COSY, TOCSY, NOESY, HSQC, HMBC) and mass spectrometry. Compound 1 was determined as 3-O-[alpha-L-rhamnopyranosyl-(1 --> 2)-beta-D-glucuronopyranosyl]-22-O-beta-D-apiofuranosyl-soyasapogenol B.
    No preview · Article · Jul 2006 · Magnetic Resonance in Chemistry
  • Nabil Semmar · Nicolas Simon
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    ABSTRACT: The pharmacokinetics of corticosteroids provides a large set of mathematical models which led to analyse many kinetic profiles corresponding to many clinical and/or physiological situations. In this paper, we present a review on the usefulness, advantages and limits of such models which could find a large application in medicinal chemistry.
    No preview · Article · May 2006 · Mini Reviews in Medicinal Chemistry

Publication Stats

193 Citations
46.67 Total Impact Points

Institutions

  • 2013-2015
    • University of Tunis El Manar
      • Higher Institute of Applied Biological Sciences
      Tunis-Ville, Tūnis, Tunisia
  • 2001-2010
    • University of Burgundy
      • Unité de Molécules d’Intérêt Biologique (UMIB)
      Dijon, Bourgogne, France
  • 2001-2006
    • Claude Bernard University Lyon 1
      • Laboratoire de chimie
      Villeurbanne, Rhône-Alpes, France
  • 2002
    • Quaid-i-Azam University
      • Department of Chemistry
      Islāmābād, Islāmābād, Pakistan