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PubMed1994

A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study.

A model of carbohydrate metabolism has been implemented as a causal probabilistic network, allowing explicit representation of the uncertainties involved in the prediction of 24-h blood glucose profiles in insulin-dependent diabetic subjects. The parameters of the model were based on experimental data from the literature describing insulin and carbohydrate absorption, renal loss of glucose, insulin-independent glucose utilisation and insulin-dependent glucose utilisation and production. The model can be adapted to the observed glucose metabolism in the individual patient and can be used to generate predicted 24-h blood glucose profiles. A penalty is assigned to each level of blood glucose, to indicate that high and low blood glucose levels are undesirable. The system can be asked to find the insulin doses that result in the most desirable 24-h blood glucose profile. In a series of 12 patients, the system predicted blood glucose with a mean error of 3.3 mmol/l. The insulin doses suggested by the system seemed reasonable and in several cases seemed more appropriate than the doses actually administered to the patients.

Absorption↗

PubMed1993

Heterogeneity and probabilistic binding contributions to receptor-mediated cell detachment kinetics.

Biospecific cell adhesion is mediated by receptor-ligand bonds. Early theoretical work presented a deterministic analysis of receptor-mediated cell attachment and detachment for a homogeneous cell population. However, initial comparison of a deterministic framework to experimental detachment profiles of model "cells" (antibody-coated latex beads) did not show qualitative or quantitative agreement (Cozens-Roberts, C., D.A. Lauffenburger, and J.A. Quinn. 1990. Biophys. J. 58:857-872). Hence, we determine the contributions of population heterogeneity and probabilistic binding to the detachment behavior of this experimental system which was designed to minimize experimental and theoretical complications. This work also corrects an error in the numerical solution of the probabilistic model of receptor-mediated cell attachment and detachment developed previously (Cozens-Roberts, C., D.A. Lauffenburger, and J.A. Quinn. 1990. Biophys J. 58:841-856). Measurement of the population distribution of the number of receptors per bead has enabled us to explicitly consider the effect of receptor number heterogeneity within the cell-surface contact area. A deterministic framework that incorporates receptor number heterogeneity qualitatively and quantitatively accounts in large part for the model cell detachment data. Using measured and estimated parameter values for the model cell system, we estimate that about 90% of the observed kinetic detachment behavior originates from heterogeneity effects, while about 10% is due to probabilistic binding effects. In general, these relative contributions may differ for other systems.

Biophysical Phenomena↗

PubMed2005

Tensorial extensions of independent component analysis for multisubject FMRI analysis.

We discuss model-free analysis of multisubject or multisession FMRI data by extending the single-session probabilistic independent component analysis model (PICA; Beckmann and Smith, 2004. IEEE Trans. on Medical Imaging, 23 (2) 137-152) to higher dimensions. This results in a three-way decomposition that represents the different signals and artefacts present in the data in terms of their temporal, spatial, and subject-dependent variations. The technique is derived from and compared with parallel factor analysis (PARAFAC; Harshman and Lundy, 1984. In Research methods for multimode data analysis, chapter 5, pages 122-215. Praeger, New York). Using simulated data as well as data from multisession and multisubject FMRI studies we demonstrate that the tensor PICA approach is able to efficiently and accurately extract signals of interest in the spatial, temporal, and subject/session domain. The final decompositions improve upon PARAFAC results in terms of greater accuracy, reduced interference between the different estimated sources (reduced cross-talk), robustness (against deviations of the data from modeling assumptions and against overfitting), and computational speed. On real FMRI 'activation' data, the tensor PICA approach is able to extract plausible activation maps, time courses, and session/subject modes as well as provide a rich description of additional processes of interest such as image artefacts or secondary activation patterns. The resulting data decomposition gives simple and useful representations of multisubject/multisession FMRI data that can aid the interpretation and optimization of group FMRI studies beyond what can be achieved using model-based analysis techniques.

Artifacts↗

PubMed1998

Noise from voltage-gated ion channels may influence neuronal dynamics in the entorhinal cortex.

Neurons of the superficial medial entorhinal cortex (MEC), which deliver neocortical input to the hippocampus, exhibit intrinsic, subthreshold oscillations with slow dynamics. These intrinsic oscillations, driven by a persistent Na+ current and a slow outward current, may help to generate the theta rhythm, a slow rhythm that plays an important role in spatial and declarative learning. Here we show that the number of persistent Na+ channels underlying subthreshold oscillations is relatively small (<10(4)) and use a physiologically based stochastic model to argue that the random behavior of these channels may contribute crucially to cellular-level responses. In acutely isolated MEC neurons under voltage clamp, the mean and variance of the persistent Na+ current were used to estimate the single channel conductance and voltage-dependent probability of opening. A hybrid stochastic-deterministic model was built by using voltage-clamp descriptions of the persistent and fast-inactivating Na+ conductances, along with the fast and slow K+ conductances. All voltage-dependent conductances were represented with nonlinear ordinary differential equations, with the exception of the persistent Na+ conductance, which was represented as a population of stochastic ion channels. The model predicts that the probabilistic nature of Na+ channels increases the cell's repertoire of qualitative behaviors; although deterministic models at a particular point in parameter space can generate either subthreshold oscillations or phase-locked spikes (but rarely both), models with an appropriate level of channel noise can replicate physiological behavior by generating both patterns of electrical activity for a single set of parameters. Channel noise may contribute to higher order interspike interval statistics seen in vitro with DC current stimulation. Models with channel noise show evidence of spike clustering seen in brain slice experiments, although the effect is apparently not as prominent as seen in experimental results. Channel noise may contribute to cellular responses in vivo as well; the stochastic system has enhanced sensitivity to small periodic stimuli in a form of stochastic resonance that is novel (in that the relevant noise source is intrinsic and voltage-dependent) and potentially physiologically relevant. Although based on a simple model that does not include all known membrane mechanisms of MEC stellate cells, these results nevertheless imply that the stochastic nature of small collections of molecules may have important effects at the cellular and network levels.

Animals↗

PubMed2004

Modeling and simulation of point-non-point source effluent trading in Taihu Lake area: perspective of non-point sources control in China.

The past decades have witnessed some efforts in point source water pollution controls in China. However, adequate abatement efforts have not been implemented on non-point source control, and non-point source contributions remain and have increased as a share of surface water degradation. It has been noted that conventional command-and-control regulations are ineffective for agricultural non-point source pollution, and watershed abatement trading between point and non-point sources may serve as a cost-effective way to deal with it. In this paper, the feasibility of point-non-point sources effluent trading in China and cost-effectiveness of the trading system on water pollution control are evaluated using a stochastic programming model and a combined probabilistic watershed simulation of a representative agricultural watershed in the Taihu Lake area. The method and model can be used to assess economic and environmental opportunities of trading in similar watersheds in China. The use of explicit emission target and reliability decision rules in the chance-constrained programming model is a practical simplification to convert a stochastic program into a solvable deterministic problem. Based on the simulation, suggestions on development and implementation of point-non-point sources abatement trading scheme in China were discussed.

Agriculture↗

PubMed2005

Characterization of model error in a simulation of fine particulate matter exposure distributions of the working age population in Helsinki, Finland.

Exposure models are needed for comparison of scenarios resulting from alternative policy options. The reliability of models used for such purposes should be quantified by comparing model outputs in a real situation with the corresponding observed exposures. Measurement errors affect the observations, but if the distribution of these errors for single observations is known, the bias caused for the population statistics can be corrected. The current paper does this and calculates model errors for a probabilistic simulation of 48-hr fine particulate matter (PM2.5) exposures. Direct and nested microenvironment-based models are compared. The direct model requires knowledge on the distribution of the indoor concentrations, whereas the nested model calculates indoor concentrations from ambient levels, using infiltration factors and indoor sources. The model error in the mean exposure level was <0.5 microg m(-3) for both models. Relative errors in the estimated population mean were +1% and -5% for the direct and nested models, respectively. Relative errors in the estimated SD were -9% and -23%, respectively. The magnitude of these errors and the errors calculated for population percentiles indicate that the model errors would not drive general conclusions derived from these models, supporting the use of the models as a tool for evaluation of potential exposure reductions in alternative policy scenarios.

Adult↗

PubMed1996

Probabilistic learning in immune network: weighted tree matching model.

Adaptive learning properties (of clonal selection and affinity maturation) in the immune network model are investigated in this paper under a nonlinear data structural representation of the involved molecules. Weighted trees are constructed to model the multiple paratopes/epitopes on the antibodies/antigens. Parallel computing experiments are carried out for the canonical coding of these trees and the corresponding multiple matching interactions. Our experiments on real data have shown significant results on the cognitive properties of the immune network. These and other computational results are presented along with a discussion of future applications.

Amino Acid Sequence↗

PubMed2006

A probabilistic approach to protein backbone tracing in electron density maps.

One particularly time-consuming step in protein crystallography is interpreting the electron density map; that is, fitting a complete molecular model of the protein into a 3D image of the protein produced by the crystallographic process. In poor-quality electron density maps, the interpretation may require a significant amount of a crystallographer's time. Our work investigates automating the time-consuming initial backbone trace in poor-quality density maps. We describe ACMI (Automatic Crystallographic Map Interpreter), which uses a probabilistic model known as a Markov field to represent the protein. Residues of the protein are modeled as nodes in a graph, while edges model pairwise structural interactions. Modeling the protein in this manner allows the model to be flexible, considering an almost infinite number of possible conformations, while rejecting any that are physically impossible. Using an efficient algorithm for approximate inference--belief propagation--allows the most probable trace of the protein's backbone through the density map to be determined. We test ACMI on a set of ten protein density maps (at 2.5 to 4.0 A resolution), and compare our results to alternative approaches. At these resolutions, ACMI offers a more accurate backbone trace than current approaches.

Algorithms↗

PubMed2003

What characterises a useful concept of causation in epidemiology?

It has recently been suggested that epidemiologists should avoid thinking of causes in deterministic terms. This would mean giving up the component-cause model in its original form. A model that has provided important contributions as to how we develop hypotheses, design our study, analyse data, interpret and communicate our results. The component-cause model has considerably more to offer than a simple probabilistic concept. What a causal model has to offer to the advancement of the discipline is equally important as the concept itself. It has been said that we should not hunt "the Holy Grail" (that is, determinism), if it does not exist. This line of reasoning neglects the fact that the "hunting" is more important than the "finding".

Causality↗

PubMed2005

A quantitative model for flow-induced bioluminescence in dinoflagellates.

A model is presented for the flash response of bioluminescent dinoflagellates stimulated by fluid shear. The model is based on the idea that the response of an individual cell to stimulation is inherently probabilistic, and can be modeled as a Poisson process over short time scales. A new cell parameter, the cell anxiety, is introduced to parameterize the probability of flashing. The statistical model is incorporated into a description of fully developed fluid flow in pipes and a cylindrical Couette chamber, and found to compare favorably with previously published data from experiments.

Animals↗

PubMed2002

Ecological and evolutionary validity: comments on Johnson-Laird, Legrenzi, Girotto, Legrenzi, and Caverni's (1999) mental-model theory of extensional reasoning.

The mental-model account of naive probabilistic reasoning by P. N. Johnson-Laird, P. Legrenzi, V. Girotto, M. S. Legrenzi, and J.-P. Caverni (1999) provides an opportunity to clarify several similarities and differences between it and ecological rationality (frequentist) accounts. First, ambiguities in the meaning of Bayesian reasoning can lead to disagreements and inappropriate arguments. Second, 2 conflated effects of using natural frequencies are noticed but not actually tested separately because of an artificial dissociation of frequency representations and natural sampling. Third, similarities are noted between the subset principle and the principle of natural sampling. Finally, some potentially misleading portrayals of the role of evolutionary factors in psychology are corrected. Mental-model theory, rather than better explaining probabilistic reasoning, may be able to use frequency representations as a key element in clarifying its own ambiguous constructs.

Bayes Theorem↗

PubMed2002

A new deformable model for analysis of X-ray CT images in preclinical studies of mice for polycystic kidney disease.

This paper describes the application of a new probabilistic shape and appearance model (PSAM) algorithm to the task of detecting polycystic kidney disease (PKD) in X-ray computed tomography images of laboratory mice. The genetically engineered PKD mouse is a valuable animal model that can be used to develop new treatments for kidney-related problems in humans. PSAM is a statistical-based deformable model that improves upon existing point distribution models for boundary-based object segmentation. This new deformable model algorithm finds the optimal boundary position using an objective function that has several unique characteristics. Most importantly, the objective function includes both global shape and local gray-level characteristics, so optimization occurs with respect to both pieces of information simultaneously. PSAM is employed to segment the mouse kidneys and then texture measurements are applied within kidney boundaries to detect PKD. The challenges associated with the segmentation non-rigid organs along with the availability of a priori information led to the choice of a trainable, deformable model for this application. In 103 kidney images that were analyzed as part of a preclinical animal study, the mouse kidneys and spine were segmented with an average error of 2.4 pixels per boundary point. In all 103 cases, the kidneys were successfully segmented at a level where PKD could be detected using mean-of-local-variance texture measurements within the located boundary.

Algorithms↗

PubMed1999

Medical application of engineering risk analysis and anesthesia patient risk illustration.

The engineering risk analysis method can be extended to include some human and organizational factors and can be used in the medical domain; this transfer is illustrated by a description of a study of anesthesia patient risk. This study involves first a dynamic analysis of accident risks. The model is then extended by relating the basic events of accident scenarios to the state of the practitioner described by the probability of personal problems that may affect his or her level of competence and alertness. These potential problems, in turn, are linked (by probabilistic relations) to the way the system is managed. This extension of the analytical framework allows assessment of the effect of particular types of practitioner problems and therefore of corresponding risk mitigation measures on the probability of the different accident scenarios. The risk analysis model can then be used as a management tool that permits setting priorities among patient safety measures, based either on the sole benefits of the corresponding decrease of patient risk or on a cost-to-benefit ratio. This probabilistic approach constitutes a departure from the classic risk studies exclusively based on statistical frequencies because it involves both available statistics and expert opinions. It is commonly used in engineering for systems for which there is not enough information at the time when decisions need to be made. I show here how the probabilistic model can be used in the medical field to support patient safety decisions before complete data sets can be gathered or in cases in which some key factors are not directly observable.

Anesthesia↗

PubMed2005

Speeded classification in a probabilistic category structure: contrasting exemplar-retrieval, decision-boundary, and prototype models.

Speeded perceptual classification experiments were conducted to distinguish among the predictions of exemplar-retrieval, decision-boundary, and prototype models. The key manipulation was that across conditions, individual stimuli received either probabilistic or deterministic category feedback. Regardless of the probabilistic feedback, however, an ideal observer would always classify the stimuli by using an identical linear decision boundary. Subjects classified the probabilistic stimuli with lower accuracy and longer response times than they classified the deterministic stimuli. These results are in accord with the predictions of the exemplar model and challenge the predictions of the prototype and decision-boundary models.

Analysis of Variance↗

PubMed1998

Artificial neural networks and logistic regression as tools for prediction of survival in patients with Stages I and II non-small cell lung cancer.

The prognosis of patients with Stage I and II non-small cell lung cancer (NSCLC) can be estimated but cannot be definitively ascertained by use of current clinicopathologic criteria and tumor marker studies. The potential value of probabilistic neural networks (NNs) with genetic algorithms and multivariate logistic regression to predict the survival of NSCLC patients has not been previously evaluated. Multiple prognostic factors (age, sex, cell type, stage, tumor grade, smoking history, and immunoreactivity to c-erbB-3, bcl-2, Glut1, Glut3, retinoblastoma gene and p53 were correlated with 5-year survival in 63 patients with Stage I or II NSCLC, treated solely by surgical excision at Baylor Medical College, Houston, Texas. Several probabilistic NNs with genetic algorithm models were developed using the prognostic features as input neurons and survival at 5 years (free of disease/dead of disease) as output neurons. The probabilistic NN yielded excellent classification rates for dependent variable survival. The best model was trained with 52 cases and classified all 11 "unknown" test cases correctly. Several statistically significant logistic regression models were fitted using 50 cases to build the models and 13 cases as "hold-out" test cases. These multivariate statistical models provide various cutoff values that predict/classify the probability of survival at 5 years. In conclusion, probabilistic NNs and logistic regression models can be useful in estimating the prognosis of patients with Stage I and II NSCLC using multiple clinicopathologic and molecular variables. These multivariate predictive models need to be validated with much larger groups of patients to assess their potential clinical value.

Adult↗

PubMed2002

Modelling large scale neuronal networks using 'average neurones'.

Large scale neuronal network models have become important tools in studying the information transmission within the CNS. In most cases, these models use simplifying assumptions because of unavailable data (e.g. unknown exact network connectivity), and for technical reasons (to preserve numerical stability of the model). Here, we present a novel approach, based on a probabilistic connectivity principle, to this modelling problem for which no knowledge of the exact network connectivity is required. This principle makes it sufficient to compute only the typical neuronal behaviour, represented by 'average neurones', in the network. As a consequence, detailed neurone models can be employed without seriously compromising computational efficiency. Our model thus provides a viable alternative to deterministic models.

Action Potentials↗

PubMed2005

Modelling the evolution of genetic regulatory networks.

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.

Algorithms↗

PubMed2006

A probabilistic framework for non-cancer risk assessment.

Risk assessment involves an analysis of the relationship between exposure and health related outcomes to derive an allowable exposure level or to estimate a low-dose risk. Acceptable levels of human exposure for non-cancer effects generally are derived by dividing an experimental no-observed-adverse-effect-level or a lower confidence limit benchmark dose by a product of several uncertainty factors. This paper presents a hierarchical modeling framework for a probabilistic approach to non-cancer risk assessment. The hierarchical model integrates the distributions of uncertainty factors and the distribution of the actual exposure level to construct the dose-response model for the proportion of population at risk and the dose-response model for the expected proportion of population at risk for a given exposure distribution. The proposed approach is based on the use of the BMDL (lower confidence limit on the benchmark dose) as a POD (point of departure) for risk assessment of non-cancer effects.

Animals↗