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PubMed2006

Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling.

The complexity of the acute inflammatory response (AIR) is, by now, generally recognized. The primary manifestation of this property has been the difficulty in translating the information derived from reductionist, basic science research into effective clinical treatment regimens for sepsis. However, the recognition of the "complexity" of the AIR is not without its pitfalls. Despite its limitations, reductionism remains the primary means of obtaining scientific information. Furthermore, a functional shortcoming of use of the term complex has been to make it equivalent to "essentially unsolvable." Therefore, a mechanism is needed to integrate the apparatus of reductionist analysis into a complex synthetic methodology that overcomes the current limitations of both. Toward this end, I propose a structure for a class of collaborative, community-wide in silico models that use the framework of agent-based modeling. Agent-based modeling is a type of mathematical modeling that focuses on the behaviors of the components of complex systems and is well suited for translating the results of basic science experiments. I will also introduce a preliminary version of a syntactical "grammar" that can potentially be used to facilitate the transfer of basic science data into computer code. It is hoped that when a mature version of this framework is implemented, the resulting models will provide a functional, synthetic data base on the AIR that could be used for directing research, testing hypotheses, teaching and training, and drug discovery/testing.

Acute Disease↗

PubMedDate not supplied

Detecting autocatalytic dynamics in data modeled by a compartmental model.

Modeling growth or reaction dynamics within a compartment in a compartmental model is often based on theoretical or first principle considerations. This approach is frequently applied due to the inability to observe or collect data directly from the compartment. When the internal dynamics are difficult to surmise, it is often the case that several competing models are constructed and compared in some way. In this paper, the dynamics which characterize the data of an autocatalytic process are used to describe a quantitative data analysis strategy to both recognize the presence of the autocatalytic process and to obtain some estimates of important parameters in the process. The compartmental model structure serves to communicate this dynamical information to the downstream compartments. This method has been applied to examine the dynamics of the engraftment of blood cells following hematopoietic stem cell transplantation in a clinical setting [Modeling the time to engraftment of white blood cells and platelets following autologous peripheral blood stem cell transplantation (2001)].

Catalysis↗

PubMed1999

Modeling risk using generalized linear models.

Traditionally, linear regression has been the technique of choice for predicting medical risk. This paper presents a new approach to modeling the second part of two-part models utilizing extensions of the generalized linear model. The primary method of estimation for this model is maximum likelihood. This method as well as the generalizations quasi-likelihood and extended quasi-likelihood are discussed. An example using medical expense data from Washington State employees is used to illustrate the methods. The model includes demographic variables as well as an Ambulatory. Care Group variable to account for prior health status.

Adolescent↗

PubMed2001

A note on R2 measures for Poisson and logistic regression models when both models are applicable.

The aim of many epidemiological studies is the regression of a dichotomous outcome (e.g., death or affection by a certain disease) on prognostic covariables. Thereby the Poisson regression model is often used alternatively to the logistic regression model. Modelling the number of events and individual outcomes, respectively, both models lead to nearly the same results concerning the parameter estimates and their significances. However, when calculating the proportion of explained variation, quantified by an R2 measure, a large difference between both models usually occurs. We illustrate this difference by an example and explain it with theoretical arguments. We conclude, the R2 measure of the Poisson regression quantifies the predictability of event rates, but it is not adequate to quantify the predictability of the outcome of individual observations.

Adult↗

PubMed1982

Spatial aggregation in gravity models: 2. One-dimensional population density models.

"This paper, the second of four, is concerned with applying a methodology for analysing the spatial aggregation problem in gravity models outlined in the first paper. The methodology is based on a consistent framework for linking measures of pattern in interaction data to the derivation and estimation of related interaction models using spatial information. In this quest, a link is forged between information in data and the parameters of an associated model.... This relationship can be formalised for the case of the continuous one-dimensional interaction model such as the population density model, and this paper is concerned with demonstrating such an application to aggregations of zones in the Reading [England] region."

Demography↗

PubMed1995

Trinucleotide models for DNA bending propensity: comparison of models based on DNaseI digestion and nucleosome packaging data.

DNaseI digestion studies (Brukner et al, EMBO J 14, 1812-1818 1995) and nucleosomebinding data (Satchwell et al, J. Mol. Biol. 191, 639-659 1986, Goodsell and Dickerson, Nucleic trinucleotides. A detailed comparison of the two models suggests that while both of them represent improvements with respect to dinucleotide based descriptions, the individual trinucleotide parameters are not highly correlated (linear correlation coefficient is 0.53), and a number of motifs such as TA-elements and CCA/TGG motifs are more realistically described in the DNaseI-based model. This may be due to the fact that the DNaseI-based model does not rely on a static geometry but rather captures a dynamic ability of ds DNA to bend towards the major grove. Future refinement of both models of both models on larger experimental data sets is expected to further improve the prediction of macroscopic DNA-curvature.

DNA↗

PubMed2003

A human model for road safety: from geometrical acquisition to model validation with radioss.

In order to investigate injury mechanisms, and to provide directions for road safety system improvements, the HUMOS project has lead to the development of a 3D finite element model of the human body in driving position. The model geometry was obtained from a 50th percentile adult male. It includes the description of all compact and trabecular bones, ligaments, tendons, skin, muscles and internal organs. Material properties were based on literature data and specific experiments performed for the project. The validation of the HUMOS model was first achieved on isolated segments and then on the whole model in both frontal and lateral impact situations. HUMOS responses were in good agreement with the experimental data used in the model validation and offers now a wide range of applications from crash simulation, optimization of safety systems, to biomedical and ergonomics.

Abdomen↗

PubMed1999

Modeling and predicting transcriptional units of Escherichia coli genes using hidden Markov models.

MOTIVATION: The hidden Markov model (HMM) is a valuable technique for gene-finding, especially because its flexibility enables the inclusion of various sequence features. Recent programs for bacterial gene-finding include the information of ribosomal binding site (RBS) to improve the recognition accuracy of the start codon, using this feature. We report here our attempt to extend the model into the total transcriptional unit, enabling the prediction of operon structures. RESULTS: First, we improved the prediction accuracy of coding sequences (CDSs) by employing the models of 'typical', 'atypical' and 'negative (false-positive)' classes as well as the models of RBS and its downstream spacer. The sensitivity of exactly predicting the 204 experimentally confirmed CDSs reached 90.2% in an objective test. Based on the prediction result of CDSs, the positions of the promoters and terminators were predicted. Our model could exactly recognize 60% of 390 known transcriptional units. Thus, the accuracy and significance of this prediction problem is far from trivial. We would like to propose this problem as an open theme in bioinformatics because the ongoing or planned post-sequencing projects will produce much data for future improvements.

Algorithms↗

PubMed1994

A comparison of the performance of model-based confidence intervals when the correct model form is unknown: coverage of asymptotic means.

We conducted a simulation study to examine the performance of confidence intervals when multiplicative and additive rate (Poisson regression) models are fit to follow-up data, but the model form may be misspecified. Data were generated from over 129,000 different population structures that ranged from sub-additive to supra-multiplicative. When a multiplicative model was fit, all of the confidence intervals that we examined performed well as interval estimators of the asymptotic means of the point estimators, even when the correct model form was not multiplicative. When an additive model was fit, (1) only the likelihood ratio interval and the score interval with expected information consistently performed well, and they consistently performed better than any of the Wald intervals that we examined; (2) Wald intervals performed better when calculated with observed information rather than with expected information; and (3) Wald intervals with expected information performed better when the information matrix was evaluated at the restricted maximum likelihood estimate rather than the unrestricted maximum likelihood estimate.

Confidence Intervals↗

PubMed1998

Modelling variability in lymphatic filariasis: macrofilarial dynamics in the Brugia pahangi--cat model.

A striking feature of lymphatic filariasis is the considerable heterogeneity in infection burden observed between hosts, which greatly complicates the analysis of the population dynamics of the disease. Here, we describe the first application of the moment closure equation approach to model the sources and the impact of this heterogeneity for macrofilarial population dynamics. The analysis is based on the closest laboratory equivalent of the life cycle and immunology of infection in humans--cats chronically infected with the filarial nematode Brugia pahangi. Two sets of long-term experiments are analysed: hosts given either single primary infections or given repeat infections. We begin by quantifying changes in the mean and aggregation of adult parasites (inversely measured by the negative binomial parameter, kappa in cohorts of hosts using generalized linear models. We then apply simple stochastic models to interpret observed patterns. The models and empirical data indicate that parasite aggregation tracks the decline in the mean burden with host age in primary infections. Conversely, in repeat infections, aggregation increases as the worm burden declines with experience of infection. The results show that the primary infection variability is consistent with heterogeneities in parasite survival between hosts. By contrast, the models indicate that the reduction in parasite variability with time in repeat infections is most likely due to the 'filtering' effect of a strong, acquired immune response, which gradually acts to remove the initial variability generated by heterogeneities in larval mortality. We discuss this result in terms of the homogenizing effect of host immunity-driven density-dependence on macrofilarial burden in older hosts.

Animals↗

PubMed2000

Fitting host-parasitoid models with CV2 > 1 using hierarchical generalized linear models.

The powerful general Pacala-Hassell host-parasitoid model for a patchy environment, which allows host density-dependent heterogeneity (HDD) to be distinguished from between-patch, host density-independent heterogeneity (HDI), is reformulated within the class of the generalized linear model (GLM) family. This improves accessibility through the provision of general software within well-known statistical systems, and allows a rich variety of models to be formulated. Covariates such as age class, host density and abiotic factors may be included easily. For the case where there is no HDI, the formulation is a simple GLM. When there is HDI in addition to HDD, the formulation is a hierarchical generalized linear model. Two forms of HDI model are considered, both with between-patch variability: one has binomial variation within patches and one has extra-binomial, overdispersed variation within patches. Examples are given demonstrating parameter estimation with standard errors, and hypothesis testing. For one example given, the extra-binomial component of the HDI heterogeneity in parasitism is itself shown to be strongly density dependent.

Animals↗

PubMed2001

Modeling of ultrasonic wave propagation in teeth using PSpice: a comparison with finite element models.

Ultrasound is used extensively in the medical field for the detection and characterization of a variety of features in the human body. Finite element models used to understand ultrasonic wave propagation in teeth have been developed so that ultrasound techniques could be realized in dentistry. This paper presents a hypothesis that underlies one possible design of an ultrasonic tool that can be used in a clinical environment, as well as several models that describe acoustic field simulation, propagation, and interaction with the layers of several tooth structures. A complete PSpice model of a single-element transducer based on Redwood's version of Mason's equivalent circuit, a focusing lens, and a multi-layer tooth structure is used to illustrate the validity of this hypothesis. Transmission line theory is employed as a basis for the models of the piezoceramic, the lens, and the different tooth layers. Results clearly depict the transmission and reflection of the ultrasonic waves as they travel through the layers within the tooth structure and point out the noticeable similarity to longitudinal L-wave signatures produced by axisymmetric finite element models presented in earlier studies.

Biomedical Engineering↗

PubMed2006

Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model.

Minimally invasive surgery (MIS) involves a multidimensional series of tasks requiring a synthesis between visual information and the kinematics and dynamics of the surgical tools. Analysis of these sources of information is a key step in defining objective criteria for characterizing surgical performance. The Blue DRAGON is a new system for acquiring the kinematics and the dynamics of two endoscopic tools synchronized with the endoscopic view of the surgical scene. Modeling the process of MIS using a finite state model [Markov model (MM)] reveals the internal structure of the surgical task and is utilized as one of the key steps in objectively assessing surgical performance. The experimental protocol includes tying an intracorporeal knot in a MIS setup performed on an animal model (pig) by 30 surgeons at different levels of training including expert surgeons. An objective learning curve was defined based on measuring quantitative statistical distance (similarity) between MM of experts and MM of residents at different levels of training. The objective learning curve was similar to that of the subjective performance analysis. The MM proved to be a powerful and compact mathematical model for decomposing a complex task such as laparoscopic suturing. Systems like surgical robots or virtual reality simulators in which the kinematics and the dynamics of the surgical tool are inherently measured may benefit from incorporation of the proposed methodology.

Computer Simulation↗

PubMed2003

A bivariate frailty model with a cure fraction for modeling familial correlations in diseases.

We suggest a cure-mixture model to analyze bivariate time-to-event data, as motivated by the article of Chatterjee and Shih (2001, Biometrics 57, 779-786), but with a simpler estimation procedure and the correlated gamma-frailty model instead of the shared gamma-frailty model. This approach allows us to deal with left-truncated and right-censored lifetime data, and accounts for heterogeneity, as well as for an insusceptible (cure) fraction in the study population. We perform a simulation study to evaluate the properties of the estimates in the proposed model and apply it to breast cancer incidence data for 5857 Swedish female monozygotic and dizygotic twin pairs from the so-called old cohort of the Swedish Twin Registry. This model is used to estimate the size of the susceptible fraction and the correlation between the frailties of the twin partners. Possible extensions, advantages, and limitations of the proposed method are discussed.

Biometry↗

PubMed2005

Tandem practice model: a model for physician-nurse practitioner collaboration in a specialty practice, neurosurgery.

PURPOSE: To describe the benefits of a physician-nurse practitioner (NP) collaborative practice model, specifically that of a tandem practice model, using a neurosurgeon and a primary care NP in the clinic and inpatient setting. DATA SOURCES: Selected journal articles from Medline and CINAHL, and anecdotal clinical experience. CONCLUSIONS: This collaborative practice model, in which the physician and NP deliver patient care in tandem, is beneficial to patients and their families because they receive comprehensive care that is patient oriented and holistic. Further, the model benefits multiple disciplines across the healthcare continuum by providing efficient communication of patient needs, accessibility of the specialty team, and timely implementation of patient interventions. The collaboration of the physician specialist and primary care NP provides a holistic approach to the care of diverse and challenging patient populations. IMPLICATIONS FOR PRACTICE: Patients seen in a specialty practice, particularly that of neurosurgery, often have little understanding of their problem and may be frightened or confused because of their perceptions of the unknown. Providing care to such specialized patient populations in a constantly changing healthcare environment may prove demanding to the specialist. The introduction of a primary care NP into such specialty settings offers patients, their families, consultants, and staff members an additional resource for evaluation, intervention, education, and communication, improving the continuity and comprehensiveness of care to challenging patient populations. This model is an option for physician specialists interested in augmenting their practice and provides further resources for meeting the holistic needs of selected patient populations regardless of the setting.

Attitude of Health Personnel↗

PubMed2005

Using L-systems for modeling source-sink interactions, architecture and physiology of growing trees: the L-PEACH model.

Functional-structural plant models simulate the development of plant structure, taking into account plant physiology and environmental factors. The L-PEACH model is based on the development of peach trees. It demonstrates the usefulness of L-systems in constructing functional-structural models. L-PEACH uses L-systems both to simulate the development of tree structure and to solve differential equations for carbohydrate flow and allocation. New L-system-based algorithms are devised for simulating the behavior of dynamically changing structures made of hundreds of interacting, time-varying, nonlinear components. L-PEACH incorporates a carbon-allocation model driven by source-sink interactions between tree components. Storage and mobilization of carbohydrates during the annual life cycle of a tree are taken into account. Carbohydrate production in the leaves is simulated based on the availability of water and light. Apices, internodes, leaves and fruit grow according to the resulting local carbohydrate supply. L-PEACH outputs an animated three-dimensional visual representation of the growing tree and user-specified statistics that characterize selected stages of plant development. The model is applied to simulate a tree's response to fruit thinning and changes in water stress. L-PEACH may be used to assist in horticultural decision-making processes after being calibrated to specific trees.

Biological Transport, Active↗

PubMed2006

Flexible modeling via a hybrid estimation scheme in generalized mixed models for longitudinal data.

To circumvent the computational complexity of likelihood inference in generalized mixed models that assume linear or more general additive regression models of covariate effects, Laplace's approximations to multiple integrals in the likelihood have been commonly used without addressing the issue of adequacy of the approximations for individuals with sparse observations. In this article, we propose a hybrid estimation scheme to address this issue. The likelihoods for subjects with sparse observations use Monte Carlo approximations involving importance sampling, while Laplace's approximation is used for the likelihoods of other subjects that satisfy a certain diagnostic check on the adequacy of Laplace's approximation. Because of its computational tractability, the proposed approach allows flexible modeling of covariate effects by using regression splines and model selection procedures for knot and variable selection. Its computational and statistical advantages are illustrated by simulation and by application to longitudinal data from a fecundity study of fruit flies, for which overdispersion is modeled via a double exponential family.

Animals↗

PubMed1997

Numerical modeling of steady inspiratory airflow through a three-generation model of the human central airways.

Two-dimensional steady inspiratory airflow through a three-generation model of the human central airways is numerically investigated, with dimensions corresponding to those encountered in the fifth to seventh generations of the Weibel's model. Wall curvatures are added at the outer walls of the junctions for physiological purposes. Computations are carried out for Reynolds numbers in the mother branch ranging from 200 to 1200, which correspond to mouth air breathing at flow rates ranging from 0.27 to 1.63 liters per second. The difficulty of generating grids in a so complex configuration is overcome using a nonoverlapping multiblock technique. Simulations demonstrate the existence of separation regions whose number, location, and size strongly depend on the Reynolds number. Consequently, four different flow configurations are detected. Velocity profiles downstream of the bifurcations are shown to be highly skewed, thus leading to an important unbalance in the flow distribution between the medial and lateral branches of the model. These results confirm the observations of Snyder et al. and Tsuda et al. and suggest that a resistance model of flow partitioning based on Kirchhoff's laws is inadequate to simulate the flow behavior accurately within the airways. When plotted in a Moody diagram, airway resistance throughout the model is shown to fit with a linear relation of slope -0.61. This is qualitatively in good agreement with the experimental investigations of Pedley et al, and Slutsky et al.

Aerosols↗