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During their life cycle plants undergo a wide variety of morphological and developmental changes. Impinging these developmental processes there is a layer of gene, protein and metabolic networks that are responsible for the initiation of the correct developmental transitions at the right time of the year to ensure plant life success. New omic technologies are allowing the acquisition of massive amount of data to develop holistic and integrative analysis to understand complex processes. Among them, Microarray, Next-generation Sequencing (NGS) and Proteomics are providing enormous amount of data from different plant species and developmental stages, thus allowing the analysis of gene networks globally. Besides, the comparison of molecular networks from different species is providing information on their evolutionary history, shedding light on the origin of many key genes/proteins. Moreover, developmental processes are not only genetically programed but are also affected by internal and external signals. Metabolism, light, hormone action, temperature, biotic and abiotic stresses, etc. have a deep effect on developmental programs. The interface and interplay between these internal and external circuits with developmental programs can be unraveled through the integration of systematic experimentation with the computational analysis of the generated omics data (Molecular Systems Biology). This Research Topic intends to deepen in the different plant developmental pathways and how the corresponding gene networks evolved from a Molecular Systems Biology perspective. Global approaches for photoperiod, circadian clock and hormone regulated processes; pattern formation, phase-transitions, organ development, etc. will provide new insights on how plant complexity was built during evolution. Understanding the interface and interplay between different regulatory networks will also provide fundamental information on plant biology and focus on those traits that may be important for next-generation agriculture.

Plant Development --- Omics --- Molecular Systems Biology --- Evolution --- Gene Regulatory Networks

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During their life cycle plants undergo a wide variety of morphological and developmental changes. Impinging these developmental processes there is a layer of gene, protein and metabolic networks that are responsible for the initiation of the correct developmental transitions at the right time of the year to ensure plant life success. New omic technologies are allowing the acquisition of massive amount of data to develop holistic and integrative analysis to understand complex processes. Among them, Microarray, Next-generation Sequencing (NGS) and Proteomics are providing enormous amount of data from different plant species and developmental stages, thus allowing the analysis of gene networks globally. Besides, the comparison of molecular networks from different species is providing information on their evolutionary history, shedding light on the origin of many key genes/proteins. Moreover, developmental processes are not only genetically programed but are also affected by internal and external signals. Metabolism, light, hormone action, temperature, biotic and abiotic stresses, etc. have a deep effect on developmental programs. The interface and interplay between these internal and external circuits with developmental programs can be unraveled through the integration of systematic experimentation with the computational analysis of the generated omics data (Molecular Systems Biology). This Research Topic intends to deepen in the different plant developmental pathways and how the corresponding gene networks evolved from a Molecular Systems Biology perspective. Global approaches for photoperiod, circadian clock and hormone regulated processes; pattern formation, phase-transitions, organ development, etc. will provide new insights on how plant complexity was built during evolution. Understanding the interface and interplay between different regulatory networks will also provide fundamental information on plant biology and focus on those traits that may be important for next-generation agriculture.

Plant Development --- Omics --- Molecular Systems Biology --- Evolution --- Gene Regulatory Networks

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Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.

Pure sciences. Natural sciences (general) --- Cellular signal transduction. --- Genetic regulation. --- Genomics --- Signal Transduction --- Gene Expression. --- Gene Regulatory Networks. --- Models, Genetic. --- Models, Statistical. --- Mathematical models. --- genetics. --- Gene regulatory networks.

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Written for students and researchers, the second edition of this best-selling textbook continues to offer a clear presentation of design principles that govern the structure and behavior of biological systems. It highlights simple, recurring circuit elements that make up the regulation of cells and tissues. Rigorously classroom-tested, this edition includes new chapters on exciting advances made in the last decade. Features: Includes seven new chapters; The new edition has 189 exercises, the previous edition had 66; offers new examples relevant to human physiology and disease. --

Systems biology. --- Computational biology. --- Biological systems --- Imagerie en biologie. --- Biologie --- Systèmes biologiques --- Mathematical models. --- Informatique. --- Modèles mathématiques. --- Gene regulatory networks. --- Gene Regulatory Networks --- Systems Biology --- Computational Biology --- Biologie systémique. --- Bio-informatique. --- Réseaux de régulation génique. --- Science. --- Gene regulatory networks --- Computational biology --- Systems biology --- methods --- Mathematical models

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The complexity of living organisms surpasses our unaided habilities of analysis. Hence, computational and mathematical methods are necessary for increasing our understanding of biological systems. At the same time, there has been a phenomenal recent progress allowing the application of novel formal methods to new domains. This progress has spurred a conspicuous optimism in computational biology. This optimism, in turn, has promoted a rapid increase in collaboration between specialists of biology with specialists of computer science. Through sheer complexity, however, many important biological problems are at present intractable, and it is not clear whether we will ever be able to solve such problems. We are in the process of learning what kind of model and what kind of analysis and synthesis techniques to use for a particular problem. Some existing formalisms have been readily used in biological problems, others have been adapted to biological needs, and still others have been especially developed for biological systems. This Research Topic has examples of cases (1) employing existing methods, (2) adapting methods to biology, and (3) developing new methods. We can also see discrete and Boolean models, and the use of both simulators and model checkers. Synthesis is exemplified by manual and by machine-learning methods. We hope that the articles collected in this Research Topic will stimulate new research.

model checking --- Logic programing --- Answer set programing --- attractors of Boolean networks --- synthesis of biochemical models --- Gene Regulatory Networks --- Boolean networks --- biochemical networks