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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.

sterile caste --- reproductive caste --- gene networks --- Isoptera --- phenotypic plasticity --- Polyethism --- Hymenoptera --- sex determination --- Eusocial --- parental effects

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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.

sterile caste --- reproductive caste --- gene networks --- Isoptera --- phenotypic plasticity --- Polyethism --- Hymenoptera --- sex determination --- Eusocial --- parental effects

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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.

Medical screening --- Drug development. --- Pharmacology. --- Phenotype. --- label-free technologies --- gene networks --- pathways --- Phenotypic screenning --- Drug Discovery --- Systems Biology --- target-based assays --- panomic data --- Drug effects --- Medical pharmacology --- Medical sciences --- Chemicals --- Chemotherapy --- Drugs --- Pharmacy --- Development of drugs --- New drug development --- Pharmacology --- Mass medical screening --- Mass screening, Medical --- Medical examinations --- Screening, Medical --- Diagnostic services --- Health risk assessment --- Physiological effect --- Development

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This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.

Mathematics. --- Systems Theory, Control. --- Mathematical and Computational Biology. --- Gene Expression. --- Control, Robotics, Mechatronics. --- Gene expression. --- Systems theory. --- Mathématiques --- Expression génique --- Gene regulatory networks -- Mathematical models. --- Gene regulatory networks. --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Gene regulatory networks --- Mathematical models. --- System theory. --- Biomathematics. --- Control engineering. --- Robotics. --- Mechatronics. --- Circuits, Gene --- Gene circuits --- Gene modules --- Gene networks --- Genetic regulatory networks --- GRNs (Gene regulatory networks) --- Modules, Gene --- Networks, Gene regulatory --- Networks, Transcriptional --- Regulatory networks, Gene --- Transcriptional networks --- Genetic regulation --- Nucleotide sequence --- Genes --- Expression --- Mechanical engineering --- Microelectronics --- Microelectromechanical systems --- Automation --- Machine theory --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Programmable controllers --- Biology --- Mathematics --- Systems, Theory of --- Systems science --- Science --- Philosophy

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This book contributes to better understand how lifestyle modulations can effectively halt the emergence and progression of human diseases. The book will allow the reader to gain a better understanding of the mechanisms by which the environment interferes with the bio-molecular regulatory processes underlying the emergence and progression of complex diseases, such as cancer. Focusing on key and early cellular bio-molecular events giving rise to the emergence of degenerative chronic disease, it builds on previous experience on the development of multi-cellular organisms, to propose a mathematical and computer based framework that allows the reader to analyze the complex interplay between bio-molecular processes and the (micro)-environment from an integrative, mechanistic, quantitative and dynamical perspective. Taking the wealth of empirical evidence that exists it will show how to build and analyze models of core regulatory networks involved in the emergence and progression of chronic degenerative diseases, using a bottom-up approach.

Biological models. --- Systems biology. --- Gene regulatory networks --- Physiology. --- Circuits, Gene --- Gene circuits --- Gene modules --- Gene networks --- Genetic regulatory networks --- GRNs (Gene regulatory networks) --- Modules, Gene --- Networks, Gene regulatory --- Networks, Transcriptional --- Regulatory networks, Gene --- Transcriptional networks --- Genetic regulation --- Nucleotide sequence --- Computational biology --- Bioinformatics --- Biological systems --- Molecular biology --- Models, Biological --- Microbial genetics. --- Microbial genomics. --- Bioinformatics. --- Systems Biology. --- Microbial Genetics and Genomics. --- Mathematical Modeling and Industrial Mathematics. --- Computational Biology/Bioinformatics. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Systems biology --- Genomics --- Microbial genetics --- Microorganisms --- Genetics --- Microbiology --- Data processing --- Mathematical models. --- Models, Mathematical --- Simulation methods