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Advances in Genomics and Epigenomics of Social Insects

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889450800 Year: Pages: 155 DOI: 10.3389/978-2-88945-080-0 Language: English
Publisher: Frontiers Media SA
Subject: Genetics --- Science (General)
Added to DOAB on : 2017-07-06 13:27:36
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Social insects are among the most successful and ecologically important animals on earth. The lifestyle of these insects has fascinated humans since prehistoric times. These species evolved a caste of workers that in most cases have no progeny. Some social insects have worker sub-castes that are morphologically specialized for discrete tasks. The organization of the social insect colony has been compared to the metazoan body. Males in the order Hymenoptera (bees, ants and wasps) are haploid, a situation which results in higher relatedness between female siblings. Sociality evolved many times within the Hymenoptera, perhaps spurred in part by increased relatedness that increases inclusive fitness benefits to workers cooperating to raise their sisters and brothers rather than reproducing themselves. But epigenetic processes may also have contributed to the evolution of sociality. The Hymenoptera provide opportunities for comparative study of species ranging from solitary to highly social. A more ancient clade of social insects, the termites (infraorder Isoptera) provide an opportunity to study alternative mechanisms of caste determination and lifestyles that are aided by an array of endosymbionts. This research topic explores the use of genome sequence data and genomic techniques to help us explore how sociality evolved in insects, how epigenetic processes enable phenotypic plasticity, and the mechanisms behind whether a female will become a queen or a worker.

Phenotypic screening in the 21st century

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889194698 Year: Pages: 67 DOI: 10.3389/978-2-88919-469-8 Language: English
Publisher: Frontiers Media SA
Subject: Therapeutics --- Science (General)
Added to DOAB on : 2016-03-10 08:14:32
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In the genomic era of 1990s-2000s, pharmaceutical research moved to target-based drug discovery which enabled development of a number of small molecule drugs against a wide range of diseases. In many cases however, drugs that arose from genomics failed, questioning the validity of the targets and the suitability of target-based drug discovery as an optimal strategy for all disease states. For monogenic diseases, target-based approaches may be well-suited to the identification of novel therapies. Most diseases, however, are caused by a combination of several genetic and environmental factors and are likely to require simultaneous modulation of multiple molecular targets/pathways for successful treatment. For such diseases, reductionist approaches focusing on individual targets rather than biological networks are unlikely to succeed and new drug development strategies are required. In search of more successful approaches, the pharmaceutical industry is moving towards phenotypic screening beyond individual genes/targets. However, this requires rethinking of diseases and drug discovery approaches from a network and systems biology perspective. Since returning to the pre-genomics era of screening drug candidates in laborious animal models is not a feasible solution, the industry needs to evolve a new paradigm of phenotypic drug discovery within the context of systems biology. Such a paradigm must combine physiologically and disease relevant biological substrates with sufficient throughput, operational simplicity and statistical vigour. Biomarker strategies for translational medicine, as well as preclinical safety and selectivity assessments, would also need to be revised to adapt to the target agnostic style. This focused issue aims to discuss strategies, key concepts and technologies related to systems-based approaches in drug development. Design and implementation of innovative biological assays, featuring multiple target strategies, and rational drug design in the absence of target knowledge during the early drug discovery are illustrated with examples. Specific topics include: • The need for systems-based approaches in drug development • Phenotypic screening strategies • Compound libraries (natural product inspired compound collections) • Target deconvolution and identification • Target agnostic lead discovery and optimization • Multi-target approaches and decoding the phenotype (understanding biological interactions and multiscale systems modelling) • Translational aspects • Early evaluation of selectivity and safety in a target agnostic manner

Molecular Computing and Bioinformatics

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ISBN: 9783039211951 / 9783039211968 Year: Pages: 390 DOI: 10.3390/books978-3-03921-196-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-08-28 11:21:27
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This text will provide the most recent knowledge and advances in the area of molecular computing and bioinformatics. Molecular computing and bioinformatics have a close relationship, paying attention to the same object but working towards different orientations. The articles will range from topics such as DNA computing and membrane computing to specific biomedical applications, including drug R&D and disease analysis.

Keywords

prostate cancer --- Mycoplasma hominis --- endoplasmic reticulum --- systems biology --- protein targeting --- biomedical text mining --- big data --- Tianhe-2 --- parallel computing --- load balancing --- bacterial computing --- bacteria and plasmid system --- Turing universality --- recursively enumerable function --- miRNA biogenesis --- structural patterns --- DCL1 --- protein–protein interaction (PPI) --- clustering --- protein complex --- penalized matrix decomposition --- avian influenza virus --- interspecies transmission --- amino acid mutation --- machine learning --- Bayesian causal model --- causal direction learning --- K2 --- brain storm optimization --- line graph --- Cartesian product graph --- join graph --- atom-bond connectivity index --- geometric arithmetic index --- P-glycoprotein --- efflux ratio --- in silico --- machine learning --- hierarchical support vector regression --- absorption --- distribution --- metabolism --- excretion --- toxicity --- image encryption --- chaotic map --- DNA coding --- Hamming distance --- Stenotrophomonas maltophilia --- iron acquisition systems --- iron-depleted --- RAST server --- NanoString Technologies --- siderophores --- gene fusion data --- gene susceptibility prioritization --- evaluating driver partner --- gene networks --- drug-target interaction prediction --- machine learning --- drug discovery --- microRNA --- environmental factor --- structure information --- similarity network --- bioinformatics --- identification of Chinese herbal medicines --- biochip technology --- DNA barcoding technology --- DNA strand displacement --- cascade --- 8-bit adder/subtractor --- domain label --- Alzheimer’s disease --- gene coding protein --- sequence information --- support vector machine --- classification --- adverse drug reaction prediction --- heterogeneous information network embedding --- stacking denoising auto-encoder --- meta-path-based proximity --- Panax ginseng --- oligopeptide transporter --- flowering plant --- phylogeny --- transcription factor --- multiple interaction networks --- function prediction --- multinetwork integration --- low-dimensional representation --- dihydrouridine --- nucleotide physicochemical property --- pseudo dinucleotide composition --- RNA secondary structure --- ensemble classifier --- diabetes mellitus --- hypoxia-inducible factor-1? --- angiogenesis --- bone formation --- osteogenesis --- protein transduction domain --- membrane computing --- edge detection --- enzymatic numerical P system --- resolution free --- molecular computing --- molecular learning --- DNA computing --- self-organizing systems --- pattern classification --- machine learning --- laccase --- Brassica napus --- lignification --- stress --- molecular computing --- bioinformatics --- machine learning --- protein --- DNA --- RNA --- drug --- bio-inspired

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