Biomedical subjects

Murali Ramanathan

Publications and source records attributed to Murali Ramanathan.

24 records · Page 2Linked to original sources

PubMed2003

An information theoretic approach for analyzing temporal patterns of gene expression.

MOTIVATION: Arrays allow measurements of the expression levels of thousands of mRNAs to be made simultaneously. The resulting data sets are information rich but require extensive mining to enhance their usefulness. Information theoretic methods are capable of assessing similarities and dissimilarities between data distributions and may be suited to the analysis of gene expression experiments. The purpose of this study was to investigate information theoretic data mining approaches to discover temporal patterns of gene expression from array-derived gene expression data. RESULTS: The Kullback-Leibler divergence, an information-theoretic distance that measures the relative dissimilarity between two data distribution profiles, was used in conjunction with an unsupervised self-organizing map algorithm. Two published, array-derived gene expression data sets were analyzed. The patterns obtained with the KL clustering method were found to be superior to those obtained with the hierarchical clustering algorithm using the Pearson correlation distance measure. The biological significance of the results was also examined. AVAILABILITY: Software code is available by request from the authors. All programs were written in ANSI C and Matlab (Mathworks Inc., Natick, MA).

Algorithms↗

PubMed2003

Dynamics of immune cell trafficking in interferon-beta treated multiple sclerosis patients.

PURPOSE: To investigate the effects of interferon-beta-1a (IFN-beta-1a) on the trafficking of cell populations in peripheral blood cells of multiple sclerosis (MS) patients. METHODS: In this open-label pharmacodynamic study, peripheral blood was obtained from 10 relapsing-remitting (RR) MS patients just prior to and at 1, 2, 4, 8, 24, 48, 120, and 168 h after intramuscular injection of 30-microg IFN-beta-1a. Timed samples were also obtained from five controls at 0, 8, 24, 48 and 168 h. The blood cells were analyzed using four-color flow cytometry with antibody conjugates directed against cell surface proteins specific for T cells, B cells, NK cells, and the activation marker, CD69. RESULTS: IFN-beta-1a treatment resulted in selective, time-dependent effects on many cell populations in peripheral blood. The trafficking of T-helper and T-suppressor/cytotoxic subsets of T cells were qualitatively different. The most prominent effects were on the trafficking of natural killer cells, the levels of which decreased to 23.5% of pretreatment values at 8 h after treatment. The levels of CD69-positive NK cells increased to a peak value of 606% of pretreatment levels at the 24-h time point. In untreated controls, these characteristic trafficking effects were not observed. There was inter-patient heterogeneity in the levels of activated NK cells at the 6-month time point that may potentially be relevant for individualizing IFN-beta therapy. CONCLUSIONS: IFN-beta treatment can induce specific, selective, and time-dependent trafficking of cells and its effects on different subsets of a given cell type are not qualitatively similar. The dynamics indicate that the activation of NK cells by IFN-beta is possibly dependent on the trafficking of NK cells. The activated NK cell levels after prolonged therapy may potentially provide a surrogate marker for IFN-beta exposure.

Adjuvants, Immunologic↗

PubMed2003

A stochastic model for optimizing composite predictors based on gene expression profiles.

PURPOSE: This project was done to develop a mathematical model for optimizing composite predictors based on gene expression profiles from DNA arrays and proteomics. METHODS: The problem was amenable to a formulation and solution analogous to the portfolio optimization problem in mathematical finance: it requires the optimization of a quadratic function subject to linear constraints. The performance of the approach was compared to that of neighborhood analysis using a data set containing cDNA array-derived gene expression profiles from 14 multiple sclerosis patients receiving intramuscular inteferon-beta1a. RESULTS: The Markowitz portfolio model predicts that the covariance between genes can be exploited to construct an efficient composite. The model predicts that a composite is not needed for maximizing the mean value of a treatment effect: only a single gene is needed, but the usefulness of the effect measure may be compromised by high variability. The model optimized the composite to yield the highest mean for a given level of variability or the least variability for a given mean level. The choices that meet this optimization criteria lie on a curve of composite mean vs. composite variability plot referred to as the "efficient frontier." When a composite is constructed using the model, it outperforms the composite constructed using the neighborhood analysis method. CONCLUSIONS: The Markowitz portfolio model may find potential applications in constructing composite biomarkers and in the pharmacogenomic modeling of treatment effects derived from gene expression endpoints.

Adult↗

PubMed2003

Assessment of Markov-dependent stochastic models for drug administration compliance.

OBJECTIVE: There are few analytical results that describe patient compliance with drug administration regimens. The purpose of this paper is to develop and assess stochastic approaches for mathematical modelling of patient compliance with administration regimens. METHODS: Two stochastic models based on Markov-dependent random variables and on the Ising model were assessed for their ability to describe the variable nature of drug compliance. RESULTS: Both models use only experimentally accessible data, and their predictions were tested against published clinical compliance data obtained from electronic monitoring devices. The models satisfactorily fitted administration interval distribution data from several patients treated with diltiazem, a calcium channel antagonist, or zidovudine, an antiretroviral agent. The Ising model provides additional analytical expressions for the distribution of success runs and 'drug holidays' in administration regimens. These distribution predictions were tested with success run data for diltiazem and drug holiday data for two nonsteroidal anti-inflammatory drugs, piroxicam and tenoxicam. CONCLUSIONS: Stochastic models can provide useful insights into drug compliance, and can be used to identify the administration patterns that are more likely to occur during drug self-administration in populations.

Algorithms↗

PubMed2003

Fourier harmonic approach for visualizing temporal patterns of gene expression data.

DNA microarray technology provides a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex dataset by presenting the data in a graphical format in which the key characteristics become more apparent. The purpose of this study is to present an interactive visualization technique conveying the temporal patterns of gene expression data in a form intuitive for non-specialized end-users. The first Fourier harmonic projection (FFHP) was introduced to translate the multi-dimensional time series data into a two dimensional scatter plot. The spatial relationship of the points reflect the structure of the original dataset and relationships among clusters become two dimensional. The proposed method was tested using two published, array-derived gene expression datasets. Our results demonstrate the effectiveness of the approach.

Algorithms↗

PubMed2002

A dispersion model for cellular signal transduction cascades.

PURPOSE: The purpose of this study was to evaluate the ability of the dispersion model to describe pharmacokinetic-pharmacodynamic data containing contributions from signal transduction cascades. METHODS: The partial differential equations and appropriate boundary conditions describing the dispersion model for signal transduction were obtained. Explicit analytical solutions to the dispersion equation were not available, and a numerical approach was necessary. Solutions were obtained by numerical inversion of the output Laplace transform. Generalized least square fitting was used to obtain parameter estimates for a variety of experimental data sets. RESULTS: The parameters of the dispersion model estimate the relative roles of diffusion, convection, and chemical reaction in signal transduction. The model is capable of describing messenger RNA and protein expression kinetics induced by drug action. CONCLUSIONS: The dispersion model may find potential applications in pharmacokinetic-pharmacodynamic models involving delayed drug effects mediated by transcriptional changes.

Models, Biological↗