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PubMed2005

Noise and functional protein dynamics.

The magnetic field dependence of the proton-spin-lattice relaxation rate in rotationally immobilized proteins shows that the one-dimensional character of the protein primary structure causes a dramatic increase in the population of low-frequency motions from 10 kHz to 20 MHz. As a consequence, the probability and rate at which functionally critical conformational states are thermally sampled in a protein are dramatically increased as well, when compared with a three-dimensional lattice structure. Studies of protein dynamics often focus on time periods far shorter than those associated with catalytic function, but we show here that the magnetic field dependence of the proton nuclear spin-lattice relaxation rate in rotationally immobilized proteins reports unambiguously the structural fluctuations in the frequency range from 10 kHz to 20 MHz. This relaxation rate decreases with increasing Larmor frequency according to a power law that derives from the distribution of dynamical states, the localization of the structural disturbances, and the spatial distribution of hydrogen atoms in the structure. The robust theoretical foundation for the spin-relaxation process, loosely characterized as a direct spin-phonon coupling, shows that the disturbances propagate in a space of reduced dimensionality, essentially along the stiff connections of the polypeptide chain. The reduced dimensionality traps the disturbance and changes the efficiency for energy redistribution in the protein and the processes that drive nuclear spin relaxation. We also show that the Larmor frequency dependence of the protein-proton-spin-lattice relaxation rate constant is related to the frequency dependence of force constants and mean-square displacement commonly observed or calculated for proteins. We believe that these approaches give additional physical insight into the character of the extremely low-frequency protein dynamics.

Computer Simulation↗
PubMed1995

New trends in natural language processing: statistical natural language processing.

The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and information-theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotation to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part-of-speech tagging, stochastic parsing, and lexical semantics.

Algorithms↗
PubMed2004

Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum.

SUMMARY: Using replicated human serum samples, we applied an error model for proteomic differential expression profiling for a high-resolution liquid chromatography-mass spectrometry (LC-MS) platform. The detailed noise analysis presented here uses an experimental design that separates variance caused by sample preparation from variance due to analytical equipment. An analytic approach based on a two-component error model was applied, and in combination with an existing data driven technique that utilizes local sample averaging, we characterized and quantified the noise variance as a function of mean peak intensity. The results indicate that for processed LC-MS data a constant coefficient of variation is dominant for high intensities, whereas a model for low intensities explains Poisson-like variations. This result leads to a quadratic variance model which is used for the estimation of sample preparation noise present in LC-MS data.

Algorithms↗
PubMed2000

Nonparametric and parametric estimation for a linear germination-growth model.

Seeds are planted on the interval [0, L] at various locations. Each seed has a location x and a potential germination time t epsilon [0, infinity), and it is assumed that the collection of such (x, t) pairs forms a Poisson process in [0, L] x [0, infinity) with intensity measure dxd lambda(t). From each seed that germinates, an inhibiting region grows bidirectionally at rate 2v. These regions inhibit germination of any seed in the region with a later potential germination time. Thus, seeds only germinate in the uninhibited part of [0, L]. We want to estimate lambda on the basis of one or more realizations of the process, the data being the locations and germination times of the germinated seeds. We derive the maximum likelihood estimator of v and a nonparametric estimator of lambda and describe methods of obtaining parametric estimates from it, illustrating these with reference to gamma densities. Simulation results are described and the methods applied to some neurobiological data. An Appendix outlines the S-PLUS code used.

Animals↗
PubMed2000

Regional confidence bands for ROC curves.

The performance of a diagnostic test is characterised by its specificity and sensitivity. For a quantitative diagnostic test these criteria depend on the selected cut-off point. The receiver operating characteristic (ROC) curve of a quantitative diagnostic test is generated by plotting sensitivity against specificity as the cut-off point runs through the whole range of possible test values. In practice, the ROC curve is estimated from clinical data. One important goal is to select an optimal cut-off point. For this purpose the sample variability has to be taken into account. Recently, Campbell has introduced nonparametric asymptotic simultaneous confidence bands that are valid for the whole ROC curve. In this paper a nonparametric asymptotic approach for the construction of regional confidence bands for ROC curves is proposed. It can be applied for any specificity interval of interest. Our approach is based on the asymptotic theory of empirical and quantile processes. To investigate the small sample properties of the different approaches, a Monte Carlo study was conducted using normal and log-normal data. A method for sample size calculation is presented. Finally, the approaches are applied to a tumour marker in the diagnosis of bone marrow metastases.

Bone Marrow Neoplasms↗
PubMed2004

Bayesian modeling of multiple lesion onset and growth from interval-censored data.

In studying rates of occurrence and progression of lesions (or tumors), it is typically not possible to obtain exact onset times for each lesion. Instead, data consist of the number of lesions that reach a detectable size between screening examinations, along with measures of the size/severity of individual lesions at each exam time. This interval-censored data structure makes it difficult to properly adjust for the onset time distribution in assessing covariate effects on rates of lesion progression. This article proposes a joint model for the multiple lesion onset and progression process, motivated by cross-sectional data from a study of uterine leiomyoma tumors. By using a joint model, one can potentially obtain more precise inferences on rates of onset, while also performing onset time-adjusted inferences on lesion severity. Following a Bayesian approach, we propose a data augmentation Markov chain Monte Carlo algorithm for posterior computation.

Adult↗
PubMed1996

Monte Carlo spreadsheet modeling of stable isotope biosynthesis.

Metabolic physiologists often introduce stable isotopes, atoms containing additional neutrons, into molecules during biosynthesis. This tags the newly synthesized material by altering its mass. Monte Carlo analysis is implemented on a popular spreadsheet to analyze this process. An example is provided where acetoacetate is synthesized by condensation of two acetate moieties. The precursor acetate is present as a mixture of natural, and 13C enriched, acetate. Monte Carlo spreadsheet modeling captures the complexity of the multi-species isotope biosynthesis by repetitively performing multiple simultaneous Boolean calculations. The effects of increasing the number of molecules synthesized on the goodness of fit of between model and an exact analytical solution is illustrated.

Acetoacetates↗
PubMed2001

Ecological forecasts: an emerging imperative.

Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts of critical ecosystem services requires a process that engages scientists and decision-makers. Interdisciplinary linkages are necessary because of the climate and societal controls on ecosystems, the feedbacks involving social change, and the decision-making relevance of forecasts.

Agriculture↗
PubMed2001

New reproductive anomalies in fruitless-mutant Drosophila males: extreme lengthening of mating durations and infertility correlated with defective serotonergic innervation of reproductive organs.

Several features of male reproductive behavior are under the neural control of fruitless (fru) in Drosophila melanogaster. This gene is known to influence courtship steps prior to mating, due to the absence of attempted copulation in the behavioral repertoire of most types of fru-mutant males. However, certain combinations of fru mutations allow for fertility. By analyzing such matings and their consequences, we uncovered two striking defects: mating times up to four times the normal average duration of copulation; and frequent infertility, regardless of the time of mating by a given transheterozygous fru-mutant male. The lengthened copulation times may be connected with fru-induced defects in the formation of a male-specific abdominal muscle. Production of sperm and certain seminal fluid proteins are normal in these fru mutants. However, analysis of postmating qualities of females that copulated with transheterozygous mutants strongly implied defects in the ability of these males to transfer sperm and seminal fluids. Such abnormalities may be associated with certain serotonergic neurons in the abdominal ganglion in which production of 5HT is regulated by fru. These cells send processes to contractile muscles of the male's internal sex organs; such projection patterns are aberrant in the semifertile fru mutants. Therefore, the reproductive functions regulated by fruitless are expanded in their scope, encompassing not only the earliest stages of courtship behavior along with almost all subsequent steps in the behavioral sequence, but also more than one component of the culminating events.

Animals↗
PubMed2004

Lattice and non-lattice models of tumour angiogenesis.

In order to progress from the relatively harmless avascular state to the potentially lethal vascular state, solid tumours must induce the growth of new blood vessels from existing ones, a process called angiogenesis. The capillary growth centres around endothelial cells: there are several cell-based models of this process in the literature and these have reproduced some of the key microscopic features of capillary growth. The most common approach is to simulate the movement of leading endothelial cells on a regular lattice. Here, we apply a circular random walk model to the process of angiogenesis, and thus allow the cells to move independently of a lattice; the results display good agreement with empirical observations. We also run simulations of two lattice-based models in order to make a critical comparison of the different modelling approaches. Finally, non-lattice simulations are carried out in the context of a realistic model of tumour angiogenesis, and potential anti-angiogenic strategies are evaluated.

Angiogenesis Inhibitors↗
PubMed2006

Photo-oxidation of diglycine in confined media Relaxation of longitudinal magnetization in spin correlated radical pairs.

Time-resolved electron paramagnetic resonance spectra (X-band) of correlated radical pairs created in AOT reverse micelles are presented and simulated using the microreactor model. They are discussed in terms of the two-site model with a particular emphasis on longitudinal relaxation mechanisms. The geminate radical pair is created by photo-oxidation of dyglicine by the excited triplet states of an anthraquinone salt. The strong chemically induced electron spin polarization observed is due to three mechanisms: TM, RPM, and SCRPM. Relative contributions from these mechanisms depend on the water pool volume and the time of observation. There are three types of longitudinal relaxation in radical pairs. The first is relaxation of the RPM induced longitudinal magnetization in spin correlated radical pairs. The second is the longitudinal relaxation in radical pairs which are not correlated (with a zero value of the double quantum coherence). In such pairs, the generation of longitudinal magnetization due to RPM is impossible, and the spin-selective recombination of the pairs is ineffective. Under all experimental conditions, the first type of relaxation is slower than the second type. For both, the physical mechanism leading to relaxation is modulation of the Heisenberg electron spin exchange interaction. This is an internal relaxation process. The third relaxation type occurs in radical pairs due to ordinary longitudinal relaxation in non-interacting radicals. Normally, relaxation of the third type is the slowest of the three. This explains time and micelle size dependence of the relative contribution of RPM into TREPR spectra. It seems reasonable to suggest that the creation of the initial spin state populations is partially adiabatic.

Electron Spin Resonance Spectroscopy↗
PubMed2005

Solvable sequence evolution models and genomic correlations.

We study a minimal model for genome evolution whose elementary processes are single site mutation, duplication and deletion of sequence regions, and insertion of random segments. These processes are found to generate long-range correlations in the composition of letters as long as the sequence length is growing; i.e., the combined rates of duplications and insertions are higher than the deletion rate. For constant sequence length, on the other hand, all initial correlations decay exponentially. These results are obtained analytically and by simulations. They are compared with the long-range correlations observed in genomic DNA, and the implications for genome evolution are discussed.

DNA↗
PubMed2006

A global approach for solving evolutive heat transfer for image denoising and inpainting.

This paper proposes an alternative to partial differential equations (PDEs) for solving problems in computer vision based on evolutive heat transfer. Traditionally, the method for solving such physics-based problems is to discretize and solve a PDE by a purely mathematical process. Instead of using the PDE, we propose to use the global heat principle and to decompose it into basic laws. We show that some of these laws admit an exact global version since they arise from conservative principles. We also show that the assumptions made about the other basic Iaws can be made wisely, taking into account knowledge about the problem and the domain. The numerical scheme is derived in a straightforward way from the modeled problem, thus providing a physical explanation for each step in the solution. The advantage of such an approach is that it minimizes the approximations made during the whole process and it modularizes it, allowing changing the application to a great number of problems. We apply the scheme to two applications: image denoising and inpainting which are modeled with heat transfer. For denoising, we propose a new approximation for the conductivity coefficient and we add thin lines to the features in order to block diffusion.

Algorithms↗
PubMed2002

T-cell tolerance by dendritic cells and macrophages as a mechanism for the major histocompatibility complex-linked resistance to autoimmune diabetes.

For poorly understood reasons, the development of autoimmune diabetes in humans and mice is dominantly inhibited by major histocompatibility complex (MHC) class II molecules with diverse antigen-binding sites. We have previously shown that thymocytes expressing a highly diabetogenic I-A(g7)-restricted T-cell receptor (TCR) (4.1-TCR) undergo negative selection in mice carrying one copy of the antidiabetogenic H-2(b) haplotype in an I-A(b)-dependent but superantigen-independent manner. Here, we show that 4.1-TCR-transgenic thymocytes undergo different forms of tolerance in NOD mice expressing antidiabetogenic I-A(d), I-A(g7PD), or I-Ealpha(k) transgenes. The ability of protective MHC class II molecules to induce thymocyte tolerance in 4.1-TCR-transgenic NOD mice correlates with their ability to prevent diabetes in non-TCR-transgenic mice and is associated with polymorphisms within positions 56-67 of their beta1 domains. The 4.1-thymocyte tolerogenic activity of these MHC class II molecules is mediated by dendritic cells and macrophages but not by B-cells or thymic epithelial cells and is a peptide-dependent process. Antidiabetogenic MHC class II molecules may thus afford diabetes resistance by presenting, on dendritic cells and macrophages, tolerogenic peptides to a subset of highly diabetogenic and MHC-promiscuous CD4(+) T-cells that play a critical role in the initiation of diabetes.

Animals↗
PubMed1997

Decision support and data analysis tools for risk assessment in primary preventive study of atherosclerosis.

In the paper we show results of two programs applied for data analysis and decision support in primary preventive study of atherosclerosis. First program E.T. (Epidemiology Tools) can be used for analysis of data from retrospective (case-control) studies, prospective (cohort) studies and for standardization. Second program called CORE (COnstitution and REduction) supports the process of selection of features that are relevant for given decision making task. Program CORE is using information theory approach. Both these programs were applied to analysis of data about 1417 middle age men collected in the longitudinal study on atherosclerosis in urban population. Apart from these two new programs we have analyzed data also by the STATISTICA software.

Adult↗
PubMed1994

Artificial neural networks and simulated molecular evolution are potential tools for sequence-oriented protein design.

The potential of artificial neural filter systems for feature extraction from amino acid sequences is discussed. Analysis of signal peptidase I cleavage-sites in protein precursor sequences serves as an example application. Trained neural networks can be used as the fitness function in an evolutionary protein design cycle termed 'simulated molecular evolution' which is an entirely computer-based method for the rational design of locally encoded amino acid sequence features. The design procedure itself is regarded as an optimization process which can follow several schemes. Gradient search, diffusive search, and evolution strategy have been compared with regard to their usefulness for optimization. It turns out that gradient search is well suited for optimization in smooth fitness landscapes without local minima, whereas evolution strategy seems to be a method of choice for optimization in a high-dimensional multimodal search space. This is concluded from optimization experiments using a multimodal example function.

Amino Acid Sequence↗
PubMed2005

Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness.

Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden "ignition" of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of "inattentional blindness," in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness.

Arousal↗
PubMed2005

Statistical kinetics of macromolecular dynamics.

Fluctuations in biochemical processes can provide insights into the underlying kinetics beyond what can be gleaned from studies of average rates alone. Historically, analysis of fluctuating transmembrane currents supplied information about ion channel conductance states and lifetimes before single-channel recording techniques emerged. More recently, fluctuation analysis has helped to define mechanochemical pathways and coupling ratios for the motor protein kinesin as well as to probe the contributions of static and dynamic disorder to the kinetics of single enzymes. As growing numbers of assays are developed for enzymatic or folding behaviors of single macromolecules, the range of applications for fluctuation analysis increases. To evaluate specific biochemical models against experimental data, one needs to predict analytically the distribution of times required for completion of each reaction pathway. Unfortunately, using traditional methods, such calculations can be challenging for pathways of even modest complexity. Here, we derive an exact expression for the distribution of completion times for an arbitrary pathway with a finite number of states, using a recursive method to solve algebraically for the appropriate moment-generating function. To facilitate comparisons with experiments on processive motor proteins, we develop a theoretical formalism for the randomness parameter, a dimensionless measure of the variance in motor output. We derive the randomness for motors that take steps of variable sizes or that move on heterogeneous substrates, and then discuss possible applications to enzymes such as RNA polymerase, which transcribes varying DNA sequences, and to myosin V and cytoplasmic dynein, which may advance by variable increments.

Algorithms↗