Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Gene Regulatory Network Inference Using Machine Learning Techniques

Systems Biology is a field that models complex biological systems in order to better understand the working of cells and organisms. One of the systems modeled is the gene regulatory network that plays the critical role of controlling an organism's response to changes in its environment. Ideally, we would like a model of the complete gene regulatory network. In recent years, several advances in technology have permitted the collection of an unprecedented amount and variety of data such as genomes, gene expression data, time-series data, and perturbation data. This has stimulated research into computational methods that reconstruct, or infer, models of the gene regulatory network from the data. Many solutions have been proposed, yet there remain open challenges in utilising the range of available data as it is inherently noisy, and must be integrated by the inference techniques. The thesis seeks to contribute to this discourse by investigating challenges of performance, scale, and data integration. We propose a new algorithm BENIN that views network inference as feature selection to address issues of scale, that uses elastic net regression for improved performance, and adapts elastic net to integrate different types of biological data. The BENIN algorithm is benchmarked on a synthetic dataset from the DREAM4 challenge, and on real expression data for the human HeLa cell cycle. On the DREAM4 dataset BENIN out-performed all DREAM4 competitors on the size 100 subchallenge, and is also competitive with more recent state-of-the-art methods. Moreover, on the HeLa cell cycle data, BENIN could infer known regulatory interactions and propose new interactions that warrant further experimental investigation. Keys words: gene regulatory network, network inference, feature selection, elastic net regression

Molecular Targets of CNS Tumors

Molecular Targets of CNS Tumors is a selected review of Central Nervous System (CNS) tumors with particular emphasis on signaling pathway of the most common CNS tumor types. To develop drugs which specifically attack the cancer cells requires an understanding of the distinct characteristics of those cells. Additional detailed information is provided on selected signal pathways in CNS tumors

Colorectal Cancer

Colorectal cancer is one of the most common malignancies worldwide, and the pathogenesis of this neoplasm is probably one of the most studied. The knowledge obtained over time has led to the development of screening and early diagnosis systems, allowing a significant reduction in the incidence of this neoplasm. However, what is currently known probably represents only the tip of the iceberg of the biology of this tumor. It was recently shown that the gut microbiota may contribute to colorectal cancerogenesis. In addition, several novel targeted therapies are now applied to patients with colorectal carcinoma. Nonetheless, several questions are still unanswered. Could the modulation of the gut microbiota modify the risk of tumor progression or the efficacy of therapies? Are there any predictive biomarkers of the risk of tumor progression or the efficacy of target therapies? Is metastatic colorectal cancer one or more diseases? This book collects a series of scientific articles reflecting part of the state of the art regarding colorectal cancer, seeking to answer these questions

Role of adipose tissue in the pathogenesis and treatment of metabolic syndrome

© Springer International Publishing Switzerland 2014. Adipocytes are highly specialized cells that play a major role in energy homeostasis in vertebrate organisms. Excess adipocyte size or number is a hallmark of obesity, which is currently a global epidemic. Obesity is not only the primary disease of fat cells, but also a major risk factor for the development of Type 2 diabetes, cardiovascular disease, hypertension, and metabolic syndrome (MetS). Today, adipocytes and adipose tissue are no longer considered passive participants in metabolic pathways. In addition to storing lipid, adipocytes are highly insulin sensitive cells that have important endocrine functions. Altering any one of these functions of fat cells can result in a metabolic disease state and dysregulation of adipose tissue can profoundly contribute to MetS. For example, adiponectin is a fat specific hormone that has cardio-protective and anti-diabetic properties. Inhibition of adiponectin expression and secretion are associated with several risk factors for MetS. For this purpose, and several other reasons documented in this chapter, we propose that adipose tissue should be considered as a viable target for a variety of treatment approaches to combat MetS

Applications, challenges and new perspectives on the analysis of transcriptional regulation using epigenomic and transcriptomic data

The integrative analysis of epigenomics and transcriptomics data is an active research field in Bioinformatics. New methods are required to interpret and process large omics data sets, as generated within consortia such as the International Human Epigenomics Consortium. In this thesis, we present several approaches illustrating how combined epigenomics and transcriptomics datasets, e.g. for differential or time series analysis, can be used to derive new biological insights on transcriptional regulation. In this work we focus on regulatory proteins called transcription factors (TFs), which are essential for orchestrating cellular processes. In our novel approaches, we combine epigenomics data, such as DNaseI-seq, predicted TF binding scores and gene-expression measurements in interpretable machine learning models. In joint work with our collaborators within and outside IHEC, we have shown that our methods lead to biological meaningful results, which could be validated with wet-lab experiments. Aside from providing the community with new tools to perform integrative analysis of epigenomics and transcriptomics data, we have studied the characteristics of chromatin accessibility data and its relation to gene-expression in detail to better understand the implications of both computational processing and of different experimental methods on data interpretation. Overall, we provide easy to use tools to enable researchers to benefit from the era of Biological Data Science.In dieser Dissertation stellen wir mehrere Ansätze vor, um die häufigsten "omics" Daten, wie beispielsweise differentielle Datenstze oder auch Zeitreihen zu verwenden, um neue Erkenntnisse über Genregulation auf transkriptioneller Ebene gewinnen zu können. Dabei haben wir uns insbesondere auf sogenannte Transkriptionsfaktoren konzentriert. Dies sind Proteine, die essentiell für die Steuerung regulatorischer Prozesse in der Zelle sind. In unseren neuen Methoden kombinieren wir epigenetische Daten, zum Beispiel DNaseI-seq oder ATAC-seq Daten, vorhergesagte Transkriptionsfaktorbindestellen und Genexpressionsdaten in interpretierbaren Modellen des maschinellen Lernens. Zusammen mit unseren Kooperationspartnern haben wir gezeigt, dass unsere Methoden zu biologisch bedeutsamen Ergebnissen führen, die exemplarisch im Labor validiert werden konnten. Ferner haben wir im Detail Zusammenhänge zwischen der Struktur des Chromatins und der Genexpression untersucht. Dies ist von immenser Bedeutung, um den Einfluss von experimentellen Charakteristika aber auch von der Modellierung der Daten auf die biologische Interpretation zu vermeiden

Advances in Lipidomics: Biomedicine, Nutrients and Methodology

This book contains 12 articles covering biomedicine, nutrition, and the methodology of lipidomics . These works were first published by MDPI in a Special Issue of Metabolites. Phospholipids, sphingolipids, glyosylinositolphosphoceramides, cholesteryl esters, acyl-carnitines, and oxylipins are within the lipid classes accounted for studies regarding liver disease, Wilson disease, kidney disease, cardiovascular disease, adipogenesis, and the role lipids play in cancer and virus infection. High-throughput lipid extraction and guidelines for lipid annotation are addressed in several papers. This book is expected to provide a comprehensive view of the diverse areas where lipidomics looms largest

Complexity in Developmental Systems: Toward an Integrated Understanding of Organ Formation

During animal development, embryonic cells assemble into intricately structured organs by working together in organized groups capable of implementing tightly coordinated collective behaviors, including patterning, morphogenesis and migration. Although many of the molecular components and basic mechanisms underlying such collective phenomena are known, the complexity emerging from their interplay still represents a major challenge for developmental biology. Here, we first clarify the nature of this challenge and outline three key strategies for addressing it: precision perturbation, synthetic developmental biology, and data-driven inference. We then present the results of our effort to develop a set of tools rooted in two of these strategies and to apply them to uncover new mechanisms and principles underlying the coordination of collective cell behaviors during organogenesis, using the zebrafish posterior lateral line primordium as a model system. To enable precision perturbation of migration and morphogenesis, we sought to adapt optogenetic tools to control chemokine and actin signaling. This endeavor proved far from trivial and we were ultimately unable to derive functional optogenetic constructs. However, our work toward this goal led to a useful new way of perturbing cortical contractility, which in turn revealed a potential role for cell surface tension in lateral line organogenesis. Independently, we hypothesized that the lateral line primordium might employ plithotaxis to coordinate organ formation with collective migration. We tested this hypothesis using a novel optical tool that allows targeted arrest of cell migration, finding that contrary to previous assumptions plithotaxis does not substantially contribute to primordium guidance. Finally, we developed a computational framework for automated single-cell segmentation, latent feature extraction and quantitative analysis of cellular architecture. We identified the key factors defining shape heterogeneity across primordium cells and went on to use this shape space as a reference for mapping the results of multiple experiments into a quantitative atlas of primordium cell architecture. We also propose a number of data-driven approaches to help bridge the gap from big data to mechanistic models. Overall, this study presents several conceptual and methodological advances toward an integrated understanding of complex multi-cellular systems

Targeting innate immunity in acute kidney injury

Acute kidney injury (AKI) is an umbrella term for various aetiological insults, disrupting the kidneys’ capacity to carry out many of its essential physiological function. We focused on the ischemia reperfusion injury (IRI) model of AKI, which is applicable across native and transplant kidney AKI. The current standard of care for patients with acute kidney injury (AKI) is limited to optimising supportive care and renal replacement therapy. Unfortunately, there are no disease modifying interventions available in clinical practice, which have significant implications to short- and long-term outcomes following AKI, including chronic kidney disease, cardiovascular and mortality risks. AKI in the immediate days following kidney transplantation (including delayed graft function, DGF) also portends poorer outcomes, with increased risk of acute rejection and worse overall graft- and patient-survival metrics. This PhD aims to determine if modulation of the innate immune response can be harnessed to limit the acute injury and maladaptive immune response which accompanies acute kidney injury. Chapter 1 presents an overview of the clinical and research landscape of acute kidney injury (AKI) and delayed graft function (DGF), includes general overview of clinical trials for AKI/DGF and immunological mechanisms in an ischemia reperfusion injury (IRI) model. Chapter 2 demonstrates the protective effects dendritic cells therapy to limit the degree of renal injury, cell death and inflammation. Chapter 3 shows the importance of pyroptosis in AKI. Impaired Gasdermin D (GSDMD) protein function was shown to be protective against severe AKI. Chapter 4 explores the Australian Chronic Allograft Dysfunction (AUSCAD) study cohort to determine if a molecular/transcriptomic profile can be matched with clinical and biopsy data to determine the patients most likely to benefit from early, effective intervention for AKI/DGF