Biomedical subjects

L M Buydens

Publications and source records attributed to L M Buydens.

10 recordsLinked to original sources

PubMed2001

A procedure for incorporating spatial variability in ecological risk assessment of Dutch river floodplains.

Floodplain soils along the river Rhine in the Netherlands show a large spatial variability in pollutant concentrations. For an accurate ecological risk characterization of the river floodplains, this heterogeneity has to be included into the ecological risk assessment. In this paper a procedure is presented that incorporates spatial components of exposure into the risk assessment by linking geographical information systems (GIS) with models that estimate exposure for the most sensitive species of a floodplain. The procedure uses readily available site-specific data and is applicable to a wide range of locations and floodplain management scenarios. The procedure is applied to estimate exposure risks to metals for a typical foodweb in the Afferdensche and Deestsche Waarden floodplain along the river Waal, the main branch of the Rhine in the Netherands. Spatial variability of pollutants is quantified by overlaying appropriate topographic and soil maps resulting in the definition of homogeneous pollution units. Next to that, GIS is used to include foraging behavior of the exposed terrestrial organisms. Risk estimates from a probabilistic exposure model were used to construct site-specific risk maps for the floodplain. Based on these maps, recommendations for future management of the floodplain can be made that aim at both ecological rehabilitation and an optimal flood defense.

Animals↗

PubMed2000

Automatic correction for phase shifts, frequency shifts, and lineshape distortions across a series of single resonance lines in large spectral data sets.

A new model-free method is presented that automatically corrects for phase shifts, frequency shifts, and additional lineshape distortions of one single resonance peak across a series of in vivo NMR spectra. All separate phase and frequency variations are quickly and directly derived from the common lineshape in the data set using principal component analysis and linear regression. First, the new approach is evaluated on simulated data in order to quantitatively assess the phase and frequency shifts which can be removed by the proposed correction procedure. Subsequently, the value of the method is demonstrated on in vivo (31)P NMR spectra from skeletal muscle of the hind leg of the mouse focusing on the phosphocreatine resonance which is distorted by the experimental procedure. Phase shifts, frequency shifts, and lineshape distortions with respect to the common lineshape in the spectral data set could successfully be removed.

Animals↗

PubMed1999

Using genetic algorithms for the construction of phylogenetic trees: application to G-protein coupled receptor sequences.

Many different phylogenetic clustering techniques are used currently. One approach is to first determine the topology with a common clustering method and then calculate the branch lengths of the tree. If the resulting tree is not optimal exchanging tree branches can make some local changes in the tree topology. The whole process can be iterated until a satisfactory result has been obtained. The efficiency of this method fully depends on the initially generated tree. Although local changes are made, the optimal tree will never be found if the initial tree is poorly chosen. In this article, genetic algorithms are applied such that the optimal tree can be found even with a bad initial tree topology. This tree generating method is tested by comparing its results with the results of the FITCH program in the PHYLIP software package. Two simulated data sets and a real data set are used.

Algorithms↗

PubMed1998

Predicting nucleic acid torsion angle values using artificial neural networks.

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles, chi, zeta and alpha from torsion angles delta, epsilon, beta and gamma for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed. About 70 to 80% of the residuals for predicted torsion angles are smaller than 10 degrees. This means that such a model can be used to construct trial structures for conformational analysis that can be refined further. Moreover, when reasonable estimates for delta, epsilon, beta and gamma are extracted from COSY experiments, this procedure can easily be extended to predict torsion angles for structures in solution.

Base Sequence↗

PubMed1997

Reinvestigation of a genetic-based classifier system: the effectiveness of recombination.

An empirical study for the effectiveness of recombination in a genetic-based classifier system applied to the field of ion chromatography is presented. From a comparison of the classifier system with and without crossover it followed that recombination was unable to make a significant contribution to the classification results. Despite this ineffectiveness, the genetic algorithm was a legitimate choice for solving the present classification problem because its population-based properties were of greater importance than the issue of whether or not recombination significantly added to the performance. These findings prompt the testing of other classifier systems as well, in order to reveal the extent to which the presented results can be classified.

Algorithms↗

PubMed1997

Application of a genetic algorithm in the conformational analysis of methylene-acetal-linked thymine dimers in DNA: comparison with distance geometry calculations.

The three-dimensional spatial structure of a methylene-acetal-linked thymine dimer present in a 10 basepair (bp) sense-antisense DNA duplex was studied with a genetic algorithm designed to interpret NOE distance restraints. Trial solutions were represented by torsion angles. This means that bond angles for the dimer trial structures are kept fixed during the genetic algorithm optimization. Bond angle values were extracted from a 10 bp sense-antisense duplex model that was subjected to energy minimization by means of a modified AMBER force field. A set of 63 proton-proton distance restraints defining the methylene-acetal-linked thymine dimer was available. The genetic algorithm minimizes the difference between distances in the trial structures and distance restraints. A large conformational search space could be covered in the genetic algorithm optimization by allowing a wide range of torsion angles. The genetic algorithm optimization in all cases led to one family of structures. This family of the methylene-acetal-linked thymine dimer in the duplex differs from the family that was suggested from distance geometry calculations. It is demonstrated that the bond angle geometry around the methylene-acetal linkage plays an important role in the optimization.

Algorithms↗

PubMed1996

Optimisation of metric matrix embedding by genetic algorithms.

To improve the convergence properties of 'embedding' distance geometry, a new approach was developed by combining the distance-geometry methodology with a genetic algorithm. This new approach is called DG-OMEGA (DG omega, optimised metric matrix embedding by genetic algorithms). The genetic algorithm was used to combine well-defined parts of individual structures generated by the distance-geometry program, and to identify new lower and upper distance bounds within the original experimental restraints in order to restrict the sampling of the metrisation algorithm to promising regions of the conformational space. The algorithm was tested on cyclosporin A, which is notorious for its intrinsic difficult sampling properties. A set of 58 distance restraints was employed. It was shown that DG omega resulted in an improvement of convergence behaviour as well as sampling properties with respect to the standard distance-geometry protocol.

Algorithms↗

PubMed1996

Parallel processing of chemical information in a local area network--II. A parallel cross-validation procedure for artificial neural networks.

This paper describes a parallel cross-validation (PCV) procedure, for testing the predictive ability of multi-layer feed-forward (MLF) neural networks models, trained by the generalized delta learning rule. The PCV program has been parallelized to operate in a local area computer network. Development and execution of the parallel application was aided by the HYDRA programming environment, which is extensively described in Part I of this paper. A brief theoretical introduction on MLF networks is given and the problems, associated with the validation of predictive abilities, will be discussed. Furthermore, this paper comprises a general outline of the PCV program. Finally, the parallel PCV application is used to validate the predictive ability of an MLF network modeling a chemical non-linear function approximation problem which is described extensively in the literature.

Artificial Intelligence↗

PubMed1996

Parallel processing of chemical information in a local area network--III. Using genetic algorithms for conformational analysis of biomacromolecules.

Multi-dimensional nuclear magnetic resonance experiments are an excellent means of revealing the three-dimensional structure of biomacromolecules in solution. However, the search space in the conformational analysis of biomacromolecules, using multi-dimensional NMR data, is huge and complex. This calls for global optimization techniques with good sampling properties. This paper describes a genetic algorithm that optimizes the fit between (simulated) experimental two-dimensional Nuclear Overhauser Effect spectra and the corresponding calculated spectra for trial structures. This is a very computational intensive procedure. Speed-up of performance is achieved by parallelizing the algorithm, i.e. creating small subpopulations of trial structures, each of which can be processed on different processors. Good sampling behavior is obtained by initializing each subpopulation with its own random seed and the introduction of a migration operator. The latter replaces the best performing individual from one subpopulation with the worst performing individual from another subpopulation after a predetermined number of generations. A parallel genetic algorithm for the conformational analysis of nucleic acids is developed using the software package HYDRA. It is demonstrated that, for the data sets used in the study, a considerable reduction in computation time is obtained for the parallel genetic algorithm as compared to a sequential implementation, while the same optimal solutions are found.

Algorithms↗

PubMedDate not supplied

The influence of different structure representations on the clustering of an RNA nucleotides data set.

The last couple of years an overwhelming amount of data has emerged in the field of biomolecular structure determination. To explore information hidden in these structure databases, clustering techniques can be used. The outcome of the clustering experiments largely depends, among others, on the way the data is represented; therefore, the choice how to represent the molecular structure information is extremely important. This article describes what the influence of the different representations on the clustering is and how it can be analyzed by means of a dendrogram comparison method. All experiments are performed using a data set consisting of RNA trinucleotides. Besides the most basic structure representation, the Cartesian coordinates representation, several other structure representations are used.

Cluster Analysis↗