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1.  BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data 
BMC Bioinformatics  2014;15(Suppl 12):S6.
DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer.
PMCID: PMC4243086  PMID: 25474268
DNA methylation; differential methylation; MBDCap-seq; Hidden Markov Model (HMM)
2.  Genomewide Mapping and Screening of Kaposi's Sarcoma-Associated Herpesvirus (KSHV) 3′ Untranslated Regions Identify Bicistronic and Polycistronic Viral Transcripts as Frequent Targets of KSHV MicroRNAs 
Journal of Virology  2014;88(1):377-392.
Kaposi's sarcoma-associated herpesvirus (KSHV) encodes over 90 genes and 25 microRNAs (miRNAs). The KSHV life cycle is tightly regulated to ensure persistent infection in the host. In particular, miRNAs, which primarily exert their effects by binding to the 3′ untranslated regions (3′UTRs) of target transcripts, have recently emerged as key regulators of KSHV life cycle. Although studies with RNA cross-linking immunoprecipitation approach have identified numerous targets of KSHV miRNAs, few of these targets are of viral origin because most KSHV 3′UTRs have not been characterized. Thus, the extents of viral genes targeted by KSHV miRNAs remain elusive. Here, we report the mapping of the 3′UTRs of 74 KSHV genes and the effects of KSHV miRNAs on the control of these 3′UTR-mediated gene expressions. This analysis reveals new bicistronic and polycistronic transcripts of KSHV genes. Due to the 5′-distal open reading frames (ORFs), KSHV bicistronic or polycistronic transcripts have significantly longer 3′UTRs than do KSHV monocistronic transcripts. Furthermore, screening of the 3′UTR reporters has identified 28 potential new targets of KSHV miRNAs, of which 11 (39%) are bicistronic or polycistronic transcripts. Reporter mutagenesis demonstrates that miR-K3 specifically targets ORF31-33 transcripts at the lytic locus via two binding sites in the ORF33 coding region, whereas miR-K10a-3p and miR-K10b-3p and their variants target ORF71-73 transcripts at the latent locus through distinct binding sites in both 5′-distal ORFs and intergenic regions. Our results indicate that KSHV miRNAs frequently target the 5′-distal coding regions of bicistronic or polycistronic transcripts and highlight the unique features of KSHV miRNAs in regulating gene expression and life cycle.
PMCID: PMC3911756  PMID: 24155407
3.  Exome-based analysis for RNA epigenome sequencing data 
Bioinformatics  2013;29(12):1565-1567.
Motivation: Fragmented RNA immunoprecipitation combined with RNA sequencing enabled the unbiased study of RNA epigenome at a near single-base resolution; however, unique features of this new type of data call for novel computational techniques.
Result: Through examining the connections of RNA epigenome sequencing data with two well-studied data types, ChIP-Seq and RNA-Seq, we unveiled the salient characteristics of this new data type. The computational strategies were discussed accordingly, and a novel data processing pipeline was proposed that combines several existing tools with a newly developed exome-based approach ‘exomePeak’ for detecting, representing and visualizing the post-transcriptional RNA modification sites on the transcriptome.
Availability: The MATLAB package ‘exomePeak’ and additional details are available at
Contact: or
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3673212  PMID: 23589649
4.  Integrative Genomics and Computational Systems Medicine 
BioMed Research International  2014;2014:945253.
PMCID: PMC4082850  PMID: 25025078
5.  Alteration of Fractional Anisotropy and Mean Diffusivity in Glaucoma: Novel Results of a Meta-Analysis of Diffusion Tensor Imaging Studies 
PLoS ONE  2014;9(5):e97445.
We hypothesized that a meta-analysis of existing studies may help to reveal significant changes on diffusion tensor imaging (DTI) in patients with glaucoma. Therefore, a meta-analysis was utilized to investigate the possibility that DTI can detect white matter damage in patients with glaucoma.
The study design and report adhered to the PRISMA Statement guidelines. DTI studies that compared glaucoma patients and controls were surveyed using PubMed, Web of Science and EMBASE (January 2008 to September 2013). Stata was used to analyze the decrease in fractional anisotropy (FA) and increase in mean diffusivity (MD) in the optic nerve and optic radiation in patients with glaucoma.
Eleven DTI studies were identified through a comprehensive literature search, and 10 independent DTI studies of glaucoma patients were eligible for the meta-analysis. A random effects model revealed a significant FA reduction in the optic nerve and optic radiation, as well as a significant MD increase in the tracts. A heterogeneity analysis suggested that FA may be related to glaucoma severity.
Our findings revealed that the optic nerve and optic radiation were vulnerable regions in patients with glaucoma and that FA may be correlated with glaucoma severity and age. Furthermore, this study suggests that magnetic resonance imaging in patients with glaucoma may help to provide objective evidence to aid in the diagnosis and management of glaucoma.
PMCID: PMC4020845  PMID: 24828063
6.  Correction: KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways 
PLoS Pathogens  2014;10(1):10.1371/annotation/582f0298-9999-46ea-be7e-6718608b11c5.
PMCID: PMC3899379
7.  Aging interferes central control mechanism for eccentric muscle contraction 
Previous studies report greater activation in the cortical motor network in controlling eccentric contraction (EC) than concentric contraction (CC) despite lower muscle activation level associated with EC vs. CC in healthy, young individuals. It is unknown, however, whether elderly people exhibiting increased difficulties in performing EC than CC possess this unique cortical control mechanism for EC movements. To address this question, we examined functional magnetic resonance imaging (fMRI) data acquired during EC and CC of the first dorsal interosseous (FDI) muscle in 11 young (20–32 years) and 9 old (67–73 years) individuals. During the fMRI experiment, all subjects performed 20 CC and 20 EC of the right FDI with the same angular distance and velocity. The major findings from the behavioral and fMRI data analysis were that (1) movement stability was poorer in EC than CC in the old but not the young group; (2) similar to previous electrophysiological and fMRI reports, the EC resulted in significantly stronger activation in the motor control network consisting of primary, secondary and association motor cortices than CC in the young and old groups; (3) the biased stronger activation towards EC was significantly greater in the old than the young group especially in the secondary and association cortices such as supplementary and premotor motor areas and anterior cingulate cortex; and (4) in the primary motor and sensory cortices, the biased activation towards EC was significantly greater in the young than the old group. Greater activation in higher-order cortical fields for controlling EC movement by elderly adults may reflect activities in these regions to compensate for aging-related impairments in the ability to control complex EC movements. Our finding is useful for potentially guiding the development of targeted therapies to counteract age-related movement deficits and to prevent injury.
PMCID: PMC4023019  PMID: 24847261
brain activation; concentric contraction; eccentric contraction; fMRI; movement stability
8.  KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways 
PLoS Pathogens  2013;9(12):e1003857.
Kaposi's sarcoma-associated herpesvirus (KSHV) is causally linked to several human cancers, including Kaposi's sarcoma, primary effusion lymphoma and multicentric Castleman's disease, malignancies commonly found in HIV-infected patients. While KSHV encodes diverse functional products, its mechanism of oncogenesis remains unknown. In this study, we determined the roles KSHV microRNAs (miRs) in cellular transformation and tumorigenesis using a recently developed KSHV-induced cellular transformation system of primary rat mesenchymal precursor cells. A mutant with a cluster of 10 precursor miRs (pre-miRs) deleted failed to transform primary cells, and instead, caused cell cycle arrest and apoptosis. Remarkably, the oncogenicity of the mutant virus was fully restored by genetic complementation with the miR cluster or several individual pre-miRs, which rescued cell cycle progression and inhibited apoptosis in part by redundantly targeting IκBα and the NF-κB pathway. Genomic analysis identified common targets of KSHV miRs in diverse pathways with several cancer-related pathways preferentially targeted. These works define for the first time an essential viral determinant for KSHV-induced oncogenesis and identify NF-κB as a critical pathway targeted by the viral miRs. Our results illustrate a common theme of shared functions with hierarchical order among the KSHV miRs.
Author Summary
Kaposi's sarcoma-associated herpesvirus (KSHV) is the causal agent of several human cancers. KSHV encodes over two dozen genes that regulate diverse cellular pathways. However, the molecular mechanism of KSHV-induced oncogenesis remains unknown. In this study, we determined the roles of KSHV microRNAs (miRs) in KSHV-induced oncogenesis using a recently developed KSHV cellular transformation system of primary rat mesenchymal precursor cells. A KSHV mutant with a cluster of 10 precursor miRs (pre-miRs) deleted failed to transform primary cells, and instead, caused cell cycle arrest and apoptosis. Expression of the miR cluster or several pre-miRs was sufficient to restore the oncogenicity of the mutant virus. KSHV miRs regulated cell cycle progression and inhibited apoptosis in part by redundantly targeting IκBα and the NF-κB pathway. By integrating gene expression profiling and target prediction, we identified common targets of KSHV miRs in diverse pathways. Importantly, several cancer-related pathways were preferentially targeted by KSHV miRs. These works have demonstrated for the first time the important roles of KSHV miRs in oncogenesis and identified NF-κB as a critical pathway targeted by the miRs. Our results reveal that shared function is a common theme of KSHV miRs, which manifest functional hierarchical order.
PMCID: PMC3873467  PMID: 24385912
9.  Interdisciplinary dialogue for education, collaboration, and innovation: Intelligent Biology and Medicine in and beyond 2013 
BMC Genomics  2013;14(Suppl 8):S1.
The 2013 International Conference on Intelligent Biology and Medicine (ICIBM 2013) was held on August 11-13, 2013 in Nashville, Tennessee, USA. The conference included six scientific sessions, two tutorial sessions, one workshop, two poster sessions, and four keynote presentations that covered cutting-edge research topics in bioinformatics, systems biology, computational medicine, and intelligent computing. Here, we present a summary of the conference and an editorial report of the supplements to BMC Genomics and BMC Systems Biology that include 19 research papers selected from ICIBM 2013.
PMCID: PMC4042234  PMID: 24564388
10.  BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database 
BMC Systems Biology  2013;7(Suppl 5):S5.
Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.
Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.
BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.
The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.
Website: The web based application is developed and can be access through the following link
PMCID: PMC4029357  PMID: 24564956
Connectivity Map; Mode of Action (MoA); Breast Cancer Mode of Action Network (BRCA-MoNet)
11.  Correction: Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors 
PLoS ONE  2013;8(9):10.1371/annotation/a076115e-dd8c-4da7-989d-c1174a8cd31e.
PMCID: PMC3792147  PMID: 24116246
12.  Correction: Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors 
PLoS ONE  2013;8(8):10.1371/annotation/28592478-72f5-4937-919b-b2342d6ceda0.
PMCID: PMC3752034
13.  Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors 
PLoS ONE  2013;8(8):e70204.
The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies.
Methodology/Principal Findings
It is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted k most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates.
The superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted k most similar neighbors. The online prediction and analysis tool is freely available at
PMCID: PMC3738541  PMID: 23950912
15.  A Kaposi's Sarcoma-Associated Herpesvirus MicroRNA and Its Variants Target the Transforming Growth Factor β Pathway To Promote Cell Survival 
Journal of Virology  2012;86(21):11698-11711.
Transforming growth factor β (TGF-β) signaling regulates cell growth and survival. Dysregulation of the TGF-β pathway is common in viral infection and cancer. Latent infection by Kaposi's sarcoma-associated herpesvirus (KSHV) is required for the development of several AIDS-related malignancies, including Kaposi's sarcoma and primary effusion lymphoma (PEL). KSHV encodes more than two dozen microRNAs (miRs) derived from 12 pre-miRs with largely unknown functions. In this study, we show that miR variants processed from pre-miR-K10 are expressed in KSHV-infected PEL cells and endothelial cells, while cellular miR-142-3p and its variant miR-142-3p_-1_5, which share the same seed sequence with miR-K10a_ +1_5, are expressed only in PEL cells and not in uninfected and KSHV-infected TIME cells. KSHV miR-K10 variants inhibit TGF-β signaling by targeting TGF-β type II receptor (TβRII). Computational and reporter mutagenesis analyses identified three functional target sites in the TβRII 3′ untranslated region (3′UTR). Expression of miR-K10 variants is sufficient to inhibit TGF-β-induced cell apoptosis. A suppressor of the miRs sensitizes latent KSHV-infected PEL cells to TGF-β and induces apoptosis. These results indicate that miR-K10 variants manipulate the TGF-β pathway to confer cells with resistance to the growth-inhibitory effect of TGF-β. Thus, KSHV miRs might target the tumor-suppressive TGF-β pathway to promote viral latency and contribute to malignant cellular transformation.
PMCID: PMC3486299  PMID: 22915806
16.  De-Regulated MicroRNAs in Pediatric Cancer Stem Cells Target Pathways Involved in Cell Proliferation, Cell Cycle and Development 
PLoS ONE  2013;8(4):e61622.
microRNAs (miRNAs) have been implicated in the control of many biological processes and their deregulation has been associated with many cancers. In recent years, the cancer stem cell (CSC) concept has been applied to many cancers including pediatric. We hypothesized that a common signature of deregulated miRNAs in the CSCs fraction may explain the disrupted signaling pathways in CSCs.
Using a high throughput qPCR approach we identified 26 CSC associated differentially expressed miRNAs (DEmiRs). Using BCmicrO algorithm 865 potential CSC associated DEmiR targets were obtained. These potential targets were subjected to KEGG, Biocarta and Gene Ontology pathway and biological processes analysis. Four annotated pathways were enriched: cell cycle, cell proliferation, p53 and TGF-beta/BMP. Knocking down hsa-miR-21-5p, hsa-miR-181c-5p and hsa-miR-135b-5p using antisense oligonucleotides and small interfering RNA in cell lines led to the depletion of the CSC fraction and impairment of sphere formation (CSC surrogate assays).
Our findings indicated that CSC associated DEmiRs and the putative pathways they regulate may have potential therapeutic applications in pediatric cancers.
PMCID: PMC3629228  PMID: 23613887
17.  Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis 
Advances in Bioinformatics  2013;2013:360678.
Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.
PMCID: PMC3610383  PMID: 23573084
18.  Advances in systems biology: computational algorithms and applications 
BMC Systems Biology  2012;6(Suppl 3):S1.
The 2012 International Conference on Intelligent Biology and Medicine (ICIBM 2012) was held on April 22-24, 2012 in Nashville, Tennessee, USA. The conference featured six technical sessions, one tutorial session, one workshop, and 3 keynote presentations that covered state-of-the-art research activities in genomics, systems biology, and intelligent computing. In addition to a major emphasis on the next generation sequencing (NGS)-driven informatics, ICIBM 2012 aligned significant interests in systems biology and its applications in medicine. We highlight in this editorial the selected papers from the meeting that address the developments of novel algorithms and applications in systems biology.
PMCID: PMC3524016  PMID: 23281622
19.  A Bayesian decision fusion approach for microRNA target prediction 
BMC Genomics  2012;13(Suppl 8):S13.
MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network. BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm. All the related materials including genome-wide prediction of human targets and a web-based tool are available at
PMCID: PMC3535698  PMID: 23282032
20.  Genomics in 2012: challenges and opportunities in the next generation sequencing era 
BMC Genomics  2012;13(Suppl 8):S1.
We present a report of the 2012 International Conference on Intelligent Biology and Medicine (ICIBM 2012) and the editorial report of the supplement to BMC Genomics that includes 22 research papers selected from ICIBM 2012, which was held on April 22-24, 2012 in Nashville, Tennessee, USA. The conference covered a variety of research areas, including bioinformatics, systems biology, and intelligent computing. It included six sessions, a tutorial - Introduction to Proteome Informatics, a workshop - Next Generation Sequencing, and a poster session. The selected papers in this Supplement issue represent the genomic focus in ICIBM 2012.
PMCID: PMC3535713  PMID: 23281891
21.  A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles 
BMC Genomics  2012;13(Suppl 6):S20.
DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic.
A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters.
The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.
PMCID: PMC3481479  PMID: 23134689
23.  Characterization and Robust Classification of EEG Signal from Image RSVP Events with Independent Time-Frequency Features 
PLoS ONE  2012;7(9):e44464.
This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied to EEG recordings of a RSVP experiment with multiple sessions and subjects.
The results show that the target image events are mainly characterized by 3 distinct patterns in the time-frequency domain, i.e., a theta band (4.3 Hz) power boosting 300–700 ms after the target image onset, an alpha band (12 Hz) power boosting 500–1000 ms after the stimulus onset, and a delta band (2 Hz) power boosting after 500 ms. The most discriminant time-frequency features are power boosting and are relatively consistent among multiple sessions and subjects.
Since the original discriminant time-frequency features are highly correlated, we constructed the uncorrelated features using hierarchical clustering for better classification of target and non-target images. With feature clustering, performance (area under ROC) improved from 0.85 to 0.89 on within-session tests, and from 0.76 to 0.84 on cross-subject tests. The constructed uncorrelated features were more robust than the original discriminant features and corresponded to a number of local regions on the time-frequency plane.
Availability: The data and code are available at:
PMCID: PMC3445552  PMID: 23028544
24.  Direct and efficient cellular transformation of primary rat mesenchymal precursor cells by KSHV 
The Journal of Clinical Investigation  2012;122(3):1076-1081.
Infections by viruses are associated with approximately 12% of human cancer. Kaposi’s sarcoma-associated herpesvirus (KSHV) is causally linked to several malignancies commonly found in AIDS patients. The mechanism of KSHV-induced oncogenesis remains elusive, due in part to the lack of an adequate experimental system for cellular transformation of primary cells. Here, we report efficient infection and cellular transformation of primary rat embryonic metanephric mesenchymal precursor cells (MM cells) by KSHV. Cellular transformation occurred at as early as day 4 after infection and in nearly all infected cells. Transformed cells expressed hallmark vascular endothelial, lymphatic endothelial, and mesenchymal markers and efficiently induced tumors in nude mice. KSHV established latent infection in MM cells, and lytic induction resulted in low levels of detectable infectious virions despite robust expression of lytic genes. Most KSHV-induced tumor cells were in a latent state, although a few showed heterogeneous expression of lytic genes. This efficient system for KSHV cellular transformation of primary cells might facilitate the study of growth deregulation mechanisms resulting from KSHV infections.
PMCID: PMC3287217  PMID: 22293176
25.  Bayesian parameter estimation for nonlinear modelling of biological pathways 
BMC Systems Biology  2011;5(Suppl 3):S9.
The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data.
We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems.
Our proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models for biological pathways. This method can be further extended to high order systems and thus provides a useful tool to analyze biological dynamics and extract information using temporal data.
PMCID: PMC3287577  PMID: 22784628

Results 1-25 (46)