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Murali Ramanathan

Publications and source records attributed to Murali Ramanathan.

17 recordsLinked to original sources
PubMed2006

VizStruct for visualization of genome-wide SNP analyses.

MOTIVATION: The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing patterns in SNP datasets. RESULTS: VizStruct is an interactive visualization technique that reduces multi-dimensional data to two dimensions using the complex-valued harmonics of the discrete Fourier transform (DFT). In the 3D VizStruct extension, the multi-dimensional SNP data vectors are reduced to three dimensions using a combination of the DFT and the Kullback-Leibler divergence. The performance of 3D VizStruct was challenged with several biologically relevant published datasets that included human Chromosome 21, the human lipoprotein lipase (LPL) gene locus and the multi-locus genotypes of coral populations. In every case, the 3D VizStruct mapping provided an intuitive visual description of the key characteristics of the underlying multi-dimensional genotype.

Animals↗
PubMed2006

Application of SPLINDID, a semiparametric, model-based method for pharmacogenomic modeling of mRNA dynamics.

PURPOSE: This study was conducted to evaluate the applicability of SPLINDID, a semiparametric, model-based approach for obtaining transcription rates from the pharmacodynamics of mRNA expression. METHODS: A nonparametric exponential cubic spline function was used to obtain the transcription rate profile and the dynamics of mRNA expression was fitted using compartmental approaches. The transcription rate profile and mRNA degradation parameter was estimated using maximum likelihood method of ADAPT II software. RESULTS: Data sets containing noise for mRNA levels were simulated for four diverse pharmaceutically relevant conditions: receptor nonlinearity, a model in which the variant mRNAs differing in mRNA degradation constants were transcribed and for a minimal model of the cell cycle. SPLINDID was able to fit the data sets and accurately recapitulate the transcription rate profiles normalized to the mRNA degradation rate constants. The model was also challenged using experimental data containing time profiles of cell-cycle-regulated genes. CONCLUSIONS: The SPLINDID approach is flexible in capturing complicated/complex mRNA profiles that are encountered in many experimental data sets.

Algorithms↗
PubMed2005

SPLINDID: a semi-parametric, model-based method for obtaining transcription rates and gene regulation parameters from genomic and proteomic expression profiles.

PURPOSE: To evaluate a semi-parametric, model-based approach for obtaining transcription rates from mRNA and protein expression. METHODS: The transcription profile input was modeled using an exponential function of a cubic spline and the dynamics of translation; mRNA and protein degradation were modeled using the Hargrove-Schmidt model. The transcription rate profile and the translation, and mRNA and protein degradation rate constants were estimated by the maximum likelihood method. RESULTS: Simulated datasets generated from the stochastic, transit compartment and dispersion signaling models were used to test the approach. The approach satisfactorily fit the mRNA and protein data, and accurately recapitulated the parameter and the normalized transcription rate profile values. The approach was successfully used to model published data on tyrosine aminotransferase pharmacodynamics. CONCLUSIONS: The semi-parametric approach is effective and could be useful for delineating the genomic effects of drugs. AVAILABILITY: Code suitable for use with the ADAPT software program is available from the corresponding author. CONTACT: murali@acsu.buffalo.edu.

Algorithms↗
PubMed2005

Low fat dietary intervention with omega-3 fatty acid supplementation in multiple sclerosis patients.

OBJECTIVES: To determine whether a low fat diet supplemented with omega-3 positively affects quality of life (QOL) in relapsing-remitting MS (RRMS) patients. In this 1-year long double-blind, randomized trial, patients were randomized to two dietary interventions: the "Fish Oil" (FO) group received a low fat diet (15% fat) with omega-3 FOs and the "Olive Oil" (OO) group received the AHA Step I diet (fat 30%) with OO supplements. The primary outcome measure was the Physical Components Summary Scale (PCS) of the Short Health Status Questionnaire (SF-36). Additional measures using MS specific QOL questionnaires, neurological status and relapse rate were obtained. RESULTS: 31 RRMS patients were enrolled, with mean follow up over 11 +/- SD 2.9 months. Clinical benefits favoring the FO group were observed on PCS/SF-36 (P = 0.050) and MHI (P = 0.050) at 6 months. Reduced fatigue was seen on the OO diet at 6 months (P = 0.035). The relapse rate decreased in both groups relative to the rates during the 1 year preceding the study: mean change in relapse rate in the FO group: -0.79 +/- SD 1.12 relapses/year (P = 0.021) vs. -0.69 +/- SD 1.11 (P = 0.044) in the OO group. This study suggests that a low fat diet supplemented with omega-3 PUFA can have moderate benefits in RRMS patients on concurrent disease modifying therapies.

Adolescent↗
PubMed2004

ESPD: a pattern detection model underlying gene expression profiles.

MOTIVATION: DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in high-dimensional gene space. Furthermore, most of the genes collected may not necessarily be of interest and uncertainty about which genes are relevant makes it difficult to construct an informative gene space. Unsupervised empirical sample pattern discovery and informative genes identification of such sparse high-dimensional datasets present interesting but challenging problems. RESULTS: A new model called empirical sample pattern detection (ESPD) is proposed to delineate pattern quality with informative genes. By integrating statistical metrics, data mining and machine learning techniques, this model dynamically measures and manipulates the relationship between samples and genes while conducting an iterative detection of informative space and the empirical pattern. The performance of the proposed method with various array datasets is illustrated.

Algorithms↗
PubMed2004

VizStruct: exploratory visualization for gene expression profiling.

MOTIVATION: DNA arrays provide 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 data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.

Algorithms↗
PubMed2004

Analysis of pharmacokinetics, pharmacodynamics, and pharmacogenomics data sets using VizStruct, a novel multidimensional visualization technique.

PURPOSE: Data visualization techniques for the pharmaceutical sciences have not been extensively investigated. The purpose of this study was to evaluate the usefulness of VizStruct, a multidimensional visualization tool, for applications in pharmacokinetics, pharmacodynamics, and pharmacogenomics. METHODS: The VizStruct tool uses the first harmonic of the discrete Fourier transform to map multidimensional data to two dimensions for visualization. The mapping was used to visualize several published pharmacokinetic, pharmacodynamic, and pharmacogenomic data sets. The VizStruct approach was evaluated using simulated population pharmacokinetics data sets, the data from Dalen and colleagues (Clin. PharmacoL Ther. 63:444-452, 1998) on the kinetics of nortriptyline and its 10-hydroxynortriptyline metabolite in subjects with differing number of copies of the CYP2D6, and the gene expression profiling data of Bohen and colleagues (Proc. Natl. Acad. Sci. USA 100:1926-1930, 2003) on follicular lymphoma patients responsive and nonresponsive to rituximab. RESULTS: The VizStruct mapping preserves the key characteristics of multidimensional data in two dimensions in a manner that facilitates visualization. The mapping is computationally efficient and can be used for cluster detection and class prediction in pharmaceutical data sets. The VizStruct visualization succinctly summarized the salient similarities and differences in the nortriptyline and 10-hydroxynortriptyline pharmacokinetic profiles in subjects with increasing number of CYP2D6 gene copies. In the simulated population pharmacokinetic data sets, it was capable of discriminating the subtle differences between pharmacokinetic profiles derived from 1- and 2-compartment models with the same area under the curve. The two-dimensional VizStruct mapping computed from a subset of 102 informative genes from the Bohen and colleagues data set effectively separated the rituximab responder, rituximab nonresponder, and control subject groups. CONCLUSIONS: The VizStruct approach is a computationally efficient and effective approach for visualizing complex, multidimensional data sets. It could have many useful applications in the pharmaceutical sciences.

Algorithms↗
PubMed2004

Risk of bone loss in men with multiple sclerosis.

CONTEXT: Osteoporosis and the increased fracture risk associated with osteoporosis become apparent in men approximately 10 years later than women. However, in recent studies, approximately 20% of healthy men in the age range 55-64 years were found to be osteopenic. Emerging data suggest a significantly increased prevalence of osteoporosis in men and women with multiple sclerosis (MS) compared to age-matched controls, but no specific clinical testing recommendations are available for men. OBJECTIVE: To determine the proportion of male MS patients with osteoporosis and to identify the factors associated with the reduction in bone mass. DESIGN: Consecutive male MS patients seen at our MS clinic were screened with dual-X-ray absorptiometry (DEXA) scan for determining the bone mineral density (BMD). All patients had neurological Expanded Disability Status Scale (EDSS) evaluation. The results were compared to healthy age-matched male reference population using the Z score and to a cohort of women MS patients and women controls. Calcium, total testosterone, sex-hormone binding globulin (SHBG), 25-hydroxy-vitamin-D, and parathyroid hormone (PTH) were evaluated in male patients with decreased BMD. Relevant data on body mass index (BMI), medication, alcohol consumption, smoking, and sexual dysfunction were recorded. SETTING: Academic MS Centre. PATIENTS AND OTHER PARTICIPANTS: Forty consecutive male MS patients, age mean 51.2 +/- 8.7 years, and mean EDSS of 5.8 +/- 1.9 were evaluated with DEXA scan. Of these, 17.5% patients were relapsing-remitting (RR) MS, 57.5% were secondary progressive (SP) MS and 25% were primary progressive (PP) MS. MAIN OUTCOME MEASURE: Proportion of male MS patients with reduced BMD at the lumbar spine and femoral neck. RESULTS: Thirty-two (80%) of our patients had a reduced bone mass of either lumbar spine or the femoral neck; of these 17 patients (42.5%) had osteopenia and 15 patients (37.5%) had osteoporosis. Twenty-one per cent (eight out of 38 patients) had vertebral, rib or extremities fractures. Multivariate linear regression analysis indicated that the EDSS (P < 0.0001) and BMI (P = 0.0004) were the important factors associated with low BMD at the femoral neck and the EDSS was the important factor (P = 0.0017) associated with low BMD at the lumbar spine. The same factors emerged as significantly associated with the corresponding Z scores, which are corrected for age and sex. No clear association between intravenous steroid therapy and BMD was evident in the multivariate analysis. Low levels of 25-hydroxy-vitamin-D were seen in 37.5% of patients. CONCLUSIONS: The proportion of male MS patients with reduced bone mass is high and disproportionate to their age and ambulation, consistent with an association between the MS disease process and pathological bone loss. Increased awareness and bone density screening of male and female MS patients over 40 years of age is warranted.

Absorptiometry, Photon↗
PubMed2003

Genomic effects of IFN-beta in multiple sclerosis patients.

The purpose of this report was to characterize the dynamics of the gene expression cascades induced by an IFN-beta-1a treatment regimen in multiple sclerosis patients and to examine the molecular mechanisms potentially capable of causing heterogeneity in response to therapy. In this open-label pharmacodynamic study design, peripheral blood was obtained from eight relapsing-remitting multiple sclerosis patients just before and at 1, 2, 4, 8, 24, 48, 120, and 168 h after i.m. injection of 30 micro g of IFN-beta-1a. The total RNA was isolated from monocyte-depleted PBL and analyzed using cDNA microarrays containing probes for >4000 known genes. IFN-beta-1a treatment resulted in selective, time-dependent effects on multiple genes. The mRNAs for genes implicated in the anti-viral response, e.g., double-stranded RNA-dependent protein kinase, myxovirus resistance proteins 1 and 2, and guanylate binding proteins 1 and 2 were rapidly induced within 1-4 h of IFN-beta treatment. The mRNAs for several genes involved in IFN-beta signaling, such as IFN-alpha/beta receptor-2 and Stat1, were also increased. The mRNAs for lymphocyte activation markers, such as IFN-induced transmembrane protein 1 (9-27), IFN-induced transmembrane protein 2 (1-8D), beta(2)-microglobulin, and CD69, were also increased in a time-dependent manner. The findings demonstrate that IFN-beta treatment induces specific and time-dependent changes in multiple mRNAs in lymphocytes of multiple sclerosis patients that could provide a framework for rapid monitoring of the response to therapy.

Adult↗
PubMed2003

Sex differences in in vitro pro-inflammatory cytokine production from peripheral blood of multiple sclerosis patients.

We compared the patterns of the pro-inflammatory cytokines, interferon-gamma (IFN-gamma), interleukin-2 (IL-2) and tumor necrosis factor-alpha (TNF-alpha), and the anti-inflammatory cytokines, interleukin-10 (IL-10) and tumor growth factor-beta (TGF-beta) from peripheral blood of male and female patients with relapsing-remitting (RR) and secondary progressive (SP) forms of multiple sclerosis (MS). The relationships between pro-inflammatory cytokines and disability (expanded disability status scale, EDSS) were also examined. Peripheral blood anti-coagulated with heparin was obtained from 47 MS patients (30 women and 17 men) and activated with phorbol-12-myristate 13 acetate (PMA) and ionomycin in the presence of brefeldin A and stained for flow cytometry with fluorescently labeled antibodies against intracellular IFN-gamma, TNF-alpha, IL-2, IL-4 and IL-10. The T cells were delineated with peridinin chlorophyll protein (Per-CP) labeled anti-CD3 antibody. The stained samples were analyzed on a flow cytometer to assess the intracellular pro-inflammatory cytokine patterns. The levels of interleukin-10 (IL-10) and tumor growth factor-beta (TGF-beta) were measured in plasma using enzyme-linked immunoassay. The percentage of TNF-alpha-producing CD3 positive cells was significantly higher (P=0.045) in men (mean+/-S.D., 39+/-13%) than in women (mean+/-S.D., 29+/-13%) RR-MS patients. The percentage of CD3 positive cells producing IFN-gamma was significantly correlated with EDSS in females but not in males (Spearman rank correlation r(S)=0.49, P=0.018). The secretion of the pro-inflammatory cytokines, IFN-gamma and TNF-alpha, is influenced by gender in MS patients and may contribute to the sexual dimorphism of MS.

Adolescent↗
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↗