Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

The expanding roles of the ghrelin-gene derived peptide obestatin in health and disease

Obestatin is a 23 amino acid, ghrelin gene-derived peptide hormone produced in the stomach and a range of other tissues throughout the body. While it was initially reported that obestatin opposed the actions of ghrelin with regards to appetite and food intake, it is now clear that obestatin is not an endogenous ghrelin antagonist of ghrelin, but it is a multi-functional peptide hormone in its own right. In this review we will discuss the controversies associated with the discovery of obestatin and explore emerging central and peripheral roles of obestatin, roles in adipogenesis, pancreatic homeostasis and cancer.

A variant of the KLK4 gene is expressed as a cis sense-antisense chimeric transcript in prostate cancer cells

In humans, more than 30,000 chimeric transcripts originating from 23,686 genes have been identified. The mechanisms and association of chimeric transcripts arising from chromosomal rearrangements with cancer are well established, but much remains unknown regarding the biogenesis and importance of other chimeric transcripts that arise from nongenomic alterations. Recently, a SLC45A3–ELK4 chimera has been shown to be androgen-regulated, and is overexpressed in metastatic or high-grade prostate tumors relative to local prostate cancers. Here, we characterize the expression of a KLK4 cis sense–antisense chimeric transcript, and show other examples in prostate cancer. Using non-protein-coding microarray analyses, we initially identified an androgen-regulated antisense transcript within the 3′ untranslated region of the KLK4 gene in LNCaP cells. The KLK4 cis-NAT was validated by strand-specific linker-mediated RT-PCR and Northern blotting. Characterization of the KLK4 cis-NAT by 5′ and 3′ rapid amplification of cDNA ends (RACE) revealed that this transcript forms multiple fusions with the KLK4 sense transcript. Lack of KLK4 antisense promoter activity using reporter assays suggests that these transcripts are unlikely to arise from a trans-splicing mechanism. 5′ RACE and analyses of deep sequencing data from LNCaP cells treated ±androgens revealed six high-confidence sense–antisense chimeras of which three were supported by the cDNA databases. In this study, we have shown complex gene expression at the KLK4 locus that might be a hallmark of cis sense–antisense chimeric transcription.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This article provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox–Ingersoll–Ross and Ornstein–Uhlenbeck equations respectively.

Expression of PSA-RP2, an alternatively spliced variant from the PSA gene, is increased in prostate cancer tissues but the protein is not secreted from prostate cancer cells

PSA-RP2 is a variant transcript expressed from the PSA gene that is conserved in gorillas, chimpanzees and humans suggesting a particular relevance for this transcript in these primates. We demonstrated by qRT-PCR that PSA-RP2 is upregulated in prostate cancer compared with benign prostatic hyperplasia tissues. The PSA-RP2 protein was not detected in seminal fluid and was cytoplasmically localised but not secreted from LNCaP or transfected PC3 prostate cells, despite secretion from transfected Cos-7 and HEK293 kidney cell lines. PSA-RP2-transfected PC3 cells showed slightly decreased proliferation and increased migration towards PC3-conditioned medium that could suggest a functional role in prostate cancer.