DEVELOPMENT OF BIOINFORMATICS TOOLS AND ALGORITHMS FOR IDENTIFYING PATHWAY REGULATORS, INFERRING GENE REGULATORY RELATIONSHIPS AND VISUALIZING GENE EXPRESSION DATA

In the era of genetics and genomics, the advent of big data is transforming the field of biology into a data-intensive discipline. Novel computational algorithms and software tools are in demand to address the data analysis challenges in this growing field. This dissertation comprises the development of a novel algorithm, web-based data analysis tools, and a data visualization platform. Triple Gene Mutual Interaction (TGMI) algorithm, presented in Chapter 2 is an innovative approach to identify key regulatory transcription factors (TFs) that govern a particular biological pathway or a process through interaction among three genes in a triple gene block, which consists of a pair of pathway genes and a TF. The identification of key TFs controlling a biological pathway or a process allows biologists to understand the complex regulatory mechanisms in living organisms. TF-Miner, presented in Chapter 3, is a high-throughput gene expression data analysis web application that was developed by integrating two highly efficient algorithms; TF-cluster and TF-Finder. TF-Cluster can be used to obtain collaborative TFs that coordinately control a biological pathway or a process using genome-wide expression data. On the other hand, TF-Finder can identify regulatory TFs involved in or associated with a specific biological pathway or a process using Adaptive Sparse Canonical Correlation Analysis (ASCCA). Chapter 4 presents ExactSearch; a suffix tree based motif search algorithm, implemented in a web-based tool. This tool can identify the locations of a set of motif sequences in a set of target promoter sequences. ExactSearch also provides the functionality to search for a set of motif sequences in flanking regions from 50 plant genomes, which we have incorporated into the web tool. Chapter 5 presents STTM JBrowse; a web-based RNA-Seq data visualization system built using the JBrowse open source platform. STTM JBrowse is a unified repository to share/produce visualizations created from large RNA-Seq datasets generated from a variety of model and crop plants in which miRNAs were destroyed using Short Tandem Target Mimic (STTM) Technology

Transformations and analysis of parallel real time programs

The problem of schedulability analysis of a set of real time programs form a NP complete problem. The exponential complexity of analysis is a direct result of the complexity in the real time programs, as a combinatorial explosion takes place when trying to determine access patterns of shared resources. Thus, to transform the original programs to a less complex form, while preserving its timing characteristics, is the only viable solution. By using such transformations to reduce the complexity of real time programs, it is possible to schedulability analyze programs at compile time efficiently, without adding an unnecessary overhead to the compilation time. A set of suitable transformations and run time scheduling algorithms are introduced and implemented in C++. A library of transformations and analysis routines are provided. The library routines can be used to build prototype schedulability analyzers for testing various analysis techniques. These transformations and the scheduling algorithm will be an integral part of the real time compiler for the real time language RTL. The RTL compiler will not only produce fast and efficient code for an arbitrarily specified real time hardware architecture, but also will provide the worst case timing characteristics for the programs

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories

Best foot forward, watching your step, jumping in with both feet, or sticking your foot in it? - the politics of researching academic viewpoints

This article presents our experiences of conducting research interviews with Australian academics, in order to reflect on the politics of researcher and participant positionality. In particular, we are interested in the ways that academic networks, hierarchies and cultures, together with mobility in the higher education sector, contribute to a complex discursive terrain in which researchers and participants alike must maintain vigilance about where they 'put their feet' in research interviews. We consider the implications for higher education research, arguing that the positionality of researchers and participants pervades and exceeds these specialised research situations.15 page(s

Modeling and simulation of temperature variation in bearings In a hydro electric power generating unit

Hydroelectric power contributes around 20% to the world electricity supply and is considered as the most important, clean, emission free and an economical renewable energy source. Hydro electric power plants operating all over the world has been built in the 20th century in many countries and running at a higher plant-factor. This is achieved by minimizing the failures and operating the plants continuously for a longer period at a higher load. However, continuous operation of old plants have constrained with the failures due to bearing overheating. The aim of this research is to model and simulate the dynamic variation of temperatures of bearing temperature of a hydro electric generating unit. Multi-input, multi-output (MIMO) system with complex nonlinear characteristics of this nature is difficult to model using conventional modeling methods. Hence, in this research neural network (NN) technique has been used for modeling the system

Used product acquisition, sorting and disposition for circular supply chains: Literature review and research directions

The vision of a circular economy (CE) inspires firms, governments, and scholars alike. The transition is underway in both practice and the literature, but success depends on the effective implementation of circular supply chains (CSCs), which encompass acquiring used products, sorting them by type and quality, and deciding which to dispose to various processing options. We review 131 high-impact journal articles on returns acquisition, sorting, and disposition (ASD) over the decade 2012-2021 to assess the current status of ASD research for CSCs and to discuss important research directions for supporting the transition to a CE. Uniquely synthesising the state of the art on all these three overarching decision areas, we find aspects of CSCs prominent in the decade's research agenda, such as closed loop supply chain coordination and ASD for remanufacturing, and highlight growing coverage of behavioural considerations. Research applicability has been constrained by a lack of empirical studies, limited practical validation of mathematical models, a focus on economic objectives, and restrictive modelling assumptions about behaviour and uncertainty in returns. We recommend further research in each part of ASD to facilitate a CSC, and as a whole, for transitioning to a CE. CE concepts such as joint decision-making between product design and returns management, cross-sector collaboration, and product-service systems should inform the agenda for CSC research

A machine learning case–control classifier for schizophrenia based on DNA methylation in blood

Epigenetic dysregulation is thought to contribute to the etiology of schizophrenia (SZ), but the cell type-specificity of DNA methylation makes population-based epigenetic studies of SZ challenging. To train an SZ case–control classifier based on DNA methylation in blood, therefore, we focused on human genomic regions of systemic interindividual epigenetic variation (CoRSIVs), a subset of which are represented on the Illumina Human Methylation 450K (HM450) array. HM450 DNA methylation data on whole blood of 414 SZ cases and 433 non-psychiatric controls were used as training data for a classification algorithm with built-in feature selection, sparse partial least squares discriminate analysis (SPLS-DA); application of SPLS-DA to HM450 data has not been previously reported. Using the first two SPLS-DA dimensions we calculated a “risk distance” to identify individuals with the highest probability of SZ. The model was then evaluated on an independent HM450 data set on 353 SZ cases and 322 non-psychiatric controls. Our CoRSIV-based model classified 303 individuals as cases with a positive predictive value (PPV) of 80%, far surpassing the performance of a model based on polygenic risk score (PRS). Importantly, risk distance (based on CoRSIV methylation) was not associated with medication use, arguing against reverse causality. Risk distance and PRS were positively correlated (Pearson r = 0.28, P = 1.28 × 10−12), and mediational analysis suggested that genetic effects on SZ are partially mediated by altered methylation at CoRSIVs. Our results indicate two innate dimensions of SZ risk: one based on genetic, and the other on systemic epigenetic variants

Non-cysteine linked MUC1 cytoplasmic dimers are required for Src recruitment and ICAM-1 binding induced cell invasion

<p>Abstract</p> <p>Background</p> <p>The mucin MUC1, a type I transmembrane glycoprotein, is overexpressed in breast cancer and has been correlated with increased metastasis. We were the first to report binding between MUC1 and Intercellular adhesion molecule-1 (ICAM-1), which is expressed on stromal and endothelial cells throughout the migratory tract of a metastasizing breast cancer cell. Subsequently, we found that MUC1/ICAM-1 binding results in pro-migratory calcium oscillations, cytoskeletal reorganization, and simulated transendothelial migration. These events were found to involve Src kinase, a non-receptor tyrosine kinase also implicated in breast cancer initiation and progression. Here, we further investigated the mechanism of MUC1/ICAM-1 signalling, focusing on the role of MUC1 dimerization in Src recruitment and pro-metastatic signalling.</p> <p>Methods</p> <p>To assay MUC1 dimerization, we used a chemical crosslinker which allowed for the detection of dimers on SDS-PAGE. We then generated MUC1 constructs containing an engineered domain which allowed for manipulation of dimerization status through the addition of ligands to the engineered domain. Following manipulation of dimerization, we immunoprecipitated MUC1 to investigate recruitment of Src, or assayed for our previously observed ICAM-1 binding induced events. To investigate the nature of MUC1 dimers, we used both non-reducing SDS-PAGE and generated a mutant construct lacking cysteine residues.</p> <p>Results</p> <p>We first demonstrate that the previously observed MUC1/ICAM-1signalling events are dependent on the activity of Src kinase. We then report that MUC1 forms constitutive cytoplasmic domain dimers which are necessary for Src recruitment, ICAM-1 induced calcium oscillations and simulated transendothelial migration. The dimers are not covalently linked constitutively or following ICAM-1 binding. In contrast to previously published reports, we found that membrane proximal cysteine residues were not involved in dimerization or ICAM-1 induced signalling.</p> <p>Conclusions</p> <p>Our data implicates non-cysteine linked MUC1 dimerization in cell signalling pathways required for cancer cell migration.</p

Methyl-β-cyclodextrin restores the structure and function of pulmonary surfactant films impaired by cholesterol

AbstractPulmonary surfactant, a defined mixture of lipids and proteins, imparts very low surface tension to the lung–air interface by forming an incompressible film. In acute respiratory distress syndrome and other respiratory conditions, this function is impaired by a number of factors, among which is an increase of cholesterol in surfactant. The current study shows in vitro that cholesterol can be extracted from surfactant and function subsequently restored to dysfunctional surfactant films in a dose-dependent manner by methyl-β-cyclodextrin (MβCD). Bovine lipid extract surfactant was supplemented with cholesterol to serve as a model of dysfunctional surfactant. Likewise, when cholesterol in a complex with MβCD (“water-soluble cholesterol”) was added in aqueous solution, surfactant films were rendered dysfunctional. Atomic force microscopy showed recovery of function by MβCD is accompanied by the re-establishment of the native film structure of a lipid monolayer with scattered areas of lipid bilayer stacks, whereas dysfunctional films lacked bilayers. The current study expands upon a recent perspective of surfactant inactivation in disease and suggests a potential treatment