scholarly journals Effective Single-Step Posttranscriptional Dynamics Allowing for a Direct Maximum Likelihood Estimation of Transcriptional Activity and the Quantification of Sources of Gene Expression Variability with an Illustration for the Hypoxia and TNFα Regulated Inflammatory Pathway

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
T. D. Frank ◽  
A. J. F. Collins ◽  
A. Cheong
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Maximum Likelihood ◽  
Transcriptional Activity ◽  
Search Algorithms ◽  
Hek293 Cells ◽  
Model Parameters ◽  
Inflammatory Pathway ◽  
Expression Variability ◽  
Model Based

Data analysis methods for estimating promoter activity from gene reporter data frequently involve the reconstruction of the dynamics of unobserved species and numerical search algorithms for determining optimal model parameters. In contrast, we argue that posttranscriptional dynamics effectively behave like a singlestep stochastic process when gene expression variability is relatively low and, half-lives of the unobserved species are relatively small compared to characteristic observation time scales. In this case, by means of maximum likelihood estimators, for which analytical expressions exist, transcriptional activity of gene promoters can be estimated directly from observed gene reporter data without the need for numerical search algorithms and the reconstruction of unobserved variables. In addition, the model-based data analysis approach yields a single variable that measures the effective strength of the sources that give rise to gene expression variability. The approach is applied to conduct a model-based analysis of the inflammatory pathway under hypoxia condition and stimulation with tumor necrosis factor alpha in HEK293 cells.

scholarly journals A Square-Root Factor-Based Multi-Population Extension of the Mortality Laws

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2402
Author(s):  
Petar Jevtić ◽  
Luca Regis
Keyword(s):  
Maximum Likelihood ◽  
Model Parameters ◽  
Time Varying ◽  
Square Root ◽  
Stochastic Mortality ◽  
Mortality Model ◽  
Model Based ◽  
Males And Females ◽  
Quasi Maximum Likelihood ◽  
Stochastic Mortality Model

In this paper, we present and calibrate a multi-population stochastic mortality model based on latent square-root affine factors of the Cox-Ingersoll and Ross type. The model considers a generalization of the traditional actuarial mortality laws to a stochastic, multi-population and time-varying setting. We calibrate the model to fit the mortality dynamics of UK males and females over the last 50 years. We estimate the optimal states and model parameters using quasi-maximum likelihood techniques.

DYNAMIC MODEL-BASED CLUSTERING FOR TIME–COURSE GENE EXPRESSION DATA

2005 ◽  
Vol 03 (04) ◽  
pp. 821-836 ◽  
Author(s):  
FANG-XIANG WU ◽  
W. J. ZHANG ◽  
ANTHONY J. KUSALIK
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Dynamic Model ◽  
Gene Expression Data ◽  
Time Course ◽  
Disease Diagnosis ◽  
Model Parameters ◽  
Expression Data ◽  
Model Based Clustering ◽  
Model Based

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.

scholarly journals The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data

2017 ◽  
Vol 16 ◽  
pp. 117693511771053 ◽  
Author(s):  
Eunji Kim ◽  
Ivan Ivanov ◽  
Jianping Hua ◽  
Johanna W Lampe ◽  
Meredith AJ Hullar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Measurement Technology ◽  
Model Parameters ◽  
Rna Seq ◽  
Error Estimators ◽  
Feature Sets ◽  
Model Based ◽  
Sequencing Technologies ◽  
Phenotype Classification ◽  
Small Feature

Ranking feature sets for phenotype classification based on gene expression is a challenging issue in cancer bioinformatics. When the number of samples is small, all feature selection algorithms are known to be unreliable, producing significant error, and error estimators suffer from different degrees of imprecision. The problem is compounded by the fact that the accuracy of classification depends on the manner in which the phenomena are transformed into data by the measurement technology. Because next-generation sequencing technologies amount to a nonlinear transformation of the actual gene or RNA concentrations, they can potentially produce less discriminative data relative to the actual gene expression levels. In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of finding a good feature set becomes weaker when gene concentrations are transformed by the sequencing machine.

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

Review of Progress in Predicting Protein Methylation Sites

2019 ◽  
Vol 23 (15) ◽  
pp. 1663-1670 ◽  
Author(s):  
Chunyan Ao ◽  
Shunshan Jin ◽  
Yuan Lin ◽  
Quan Zou
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
Transcriptional Activity ◽  
Regulation Of Gene Expression ◽  
Protein Methylation ◽  
Biological Processes ◽  
Post Translational Modification ◽  
Regulatory Enzymes ◽  
Lysine Residues ◽  
Arginine Residues

Protein methylation is an important and reversible post-translational modification that regulates many biological processes in cells. It occurs mainly on lysine and arginine residues and involves many important biological processes, including transcriptional activity, signal transduction, and the regulation of gene expression. Protein methylation and its regulatory enzymes are related to a variety of human diseases, so improved identification of methylation sites is useful for designing drugs for a variety of related diseases. In this review, we systematically summarize and analyze the tools used for the prediction of protein methylation sites on arginine and lysine residues over the last decade.

scholarly journals Regulation of c-Fos Gene Expression by NF-κB: A p65 Homodimer Binding Site in Mouse Embryonic Fibroblasts but Not Human HEK293 Cells

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e84062 ◽  
Author(s):  
Yu-Cheng Tu ◽  
Duen-Yi Huang ◽  
Shine-Gwo Shiah ◽  
Jang-Shiun Wang ◽  
Wan-Wan Lin
Keyword(s):  
Gene Expression ◽  
Binding Site ◽  
Hek293 Cells ◽  
Mouse Embryonic Fibroblasts ◽  
Embryonic Fibroblasts

scholarly journals A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Frauke Kachholz ◽  
Jens Tränckner
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Small Rivers ◽  
Small Scale ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Based ◽  
Current State ◽  
The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

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