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Biomedical subjects

Raimond L Winslow

Publications and source records attributed to Raimond L Winslow.

At least 19 recordsLinked to original sources
PubMed2006

A computational model integrating electrophysiology, contraction, and mitochondrial bioenergetics in the ventricular myocyte.

An intricate network of reactions is involved in matching energy supply with demand in the heart. This complexity arises because energy production both modulates and is modulated by the electrophysiological and contractile activity of the cardiac myocyte. Here, we present an integrated mathematical model of the cardiac cell that links excitation-contraction coupling with mitochondrial energy generation. The dynamics of the model are described by a system of 50 ordinary differential equations. The formulation explicitly incorporates cytoplasmic ATP-consuming processes associated with force generation and ion transport, as well as the creatine kinase reaction. Changes in the electrical and contractile activity of the myocyte are coupled to mitochondrial energetics through the ATP, Ca2+, and Na+ concentrations in the myoplasmic and mitochondrial matrix compartments. The pseudo steady-state relationship between force and oxygen consumption at various stimulus frequencies and external Ca2+ concentrations is reproduced in both model simulations and direct experiments in cardiac trabeculae under normoxic conditions, recapitulating the linearity between cardiac work and respiration in the heart. Importantly, the model can also reproduce the rapid time-dependent changes in mitochondrial NADH and Ca2+ in response to abrupt changes in workload. The steady-state and dynamic responses of the model were conferred by ADP-dependent stimulation of mitochondrial oxidative phosphorylation and Ca2+ -dependent regulation of Krebs cycle dehydrogenases, illustrating how the model can be used as a tool for investigating mechanisms underlying metabolic control in the heart.

Action Potentials↗
PubMed2006

MASCOT HTML and XML parser: an implementation of a novel object model for protein identification data.

Protein identification using MS is an important technique in proteomics as well as a major generator of proteomics data. We have designed the protein identification data object model (PDOM) and developed a parser based on this model to facilitate the analysis and storage of these data. The parser works with HTML or XML files saved or exported from MASCOT MS/MS ions search in peptide summary report or MASCOT PMF search in protein summary report. The program creates PDOM objects, eliminates redundancy in the input file, and has the capability to output any PDOM object to a relational database. This program facilitates additional analysis of MASCOT search results and aids the storage of protein identification information. The implementation is extensible and can serve as a template to develop parsers for other search engines. The parser can be used as a stand-alone application or can be driven by other Java programs. It is currently being used as the front end for a system that loads HTML and XML result files of MASCOT searches into a relational database. The source code is freely available at http://www.ccbm.jhu.edu and the program uses only free and open-source Java libraries.

Databases, Protein↗
PubMed2006

Multiscale modeling of calcium signaling in the cardiac dyad.

Calcium (Ca(2+))-induced Ca(2+)-release (CICR) takes place in spatially restricted microdomains known as dyads. The length scale over which CICR occurs is on the order of nanometers and relevant time scales range from micro- to milliseconds. Quantitative understanding of CICR therefore requires development of models that are applicable over a range of spatio-temporal scales. We will present several new approaches for multiscale modeling of CICR. First, we present a model of dyad Ca(2+) dynamics in which the Fokker-Planck equation (FPE) is solved for the probability P(x, t) that a Ca(2+) ion is located at dyad position x at time t. Using this model, we demonstrate that (a) Ca(2+) signaling in the dyad is mediated by approximately tens of Ca(2+) ions; (b) these signaling events are noisy due to the small number of ions involved; and (c) the geometry of the RyR (ryanodine receptors) protein may function to restrict the diffusion of and to "funnel" Ca(2+) ions to activation-binding sites on the RyR, thus increasing RyR open probability and excitation-contraction (EC) coupling gain. Simplification of this model to one in which the dyadic space is represented using a single compartment yields the stochastic local-control model of CICR developed previously. We have shown that this model captures fundamental properties of CICR, such as graded release and voltage-dependent gain, may be integrated within a model of the myocyte and may be simulated in reasonable times using a combination of efficient numerical methods and parallel computing, but remains too complex for general use in cell simulations. To address this problem, we show how separation of time scales may be used to formulate a model in which nearby L-type Ca(2+) channels (LCCs) and RyRs gate as a coupled system that may be described using low-dimensional systems of ordinary differential equations, thus reducing computational complexity while capturing fundamentally important properties of CICR. The simplified model may be solved many orders of magnitude faster than can either of the more detailed models, thus enabling incorporation into tissue-level simulations.

Calcium Signaling↗
PubMed2005

Evidence of structural remodeling in the dyssynchronous failing heart.

Ventricular remodeling of both geometry and fiber structure is a prominent feature of several cardiac pathologies. Advances in MRI and analytical methods now make it possible to measure changes of cardiac geometry, fiber, and sheet orientation at high spatial resolution. In this report, we use diffusion tensor imaging to measure the geometry, fiber, and sheet architecture of eight normal and five dyssynchronous failing canine hearts, which were explanted and fixed in an unloaded state. We apply novel computational methods to identify statistically significant changes of cardiac anatomic structure in the failing and control heart populations. The results demonstrate significant regional differences in geometric remodeling in the dyssynchronous failing heart versus control. Ventricular chamber dilatation and reduction in wall thickness in septal and some posterior and anterior regions are observed. Primary fiber orientation showed no significant change. However, this result coupled with the local wall thinning in the septum implies an altered transmural fiber gradient. Further, we observe that orientation of laminar sheets become more vertical in the early-activated septum, with no significant change of sheet orientation in the late-activated lateral wall. Measured changes in both fiber gradient and sheet structure will affect both the heterogeneity of passive myocardial properties as well as electrical activation of the ventricles.

Animals↗
PubMed2005

Transcriptomic profiling of the canine tachycardia-induced heart failure model: global comparison to human and murine heart failure.

Alterations of cardiac gene expression are central to ventricular dysfunction in human heart failure (HF). The canine tachycardia pacing-induced HF model is known to reproduce the main hemodynamic, echocardiographic and electrophysiological changes observed in human HF. In this study, we use this HF model to compare gene expression profiles in the left and right ventricles (LV, RV) of normal and end-stage failing canine hearts and compare the transcription profiles to those in human and murine models of HF. In end-stage HF, the LV exhibits down regulation of genes involved in energy production, cardiac contraction, and modulation of excitation-contraction coupling as compared with normal LV. The majority of transcriptomic changes between normal and end-stage canine HF were shared by the RV and LV. Genes down regulated only in the LV included those involved in aerobic energy production pathways, regulation of actin filament length, and enzyme-linked receptor protein signaling pathways. In normal canine hearts, genes encoding specific components of the contractile apparatus exhibit LV-RV asymmetric expression patterns; in failing hearts, cardiac fetal transcription factors MEF2 and MITF and the stress-responsive transcription factor ATF4 showed interventricular differences in expression. The comparison among the canine tachypacing, mouse transgenic, and human HF reveals that human disease involves down regulation of genes in a broad range of biological processes while experimentally induced HF is associated with down regulation of energy pathways, and that human ischemic HF and canine HF share a similar over representation of transcriptional pathways in the up regulated genes. This study provides insights into the molecular pathways leading to end-stage tachycardia-induced HF, and into global transcriptomic differences between the animal HF models and human HF.

Activating Transcription Factor 4↗
PubMed2005

Mechanisms of excitation-contraction coupling in an integrative model of the cardiac ventricular myocyte.

It is now well established that characteristic properties of excitation-contraction (EC) coupling in cardiac myocytes, such as high gain and graded Ca(2+) release, arise from the interactions that occur between L-type Ca(2+) channels (LCCs) and nearby ryanodine-sensitive Ca(2+) release channels (RyRs) in localized microdomains. Descriptions of Ca(2+)-induced Ca(2+) release (CICR) that account for these local mechanisms are lacking from many previous models of the cardiac action potential, and those that do include local control of CICR are able to reconstruct properties of EC coupling, but require computationally demanding stochastic simulations of approximately 10(5) individual ion channels. In this study, we generalize a recently developed analytical approach for deriving simplified mechanistic models of CICR to formulate an integrative model of the canine cardiac myocyte which is computationally efficient. The resulting model faithfully reproduces experimentally measured properties of EC coupling and whole cell phenomena. The model is used to study the role of local redundancy in L-type Ca(2+) channel gating and the role of dyad configuration on EC coupling. Simulations suggest that the characteristic steep rise in EC coupling gain observed at hyperpolarized potentials is a result of increased functional coupling between LCCs and RyRs. We also demonstrate mechanisms by which alterations in the early repolarization phase of the action potential, resulting from reduction of the transient outward potassium current, alters properties of EC coupling.

Action Potentials↗
PubMed2005

Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data.

MOTIVATION: DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers. RESULTS: We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis. CONTACT: leixu@jhu.edu.

Algorithms↗
PubMed2005

Simple decision rules for classifying human cancers from gene expression profiles.

MOTIVATION: Various studies have shown that cancer tissue samples can be successfully detected and classified by their gene expression patterns using machine learning approaches. One of the challenges in applying these techniques for classifying gene expression data is to extract accurate, readily interpretable rules providing biological insight as to how classification is performed. Current methods generate classifiers that are accurate but difficult to interpret. This is the trade-off between credibility and comprehensibility of the classifiers. Here, we introduce a new classifier in order to address these problems. It is referred to as k-TSP (k-Top Scoring Pairs) and is based on the concept of 'relative expression reversals'. This method generates simple and accurate decision rules that only involve a small number of gene-to-gene expression comparisons, thereby facilitating follow-up studies. RESULTS: In this study, we have compared our approach to other machine learning techniques for class prediction in 19 binary and multi-class gene expression datasets involving human cancers. The k-TSP classifier performs as efficiently as Prediction Analysis of Microarray and support vector machine, and outperforms other learning methods (decision trees, k-nearest neighbour and naïve Bayes). Our approach is easy to interpret as the classifier involves only a small number of informative genes. For these reasons, we consider the k-TSP method to be a useful tool for cancer classification from microarray gene expression data. AVAILABILITY: The software and datasets are available at http://www.ccbm.jhu.edu CONTACT: actan@jhu.edu.

Algorithms↗
PubMed2005

Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure.

A three-dimensional (3D) diffusion-weighted imaging (DWI) method for measuring cardiac fiber structure at high spatial resolution is presented. The method was applied to the ex vivo reconstruction of the fiber architecture of seven canine hearts. A novel hypothesis-testing method was developed and used to show that distinct populations of secondary and tertiary eigenvalues may be distinguished at reasonable confidence levels (P < or = 0.01) within the canine ventricle. Fiber inclination and sheet angles are reported as a function of transmural depth through the anterior, lateral, and posterior left ventricle (LV) free wall. Within anisotropic regions, two consistent and dominant orientations were identified, supporting published results from histological studies and providing strong evidence that the tertiary eigenvector of the diffusion tensor (DT) defines the sheet normal.

Algorithms↗
PubMed2005

Large deformation diffeomorphic metric mapping of vector fields.

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRIs) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the Large Deformation Diffeomorphic Metric Mapping framework to this type of dataset, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two images. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart images.

Algorithms↗
PubMed2005

Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging.

The ventricular myocardium is known to exhibit a complex spatial organization, with fiber orientation varying as a function of transmural location. It is now well established that diffusion tensor magnetic resonance imaging (DTMRI) may be used to measure this fiber orientation at high spatial resolution. Cardiac fibers are also known to be organized in sheets with surface orientation varying throughout the ventricles. This article reviews results on use of DTMRI for measuring ventricular fiber orientation, as well as presents new results providing strong evidence that the tertiary eigenvector of the diffusion tensor is aligned locally with the cardiac sheet surface normal. Considered together, these data indicate that DTMRI may be used to reconstruct both ventricular fiber and sheet organization. This article also presents the large deformation diffeomorphic metric mapping (LDDMM) algorithm and shows that this algorithm may be used to bring ensembles of imaged and reconstructed hearts into correspondence (e.g., registration) so that variability of ventricular geometry, fiber, and sheet orientation may be quantified. Ventricular geometry and fiber structure is known to be remodeled in a range of disease processes; however, descriptions of this remodeling have remained subjective and qualitative. We anticipate that use of DTMRI for reconstruction of ventricular anatomy coupled with application of the LDDMM method for image volume registration will enable the detection and quantification of changes in cardiac anatomy that are characteristic of specific disease processes in the heart. Finally, we show that epicardial electrical mapping and DTMRI imaging may be performed in the same hearts. The anatomic data may then be used to simulate electrical conduction in a computational model of the very same heart that was mapped electrically. This facilitates direct comparison and testing of model versus experimental results and opens the door to quantitative measurement, modeling, and analysis of the ways in which remodeling of ventricular microanatomy influences electrical conduction in the heart.

Diffusion Magnetic Resonance Imaging↗
PubMed2004

Using models of the myocyte for functional interpretation of cardiac proteomic data.

There has been significant progress towards the development of highly integrative computational models of the cardiac myocyte over the past decade. Models now incorporate descriptions of voltage-gated ionic currents and membrane transporters, mechanisms of calcium-induced calcium release and intracellular calcium cycling, mitochondrial ATP production and its coupling to energy-requiring membrane transport processes and mechanisms of force generation. There is an extensive literature documenting both the reconstructive and predictive abilities of these models and there is no question that an interplay between quantitative modelling and experimental investigation has become a central component of modern cardiovascular research. As data regarding the cardiovascular proteome in both health and disease emerge, integrative models of the myocyte are becoming useful tools for interpreting the functional significance of changes in protein expression and post-translational modifications (PTMs). Data of particular importance include information on: (a) changes of expressed protein level, (b) changes of protein PTMs, (c) protein localization, and (d) protein-protein interactions, as it is often possible to incorporate and interpret the functional significance of such findings using computational models. We provide two examples of how models may be used in this fashion. In the first example, we show how information on altered expression of the sarcoplasmic reticulum Ca2+-ATPase, when interpreted through the use of a computational model, has provided key insights into fundamental mechanisms regulating cardiac action potential duration. In the second example, we show how information on the effects of phosphorylation of L-type Ca2+ channels, when interpreted through the use of a model, provides insights on how this post-translational modification alters the properties of excitation-contraction coupling and risk for arrhythmia.

Animals↗
PubMed2004

The role of stochastic and modal gating of cardiac L-type Ca2+ channels on early after-depolarizations.

Certain signaling events that promote L-type Ca2+ channel (LCC) phosphorylation, such as beta-adrenergic stimulation or an increased expression of Ca(2+)/calmodulin-dependent protein kinase II, promote mode 2 gating of LCCs. Experimental data suggest the hypothesis that these events increase the likelihood of early after-depolarizations (EADs). We test this hypothesis using an ionic model of the canine ventricular myocyte incorporating stochastic gating of LCCs and ryanodine-sensitive calcium release channels. The model is extended to describe myocyte responses to the beta-adrenergic agonist isoproterenol. Results demonstrate that in the presence of isoproterenol the random opening of a small number of LCCs gating in mode 2 during the plateau phase of the action potential (AP) can trigger EADs. EADs occur randomly, where the likelihood of these events increases as a function of the fraction of LCCs gating in mode 2. Fluctuations of the L-type Ca2+ current during the AP plateau lead to variability in AP duration. Consequently, prolonged APs are occasionally observed and exhibit an increased likelihood of EAD formation. These results suggest a novel stochastic mechanism, whereby phosphorylation-induced changes in LCC gating properties contribute to EAD generation.

Action Potentials↗
PubMed2004

Classifying gene expression profiles from pairwise mRNA comparisons.

We present a new approach to molecular classification based on mRNA comparisons. Our method, referred to as the top-scoring pair(s) (TSP) classifier, is motivated by current technical and practical limitations in using gene expression microarray data for class prediction, for example to detect disease, identify tumors or predict treatment response. Accurate statistical inference from such data is difficult due to the small number of observations, typically tens, relative to the large number of genes, typically thousands. Moreover, conventional methods from machine learning lead to decisions which are usually very difficult to interpret in simple or biologically meaningful terms. In contrast, the TSP classifier provides decision rules which i) involve very few genes and only relative expression values (e.g., comparing the mRNA counts within a single pair of genes); ii) are both accurate and transparent; and iii) provide specific hypotheses for follow-up studies. In particular, the TSP classifier achieves prediction rates with standard cancer data that are as high as those of previous studies which use considerably more genes and complex procedures. Finally, the TSP classifier is parameter-free, thus avoiding the type of over-fitting and inflated estimates of performance that result when all aspects of learning a predictor are not properly cross-validated.

Journal Article↗
PubMed2004

Computational cardiac anatomy using MRI.

Ventricular geometry and fiber orientation may undergo global or local remodeling in cardiac disease. However, there are as yet no mathematical and computational methods for quantifying variation of geometry and fiber orientation or the nature of their remodeling in disease. Toward this goal, a landmark and image intensity-based large deformation diffeomorphic metric mapping (LDDMM) method to transform heart geometry into common coordinates for quantification of shape and form was developed. Two automated landmark placement methods for modeling tissue deformations expected in different cardiac pathologies are presented. The transformations, computed using the combined use of landmarks and image intensities, yields high-registration accuracy of heart anatomies even in the presence of significant variation of cardiac shape and form. Once heart anatomies have been registered, properties of tissue geometry and cardiac fiber orientation in corresponding regions of different hearts may be quantified.

Algorithms↗
PubMed2004

Gene expression profiles in end-stage human idiopathic dilated cardiomyopathy: altered expression of apoptotic and cytoskeletal genes.

Dilated cardiomyopathy is now the leading cause of cardiovascular morbidity and mortality. While the molecular basis of this disease remains uncertain, evidence is emerging that gene expression profiles of left ventricular myocardium isolated from failing versus nonfailing patients differ dramatically. In this study, we use high-density oligonucleotide microarrays with approximately 22000 probes to characterize differences in the expression profiles further. To facilitate interpretation of experimental data, we evaluate algorithms for normalization of hybridization data and for computation of gene expression indices using a control spike-in data set. We then use these methods to identify statistically significant changes in the expression levels of genes not previously implicated in the molecular phenotype of heart failure. These regulated genes take part in diverse cellular processes, including transcription, apoptosis, sarcomeric and cytoskeletal function, remodeling of the extracellular matrix, membrane transport, and metabolism.

Algorithms↗