Relevant bibliographies by topics / Biology|Bioinformatics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Biology|Bioinformatics'

Author: Grafiati
Published: 9 July 2021
Last updated: 15 February 2022

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Journal articles on the topic "Biology|Bioinformatics"

1

Lederman, Lynne. "Bioinformatics and Systems Biology." BioTechniques 46, no. 7 (2009): 501–3. http://dx.doi.org/10.2144/000113177.

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Ram, Prahlad T., John Mendelsohn, and Gordon B. Mills. "Bioinformatics and systems biology." Molecular Oncology 6, no. 2 (2012): 147–54. http://dx.doi.org/10.1016/j.molonc.2012.01.008.

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Lakhno, V. D. "Mathematical biology and bioinformatics." Herald of the Russian Academy of Sciences 81, no. 5 (2011): 539–45. http://dx.doi.org/10.1134/s1019331611050029.

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Dalpech, Roger. "Bioinformatics and school biology." Journal of Biological Education 40, no. 4 (2006): 147–48. http://dx.doi.org/10.1080/00219266.2006.9656035.

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Zharikova, A. A., and A. A. Mironov. "piRNAs: Biology and bioinformatics." Molecular Biology 50, no. 1 (2016): 69–76. http://dx.doi.org/10.1134/s0026893316010222.

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Rajpal, Deepak K. "Understanding Biology Through Bioinformatics." International Journal of Toxicology 24, no. 3 (2005): 147–52. http://dx.doi.org/10.1080/10915810590948325.

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During the journey from the discovery of DNA to be the source of genetic information and elucidation of double-helical nature of DNA molecule to the assembly of human genome sequence and there after, bioinformatics has become an integral part of modern biology. Bioinformatics relies substantially on significant contributions made by scientists in various fields, including but not limited to, linguistics, biology, mathematics, computer science, and statistics. There is an ever increasing amount of data to elucidate toxic mechanisms and/or adverse effects of xenobiotics in the field of toxicogenomics. Annotation in combination with various bioinformatics analytical tools can play a crucial role in the understanding of genes and proteins, and can potentially help draw meaningful conclusions from various data sources. This article attempts to present a simple overview of bioinformatics, and an effort is made to discuss annotation.
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Yao, T. "Bioinformatics and Systems Biology - towards Integrative Biology." Yearbook of Medical Informatics 14, no. 01 (2005): 535–37. http://dx.doi.org/10.1055/s-0038-1638448.

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Medema, Marnix H., and Huimin Zhao. "Editorial: Synthetic biology and bioinformatics." Natural Product Reports 33, no. 8 (2016): 913–14. http://dx.doi.org/10.1039/c6np90031c.

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Bloom, Mark. "Biology insilico: The Bioinformatics Revolution." American Biology Teacher 63, no. 6 (2001): 400–407. http://dx.doi.org/10.1662/0002-7685(2001)063[0397:bistbr]2.0.co;2.

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Kunz, Meik, Ke Xiao, Chunguang Liang, et al. "Bioinformatics of cardiovascular miRNA biology." Journal of Molecular and Cellular Cardiology 89 (December 2015): 3–10. http://dx.doi.org/10.1016/j.yjmcc.2014.11.027.

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Dissertations / Theses on the topic "Biology|Bioinformatics"

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Pomyen, Yotsawat. "Exploring microRNA biology using integrative bioinformatics." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24774.

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Deregulation of energy metabolism is one of the emerging hallmarks of cancer required for proliferation and metastasis. MicroRNAs are small RNA molecules that have crucial roles in the regulation of biological processes in organisms, including metabolism. Due to recent discovery of miRNAs in humans, roles of miRNAs in metabolism of tumour cells, and effects these have on cancer patients, are still obscure and in need of expansion. Currently, experimental and computational data on the miRNAs are being analysed by a wide range of statistical methods; however, these methods in their original forms posses many limitations. Therefore, new ways of utilising these statistical methods are needed in order to unravel the roles of miRNAs in cancer metabolism. In this thesis, the roles of a specific miRNA, miR-22, and the three metabolic target genes were investigated through the use of classical statistical methods, revealed that miR-22, the metabolic target genes, and the interactions between them, were beneficial to survival outcome of breast cancer patients. Furthermore, novel combinations of the conventional statistical methods were invented in order to investigate the global miRNA regulations on metabolic target genes. These new procedures were demonstrated by using publicly available data sets. In one analysis, it was found that miRNAs could be divided into six clusters according to the metabolic target genes through a novel combination of statistical methods. A new statistical method was also invented to provide a generalised means to test for clustering based on sets of correlations.
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Malatras, Apostolos. "Bioinformatics tools for the systems biology of dysferlin deficiency." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066627/document.

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Le but de mon projet est de créer et d’appliquer des outils pour l’analyse de la biologie des systèmes musculaires en utilisant différentes données OMICS. Ce projet s’intéresse plus particulièrement à la dysferlinopathie due la déficience d’une protéine appelée dysferline qui est exprimée principalement dans les muscles squelettiques et cardiaque. La perte du dysferline due à la mutation (autosomique-récessive) du gène DYSF entraîne une dystrophie musculaire progressive (LGMD2B, MM, DMAT). Nous avons déjà développé des outils bio-informatiques qui peuvent être utilisés pour l’analyse fonctionnelle de données OMICS, relative à la dyspherlinopathie. Ces derniers incluent le test dit «gene set enrichment analysis», test comparant les profils OMICS d’intérêts aux données OMICS musculaires préalablement publiées ; et l’analyse des réseaux impliquant les diffèrent(e)s protéines et transcrits entre eux/elles. Ainsi, nous avons analysé des centaines de données omiques publiées provenant d’archives publiques. Les outils informatiques que nous avons développés sont CellWhere et MyoMiner. CellWhere est un outil facile à utiliser, permettant de visualiser sur un graphe interactif à la fois les interactions protéine-protéine et la localisation subcellulaire des protéines. Myominer est une base de données spécialisée dans le tissu et les cellules musculaires, et qui fournit une analyse de co-expression, aussi bien dans les tissus sains que pathologiques. Ces outils seront utilisés dans l'analyse et l'interprétation de données transcriptomiques pour les dyspherlinopathies mais également les autres pathologies neuromusculaires<br>The aim of this project was to build and apply tools for the analysis of muscle omics data, with a focus on Dysferlin deficiency. This protein is expressed mainly in skeletal and cardiac muscles, and its loss due to mutation (autosomal-recessive) of the DYSF gene, results in a progressive muscular dystrophy (Limb Girdle Muscular Dystrophy type 2B (LGMD2B), Miyoshi myopathy and distal myopathy with tibialis anterior onset (DMAT)). We have developed various tools and pipelines that can be applied towards a bioinformatics functional analysis of omics data in muscular dystrophies and neuromuscular disorders. These include: tests for enrichment of gene sets derived from previously published muscle microarray data and networking analysis of functional associations between altered transcripts/proteins. To accomplish this, we analyzed hundreds of published omics data from public repositories. The tools we developed are called CellWhere and MyoMiner. CellWhere is a user-friendly tool that combines protein-protein interactions and protein subcellular localizations on an interactive graphical display (https://cellwhere-myo.rhcloud.com). MyoMiner is a muscle cell- and tissue-specific database that provides co-expression analyses in both normal and pathological tissues. Many gene co-expression databases already exist and are used broadly by researchers, but MyoMiner is the first muscle-specific tool of its kind (https://myominer-myo.rhcloud.com). These tools will be used in the analysis and interpretation of transcriptomics data from dysferlinopathic muscle and other neuromuscular conditions and will be important to understand the molecular mechanisms underlying these pathologies
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Kasap, Server. "High performance reconfigurable architectures for bioinformatics and computational biology applications." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/24757.

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The field of Bioinformatics and Computational Biology (BCB), a relatively new discipline which spans the boundaries of Biology, Computer Science and Engineering, aims to develop systems that help organise, store, retrieve and analyse genomic and other biological information in a convenient and speedy way. This new discipline emerged mainly as a result of the Human Genome project which succeeded in transcribing the complete DNA sequence of the human genome, hence making it possible to address many problems which were impossible to even contemplate before, with a plethora of applications including disease diagnosis, drug engineering, bio-material engineering and genetic engineering of plants and animals; all with a real impact on the quality of the life of ordinary individuals. Due to the sheer immensity of the data sets involved in BCB algorithms (often measured in tens/hundreds of Gigabytes) as well as their computation demands (often measured in Tera-Ops), high performance supercomputers and computer clusters have been used as implementation platforms for high performance BCB computing. However, the high cost as well as the lack of suitable programming interfaces for these platforms still impedes a wider undertaking of this technology in the BCB community. Moreover, with increased heat dissipation, supercomputers are now often augmented with special-purpose hardware (or ASICs) in order to speed up their operations while reducing their power dissipation. However, since ASICs are fully customised to implement particular tasks/algorithms, they suffer from increased development times, higher Non-Recurring-Engineering (NRE) costs, and inflexibility as they cannot be reused to implement tasks/algorithms other than those they have been designed to perform. On the other hand, Field Programmable Gate Arrays (FPGAs) have recently been proposed as a viable alternative implementation platform for BCB applications due to their flexible computing and memory architecture which gives them ASIC-like performance with the added programmability feature. In order to counter the aforementioned limitations of both supercomputers and ASICs, this research proposes the use of state-of-the-art reprogrammable system-on-chip technology, in the form of platform FPGAs, as a relatively low cost, high performance and reprogrammable implementation platform for BCB applications. This research project aims to develop a sophisticated library of FPGA architectures for bio-sequence analysis, phylogenetic analysis, and molecular dynamics simulation.
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Cingolani, Pablo. "Bioinformatics for epigenomics." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40820.

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Epigenetics refers to reversible, heritable changes in gene regulation that occur without a change in DNA sequence. These changes are usually due to methylation of cytosine bases in DNA. In this work we review existing method- ologies and propose new ones for their use in epigenomics. High throughtput methods to estimate methylation levels were developed as well as methods to make a biological interpretation of the data based on gene sets enrichment. High correlation was obtained between our methylation estimations and ex- perimental data from MeDIP experiments. Our proposed methods for gene sets enrichment performed better than well-known methods.<br>L’ ́epigenetique d ́ecrit les changements re'versibles et he'ritables de la r ́egulation g ́enique qui arrivent sans changements dans la s ́equence d’ADN. Ces change- ments sont habituellement dus `a la m ́ethylation de cytosines dans l’ADN. Dans cette th`ese, nous r ́ecapitulons les m ́ethodes bioinformatiques existantes et nous proposons des nouvelles m ́ethodes pour des probl`emes reli ́es `a l’ ́epig ́en ́etique. Les m ́ethodes a haut d ́ebit pour l’estimation du niveau de m ́ethylation sont d ́evelopp ́ees, de mˆeme que des m ́ethodes pour l’interpr ́etation biologique des donn ́ees en se basant sur l’enrichissement d’ensemble de g`enes de la mˆeme fonction. De hauts niveaux de corr ́elation sont obtenus entre nos estim ́es et les donn ́ees exp ́erimentales provenant d’exp ́eriences de type MeDIP. Les m ́ethodes que nous proposons pour l’analyse d’enrichissement de fonction des g`enes performent mieux que les autres m ́ethodes existantes.
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Malatras, Apostolos [Verfasser]. "Bioinformatics tools for the systems biology of dysferlin deficiency / Apostolos Malatras." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1171431333/34.

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Ling, Cheng. "High performance bioinformatics and computational biology on general-purpose graphics processing units." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6260.

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Bioinformatics and Computational Biology (BCB) is a relatively new multidisciplinary field which brings together many aspects of the fields of biology, computer science, statistics, and engineering. Bioinformatics extracts useful information from biological data and makes these more intuitive and understandable by applying principles of information sciences, while computational biology harnesses computational approaches and technologies to answer biological questions conveniently. Recent years have seen an explosion of the size of biological data at a rate which outpaces the rate of increases in the computational power of mainstream computer technologies, namely general purpose processors (GPPs). The aim of this thesis is to explore the use of off-the-shelf Graphics Processing Unit (GPU) technology in the high performance and efficient implementation of BCB applications in order to meet the demands of biological data increases at affordable cost. The thesis presents detailed design and implementations of GPU solutions for a number of BCB algorithms in two widely used BCB applications, namely biological sequence alignment and phylogenetic analysis. Biological sequence alignment can be used to determine the potential information about a newly discovered biological sequence from other well-known sequences through similarity comparison. On the other hand, phylogenetic analysis is concerned with the investigation of the evolution and relationships among organisms, and has many uses in the fields of system biology and comparative genomics. In molecular-based phylogenetic analysis, the relationship between species is estimated by inferring the common history of their genes and then phylogenetic trees are constructed to illustrate evolutionary relationships among genes and organisms. However, both biological sequence alignment and phylogenetic analysis are computationally expensive applications as their computing and memory requirements grow polynomially or even worse with the size of sequence databases. The thesis firstly presents a multi-threaded parallel design of the Smith- Waterman (SW) algorithm alongside an implementation on NVIDIA GPUs. A novel technique is put forward to solve the restriction on the length of the query sequence in previous GPU-based implementations of the SW algorithm. Based on this implementation, the difference between two main task parallelization approaches (Inter-task and Intra-task parallelization) is presented. The resulting GPU implementation matches the speed of existing GPU implementations while providing more flexibility, i.e. flexible length of sequences in real world applications. It also outperforms an equivalent GPPbased implementation by 15x-20x. After this, the thesis presents the first reported multi-threaded design and GPU implementation of the Gapped BLAST with Two-Hit method algorithm, which is widely used for aligning biological sequences heuristically. This achieved up to 3x speed-up improvements compared to the most optimised GPP implementations. The thesis then presents a multi-threaded design and GPU implementation of a Neighbor-Joining (NJ)-based method for phylogenetic tree construction and multiple sequence alignment (MSA). This achieves 8x-20x speed up compared to an equivalent GPP implementation based on the widely used ClustalW software. The NJ method however only gives one possible tree which strongly depends on the evolutionary model used. A more advanced method uses maximum likelihood (ML) for scoring phylogenies with Markov Chain Monte Carlo (MCMC)-based Bayesian inference. The latter was the subject of another multi-threaded design and GPU implementation presented in this thesis, which achieved 4x-8x speed up compared to an equivalent GPP implementation based on the widely used MrBayes software. Finally, the thesis presents a general evaluation of the designs and implementations achieved in this work as a step towards the evaluation of GPU technology in BCB computing, in the context of other computer technologies including GPPs and Field Programmable Gate Arrays (FPGA) technology.
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Marani, Paola <1970&gt. "From "wet biology" to statistical analysis of structural features with bioinformatics tools." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/689/.

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Many new Escherichia coli outer membrane proteins have recently been identified by proteomics techniques. However, poorly expressed proteins and proteins expressed only under certain conditions may escape detection when wild-type cells are grown under standard conditions. Here, we have taken a complementary approach where candidate outer membrane proteins have been identified by bioinformatics prediction, cloned and overexpressed, and finally localized by cell fractionation experiments. Out of eight predicted outer membrane proteins, we have confirmed the outer membrane localization for five—YftM, YaiO, YfaZ, CsgF, and YliI—and also provide preliminary data indicating that a sixth—YfaL—may be an outer membrane autotransporter.
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Lee, Anna. "Bioinformatics approaches towards facilitating drug development." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96984.

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Drug development is currently a time-consuming, costly and challenging process. The process typically starts with the identification of a therapeutic target for a given disease. A therapeutic target is some biological molecule and the binding of compounds to target molecules is expected to cause a desired therapeutic effect. That is, target binding compounds have the potential to become drug candidates. However, there is a tendency for many drug candidates to fail during clinical trials, and consequently, very few candidates become approved new drugs. This trend suggests that the early stages of drug development should be improved to provide better drug candidates.The reasons for which a drug candidate may fail during clinical trials include unacceptable toxicity and insufficient efficacy observed in humans. These reasons suggest that the assessments of a compound during the early stages of drug development often inaccurately predict the effect of the compound in humans. One of the main goals of systems biology is to accurately predict how a given biological system responds to perturbations, e.g. treatment with a compound. This suggests that systems biology can help address challenges in drug development. However, there are currently gaps in our knowledge of systems. Here we use machine learning techniques to exploit existing systems data towards filling in these gaps. In particular, we developed a method that uses the occurrences of motifs in protein sequences to predict kinase-substrate interactions. We also developed a method that uses gene expression, protein-protein interaction and phenotype data to predict genetic interactions. These predicted interactions can facilitate the identification of potential therapeutic targets. Ultimately, a better selection of therapeutic targets should lead to better drug candidates.We also address the challenge of developing combinatorial therapies. Despite the fact that combinatorial therapies are advantageous, the scale of the experiments required to search for desirable chemical combinations is currently prohibitive. We therefore developed a method that uses system response data to predict chemical synergies towards facilitating the development of combinatorial therapies.Overall, this thesis shows how computational prediction in a systems biology framework can be used to facilitate and expedite the early stages of drug development.<br>Le développement des médicaments est actuellement un processus coûteux, difficile, et qui prend beaucoup de temps. Le processus commence généralement par l'identification d'une cible thérapeutique pour une maladie spécifique. Une cible thérapeutique est une molécule biologique et l'attachement des composés aux molécules cibles est supposé causer un effet thérapeutique. Donc, les composés qui attachent aux cibles ont le potentiel de devenir des candidats médicaments. Toutefois, beaucoup de candidats médicaments ont tendance à échouer pendant les essais cliniques, et par conséquence, très peu de candidats deviennent nouveaux médicaments approuvés. Cette tendance suggère que les premières étapes du développement de médicaments doit être amélioré afin de fournir des candidats médicaments de meilleure qualité.Les raisons pour lesquelles un candidat médicament peut échouer pendant les essais cliniques incluent une toxicité inacceptable et une éfficacité insuffisante observés chez les humains. Ces raisons suggèrent que les évaluations d'un composé pendant les premières étapes du développement de médicaments mal prédirent l'effet du composé chez les humains. Un des principaux objectifs de la biologie des systèmes est de prédire avec précision comment un système biologique répond à des perturbations, par exemple, un traitement avec un composé. Ceci suggère que la biologie des systèmes peut aider à aborder les défis du développement de médicaments. Toutefois, il existe actuellement des lacunes dans notre connaissance des systèmes. Ici, nous utilisons des techniques d'apprentissage automatique pour exploiter l'information existantes des systèmes pour combler ces lacunes. En particulier, nous avons développé une méthode qui utilise des occurrences des motifs dans les séquences de protéine pour prédire des interactions kinase-substrat. Nous avons aussi développé une méthode qui utilise d'expression des gènes, des interactions entre les protéines et d'information des phénotypes pour prédire des interactions génétiques. Ces interactions prédites peuvent faciliter l'identification des cibles thérapeutiques potentielles. En fin de compte, une meilleure sélection des cibles thérapeutiques devrait entraîner des candidats médicaments de meilleure qualité.Nous avons aussi abordé le défi de développer des thérapies combinatoires. Malgré le fait que les thérapies combinatoires sont avantageuses, l'ampleur des expériences nécessaires à la recherche de combinaisons chimiques souhaitables est actuellement prohibitif. Donc, nous avons développé une méthode qui utilise d'information de réponse des systèmes pour prédire des synergies chimiques en vue de faciliter le développement de thérapies combinatoires.Dans l'ensemble, cette thèse montre comment de calcul de prédiction dans une structure de biologie des systèmes peut être utilisés pour faciliter et accélérer les premières étapes du développement de médicaments.
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Roach, Kenneth L. (Kenneth Lee) 1979. "A microwell array cytometry system for high throughput single cell biology and bioinformatics." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47850.

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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.<br>Includes bibliographical references (p. 91-101).<br>Recent advances in systems biology and bioinformatics have highlighted that no cell population is truly uniform and that stochastic behavior is an inherent property of many biological systems. As a result, bulk measurements can be misleading even when particular care has been taken to isolate a single cell type, and measurements averaged over multiple cell populations in a tissue can be as misleading as the average height at an elementary school. Unfortunately, there are relatively few experimental systems available at present that can provide a combination of single cell resolution, large cell populations, and the ability to track individual cells over multiple time points. Those systems that do exist are often difficult to automate and require extensive user intervention simply to generate the raw data sets for later analysis. The goal of this thesis project was to develop a powerful, inexpensive, and easy-to-use system that meets the above requirements and can serve as a platform for single cell bioinformatics. Our current system design is composed of two basic parts: 1) a customizable PDMS device consisting of one or more microwell arrays, each with associated alignment and identification features, and 2) a suite of custom software tools for automated image processing and data analysis. The system has a number of significant advantages over competing technologies such as flow cytometry and standard image cytometry. Unlike flow cytometry, the cells are not in suspension, and individual cells can be tracked across multiple time points or examined before and after a treatment.<br>(cont.) Unlike most image cytometry approaches, the cells are arranged in a spatially defined pattern and physically separated from one another, greatly simplifying the required image analysis. The automated analysis tools require only a minimal amount of user intervention and can easily generate multi-channel fluorescence time courses for tens of thousands of individual cells in a single experiment. For visualization purposes, tools are provided to annotate the original fluorescence images or movies with the results of later analysis, and several quality control routines are available to identify improperly seeded wells or debris. The microwell array cytometry platform has allowed us to investigate a number of biological problems that would be difficult or impossible to tackle with standard techniques. Our earliest work focused on correlating pre-stress cell states with post-stress outcomes, with a major focus on the cryopreservation of primary hepatocytes. In particular, we wanted to know whether cell survival was dominated by extrinsic factors such as ice crystal nucleation, or intrinsic factors such as the energetic state of the cell. In one set of studies, we found that cells with a high initial mitochondrial content or mitochondrial membrane potential, as measured by Rh123 or JC-1 staining, were significantly less likely to survive the freezing process. This demonstrated that intrinsic cell factors do play a major role in cryopreservation survival, but perhaps more importantly demonstrated the power and versatility of the microwell system by tracking individual cells across a treatment as extreme as freezing the entire device. In another set of cryopreservation experiments, cells were transiently transfected with a GFP-tagged protective protein and the resulting cell population, with its range of expression levels, was used to generate dose response curves with single cell resolution for the protein's protective effect.<br>(cont.) More recently, our efforts have focused on generating single cell fluorescence time courses and using bioinformatics techniques such as hierarchical and k-means clustering to visualize the data and extract interesting features. More specifically, the behavior of primary hepatocytes under oxidative stress and protective metabolic manipulation was examined using a combination of mitochondrial and free radical sensitive dyes. The resulting time courses could not only be compared between the treatment groups, but a number of distinct response patterns could be identified within each treatment group. This variation in response patterns represent potentially important information that would be missed using bulk techniques or flow cytometry. In addition, membership in each response cluster was correlated between multiple dyes and with the initial state of each cell. Using a live / dead methodology, dose response curves, survival curves, and survival time distributions were also generated for each treatment condition and further subdivided based on the initial cell state and cluster assignments. We believe that our microwell array cytometry platform will have general utility for a wide range of questions related to cell population heterogeneity, biological stochasticity, and cell behavior under stress conditions. We have really just begun exploring rich data sets of this type, and with additional work there is a great potential for groundbreaking results in many areas of biology and bioinformatics. Though we have applied techniques from gene expression analysis, there are a number of significant differences between the type of data generated by gene chips and that generated in high-throughput single cell experiments. These differences also make single cell biology a fruitful area for the development of novel bioinformatics techniques and theories.<br>by Kenneth L. Roach.<br>Ph.D.
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Pepin, Francois. "Bioinformatics approaches to understanding the breast cancer microenvironment." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=92240.

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Breast cancer is a complex disease that requires the acquisition of several traits in order to proliferate and spread to nearby and distant tissues. However, many combinations are possible, making it harder to determine their significance. Genome-wide approaches such as gene expression profiling have provided an unbiased and global tool to investigate those traits, allowing investigators to both separate tumors into biologically meaningful categories and then to investigate their features in that context. A well-organized effort is required in order to collect and analyze the large number of samples necessary for such analyses. The Bioinformatics Integrated Application Software represents a way to facilitate both the organization of laboratory manipulation and automating subsequent analyses.<br>A large part of the complexity of breast cancer comes from the different types of cells that constitute the microenvironment and participate in diverse ways to tumor progression. Blood vessels play an important role in tumor progression, as additional vessels are necessary to support tumor growth. However, those new vessels are generally immature and often cannot efficiently provide nutrients to the tumor. This thesis shows that there exist two classes of tumor blood vessels that are associated with vessel maturity and differ in their expression of several antiangiogenic drug targets.<br>Numerous interactions occur between the various components of the tumor microenvironment. Using matched expression profiles of these cell types, it is possible to iden- tify specific processes that involve several cell types, such as Th1 and Th2 immune responses. This first step will open the door to a better mapping of the interactions and signals that occur in breast cancer.<br>Le cancer du sein est une maladie complexe qui requiert l'accumulation de plusieurs caractéristiques avant de pouvoir se multiplier et envahir les tissues rapprochés et éloignés. Plusieurs combinaisons sont par contre possibles, compliquant la tâche de d ́eterminer leurs importances. Les techniques d'analyse sur tout le génome comme l'expression génique sont des outils globaux et non biaisés pour étudier ces caractéristiques. Elle permettent de séparer les tumeurs en groupes biologiquement significatifs et d'étudier leurs caractéristiques dans ce contexte. Un effort concerté est nécessaire pour collecter et analyser la grande quantité de tumeurs requise. Le "Bioinformatics Integrated Application Software" est un système qui permet d'organiser les manipulations de laboratoire et d'automatiser les analyses ultérieures.<br>Une large proportion de la complexité du cancer du sein provient des diff ́erentes espèces de cellules faisant partie du microenvironnement et participant à la progression de la tumeur. Les vaisseaux sanguins jouent un rôle important dans la progression du cancer car des vaisseaux additionels sont nécessaires pour supporter la croissance tumorale. Ces vaisseaux sont par contre généralement immatures et ne peuvent souvent pas alimenter efficacement la tumeur. Cette thèse démontre qu'il existe deux catégories de vaisseaux sanguins tumoraux qui sont associées avec la maturité des vaisseaux et différent dans leur expression de gènes cibles de plusieurs médicaments antiangiogenèses.<br>De nombreuses interactions se produisent entre les différentes composantes du microenvironnement tumoral. L'utilisation de profils d'expressions concordants de différentes espèces cellulaires rend possible l'identification de procédés impliquant plusieurs espèces cellulaires, incluant des réactions immunitaires de types Th1 et Th2. Cette première étape va ouvrir la porte à une meilleure connaissance des échanges de signaux dans le cancer du sein.
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Books on the topic "Biology|Bioinformatics"

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Bioinformatics: Genome bioinformatics and computational biology. Nova Science, 2011.

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Bioinformatics and systems biology. Springer, 2008.

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Marcus, Frederick B. Bioinformatics and Systems Biology. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78353-4.

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Krawetz, Stephen, ed. Bioinformatics for Systems Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-440-7.

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Rajasekaran, Sanguthevar, ed. Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9.

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Chan, Jonathan H., Yew-Soon Ong, and Sung-Bae Cho, eds. Computational Systems-Biology and Bioinformatics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16750-8.

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Structural bioinformatics. 2nd ed. Wiley-Blackwell, 2009.

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Essential bioinformatics. Cambridge University Press, 2006.

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Alves, Ronnie, ed. Advances in Bioinformatics and Computational Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01722-4.

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de Souto, Marcilio C., and Maricel G. Kann, eds. Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31927-3.

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Book chapters on the topic "Biology|Bioinformatics"

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Singh, Desh Deepak. "Bioinformatics—Structural Biology Interface." In Bioinformatics: Applications in Life and Environmental Sciences. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-8880-3_4.

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Maulik, Ujjwal, Sanghamitra Bandyopadhyay, and Anirban Mukhopadhyay. "Computational Biology and Bioinformatics." In Multiobjective Genetic Algorithms for Clustering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16615-0_4.

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Marcus, Frederick B. "Bioinformatics." In Bioinformatics and Systems Biology. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78353-4_2.

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Hao, Bailin, Chunting Zhang, Yixue Li, et al. "Advanced Topics in Bioinformatics and Computational Biology." In Basics of Bioinformatics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38951-1_12.

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Prasad, T. V., and S. I. Ahson. "Data Mining for Bioinformatics— Systems Biology." In Bioinformatics: Applications in Life and Environmental Sciences. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-8880-3_9.

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Schomburg, Dietmar. "Biochemical Networks, Bioinformatics and Systems Biology." In Biochemical Pathways. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118657072.ch10.

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Marcus, Frederick B. "Systems Biology." In Bioinformatics and Systems Biology. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78353-4_3.

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Wishart, David S. "Bioinformatics for Metabolomics." In Bioinformatics for Systems Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-440-7_30.

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LeMeur, Nolweim, Michael Lawrence, Merav Bar, Muneesh Tewari, and Robert Gentleman. "R and Bioconductor Packages in Bioinformatics: Towards Systems Biology." In Statistical Bioinformatics. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470567647.ch13.

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Marcus, Frederick B. "Developmental Biology and Ageing." In Bioinformatics and Systems Biology. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78353-4_4.

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Conference papers on the topic "Biology|Bioinformatics"

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Jasinski, Joseph M. ""Computational Biology and Bioinformatics"." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259775.

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"Bioinformatics and computational biology, systems biology and modeling." In 2014 Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2014. http://dx.doi.org/10.1109/cibec.2014.7020933.

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Kolchanov, N. A. "Systems Computer Biology and Bioinformatics." In IX Congress of society physiologists of plants of Russia "Plant physiology is the basis for creating plants of the future". Kazan University Press, 2019. http://dx.doi.org/10.26907/978-5-00130-204-9-2019-17.

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Bush, William S. "Introduction to bioinformatics and computational biology." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330935.

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Conley, Catharine. "Using bioinformatics to understand biology in space." In Space 2000 Conference and Exposition. American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-5100.

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SEN, PRANAB K. "WHITHER BIOSTOCHASTICS IN COMPUTATIONAL BIOLOGY AND BIOINFORMATICS." In Proceedings of the 2008 Conference on FACM'08. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812835291_0002.

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Lakhno, V. D., M. N. Ustinin, and N. N. Nazipova. "The online journal "Mathematical biology and bioinformatics": ways and prospects." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.112.

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Qin, Hong. "Teaching computational thinking through bioinformatics to biology students." In the 40th ACM technical symposium. ACM Press, 2009. http://dx.doi.org/10.1145/1508865.1508932.

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Lee, Keeheon, Yong Hwan Kim, Seung Han Baek, and Min Song. "Analyzing Subject-Method Network of Bioinformatics and Biology." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. ACM, 2015. http://dx.doi.org/10.1145/2811163.2811181.

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"Session details: BIO - computational biology and bioinformatics track." In SAC 2017: Symposium on Applied Computing, edited by Paola Lecca, Dan Tulpan, and Juan Manuel Corchado Rodriguez. ACM, 2017. http://dx.doi.org/10.1145/3243941.

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Reports on the topic "Biology|Bioinformatics"

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Wallace, Susan S. DOE EPSCoR Initiative in Structural and computational Biology/Bioinformatics. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/924036.

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Chakraborty, Srijani. Promises and Challenges of Systems Biology. Nature Library, 2020. http://dx.doi.org/10.47496/nl.blog.09.

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Abstract:
Modern systems biology is essentially interdisciplinary, tying molecular biology, the omics, bioinformatics and non-biological disciplines like computer science, engineering, physics, and mathematics together.
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