Paj Peter Hilbers

The role of collagen in bone apatite formation in the presence of hydroxyapatite nucleation inhibitors

Bone is a composite material in which collagen fibrils form a scaffold for a highly organized arrangement of uniaxially oriented apatite crystals. In the periodic 67 nm cross-striated pattern of the collagen fibril, the less dense 40-nm-long gap zone has been implicated as the place where apatite crystals nucleate from an amorphous phase, and subsequently grow. This process is believed to be directed by highly acidic non-collagenous proteins; however, the role of the collagen matrix during bone apatite mineralization remains unknown. Here, combining nanometre-scale resolution cryogenic transmission electron microscopy and cryogenic electron tomography with molecular modelling, we show that collagen functions in synergy with inhibitors of hydroxyapatite nucleation to actively control mineralization. The positive net charge close to the C-terminal end of the collagen molecules promotes the infiltration of the fibrils with amorphous calcium phosphate (ACP). Furthermore, the clusters of charged amino acids, both in gap and overlap regions, form nucleation sites controlling the conversion of ACP into a parallel array of oriented apatite crystals. We developed a model describing the mechanisms through which the structure, supramolecular assembly and charge distribution of collagen can control mineralization in the presence of inhibitors of hydroxyapatite nucleation.

Monte Carlo simulations of a simple model for the electrocatalytic CO oxidation on platinum

A simple lattice-gas model for the electrocatalytic carbon monoxide oxidation on a platinum electrode is studied by dynamic Monte Carlo simulations. The CO oxidation takes place through a Langmuir–Hinshelwood reaction between adsorbed CO and an adsorbed OH radical resulting from the dissociative adsorption of water. The model enables the investigation of the role of CO surface mobility on the macroscopic electrochemical response such as linear sweep voltammetry and potential step chronoamperometry. Our results show that the mean-field approximation, the traditional but often tacitly made assumption in electrochemistry, breaks down severely in the limit of vanishing CO surface mobility. Comparison of the simulated and experimental voltammetry suggests that on platinum CO oxidation is the intrinsically fastest reaction on the surface and that CO has a high surface mobility. However, under the same conditions, the model predicts some interesting deviations from the potential step current transients derived f...

Understanding Cooperativity in Hydrogen-Bond-Induced Supramolecular Polymerization: A Density Functional Theory Study

Understanding the molecular mechanism of cooperative self-assembly is a key component in the design of self-assembled supramolecular architectures across multiple length scales with defined function and composition. In this work, we use density functional theory to rationalize the experimentally observed cooperative growth of C(3)-symmetrical trialkylbenzene-1,3,5-tricarboxamide- (BTA-) based supramolecular polymers that self-assemble into ordered one-dimensional supramolecular structures through hydrogen bonding. Our analysis shows that the cooperative growth of these structures is caused by electrostatic interactions and nonadditive effects brought about by redistribution of the electron density with aggregate length.

Monte Carlo simulations of a surface reaction model showing spatio-temporal pattern formations and oscillations

Results of dynamic Monte Carlo simulations of a model for CO oxidation on a reconstructing Pt(100) surface are presented. A comparison is made between simulations that explicitly include surface diffusion of adsorbed CO and simulations without diffusion. Oscillatory behavior as well as spatio-temporal pattern formation are studied as a function of system size. In the absence of diffusion the amplitude of kinetic oscillations decreases with grid size and oscillations are not stable. Spatio-temporal patterns appear, as expected for an excitable medium. Such patterns become stabilized by structural substrate defects. The length scale of the patterns is in the order of 10–100 nm, the temporal period of the oscillations is around 200 seconds. Inclusion of diffusion stabilizes and synchronizes oscillations. Spatio-temporal features now appear with larger spatial dimensions.

A Bayesian approach to targeted experiment design

Motivation: Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity. Results: We have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions. Availability and implementation: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html Contact: j.vanlier@tue.nl; N.A.W.v.Riel@tue.nl Supplementary information: Supplementary data are available at Bioinformatics online.

An integrated strategy for prediction uncertainty analysis

Motivation: To further our understanding of the mechanisms underlying biochemical pathways mathematical modelling is used. Since many parameter values are unknown they need to be estimated using experimental observations. The complexity of models necessary to describe biological pathways in combination with the limited amount of quantitative data results in large parameter uncertainty which propagates into model predictions. Therefore prediction uncertainty analysis is an important topic that needs to be addressed in Systems Biology modelling. Results: We propose a strategy for model prediction uncertainty analysis by integrating profile likelihood analysis with Bayesian estimation. Our method is illustrated with an application to a model of the JAK-STAT signalling pathway. The analysis identified predictions on unobserved variables that could be made with a high level of confidence, despite that some parameters were non-identifiable. Availability and implementation: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html. Contact: j.vanlier@tue.nl Supplementary information: Supplementary data are available at Bioinformatics online.

Quantification of atherosclerotic plaque components using in vivo MRI and supervised classifiers

In this work we aimed to study the possibility of using supervised classifiers to quantify the main components of carotid atherosclerotic plaque in vivo on the basis of multisequence MRI data. MRI data consisting of five MR weightings were obtained from 25 symptomatic subjects. Histological micrographs of endarterectomy specimens from the 25 carotids were used as a standard of reference for training and evaluation. The set of subjects was divided in a training set (12 subjects) and an evaluation set (13 subjects). Four different classifiers and two human MRI readers determined the percentages of calcified tissue, fibrous tissue, lipid core, and intraplaque hemorrhage on the subject level for all subjects in the evaluation set. Quantification of the relatively small amounts of calcium could not be done with statistical significance by either the classifiers or the MRI readers. For the other tissues a simple Bayesian classifier (Bayes) performed better than the other classifiers and the MRI readers. All classifiers performed better than the MRI readers in quantifying the sum of hemorrhage and lipid proportions. The MRI readers overestimated the hemorrhage proportions and tended to underestimate the lipid proportions. In conclusion, this pilot study demonstrates the benefits of algorithmic classifiers for quantifying plaque components. Magn Reson Med, 2006.

Volume fraction dependence and reorganization in cluster-cluster aggregation processes

Off‐lattice diffusion limited cluster aggregation simulations in two dimensions have been performed in a wide volume fraction range between 0.001 and 0.60. Starting from a system of 10 000 monomers with radius 0.5, that follow Brownian trajectories, larger aggregates are generated by bond formation between overlapping aggregates. No rings are present in the nonaged structures. The influence of the initial monomer volume fraction on the fractal properties of the gels is studied and interpreted by calculation of small angle scattering structure factor patterns to find the fractal dimension. It is found that an increase of the volume fraction leads to the development of two distinct fractal regions. The fractal dimension at short length scale shows the diffusion limited cluster aggregation value of 1.45 up to the correlation length, while the long range fractal dimension gradually increases from 1.45 to 2.00, the Euclidean dimension of the simulation space. It is shown that high volume fractions lead to chan...

SIMULATION OF SMALL-ANGLE SCATTERING FROM LARGE ASSEMBLIES OF MULTI-TYPE SCATTERER PARTICLES

Abstract We describe a central processing unit (CPU)-efficient expansion of the Debye scattering formula for the calculation of small-angle scattering patterns of model systems composed of different types of scatterers. The algorithm permits the use of atomic scattering factors or form factors of hard spheres of variable radius and scattering density. We apply the algorithm to the computation of partial small-angle scattering profiles in biological multi-type systems and examine the relative importance of particles with different connectivities in determining the fractal dimension of large particle networks.

Parameter adaptations during phenotype transitions in progressive diseases

BackgroundThe study of phenotype transitions is important to understand progressive diseases, e.g., diabetes mellitus, metabolic syndrome, and cardiovascular diseases. A challenge remains to explain phenotype transitions in terms of adaptations in molecular components and interactions in underlying biological systems.ResultsHere, mathematical modeling is used to describe the different phenotypes by integrating experimental data on metabolic pools and fluxes. Subsequently, trajectories of parameter adaptations are identified that are essential for the phenotypical changes. These changes in parameters reflect progressive adaptations at the transcriptome and proteome level, which occur at larger timescales. The approach was employed to study the metabolic processes underlying liver X receptor induced hepatic steatosis. Model analysis predicts which molecular processes adapt in time after pharmacological activation of the liver X receptor. Our results show that hepatic triglyceride fluxes are increased and triglycerides are especially stored in cytosolic fractions, rather than in endoplasmic reticulum fractions. Furthermore, the model reveals several possible scenarios for adaptations in cholesterol metabolism. According to the analysis, the additional quantification of one cholesterol flux is sufficient to exclude many of these hypotheses.ConclusionsWe propose a generic computational approach to analyze biological systems evolving through various phenotypes and to predict which molecular processes are responsible for the transition. For the case of liver X receptor induced hepatic steatosis the novel approach yields information about the redistribution of fluxes and pools of triglycerides and cholesterols that was not directly apparent from the experimental data. Model analysis provides guidance which specific molecular processes to study in more detail to obtain further understanding of the underlying biological system.