November 23, 2020

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Computational Systems Biology

Computational Systems Biology
Author : Andres Kriete,Roland Eils
Publisher : Academic Press
Release Date : 2013-11-26
Category : Computers
Total pages :548
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This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. Logical information flow aids understanding of basic building blocks of life through disease phenotypes Evolved principles gives insight into underlying organizational principles of biological organizations, and systems processes, governing functions such as adaptation or response patterns Coverage of technical tools and systems helps researchers to understand and resolve specific systems biology problems using advanced computation Multi-scale modeling on disparate scales aids researchers understanding of dependencies and constraints of spatio-temporal relationships fundamental to biological organization and function.

Elements of Computational Systems Biology

Elements of Computational Systems Biology
Author : Huma M. Lodhi,Stephen H. Muggleton
Publisher : John Wiley & Sons
Release Date : 2010-03-25
Category : Computers
Total pages :400
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Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology. Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.

Computational Systems Biology of Cancer

Computational Systems Biology of Cancer
Author : Emmanuel Barillot,Laurence Calzone,Philippe Hupe,Jean-Philippe Vert,Andrei Zinovyev
Publisher : CRC Press
Release Date : 2012-08-25
Category : Science
Total pages :461
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The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models—integrating our knowledge of tumour progression mechanisms with the tsunami of information brought by high-throughput technologies such as microarrays and next-generation sequencing. Offering promising insights on how to defeat cancer, the emerging field of systems biology captures the complexity of biological phenomena using mathematical and computational tools. Novel Approaches to Fighting Cancer Drawn from the authors’ decade-long work in the cancer computational systems biology laboratory at Institut Curie (Paris, France), Computational Systems Biology of Cancer explains how to apply computational systems biology approaches to cancer research. The authors provide proven techniques and tools for cancer bioinformatics and systems biology research. Effectively Use Algorithmic Methods and Bioinformatics Tools in Real Biological Applications Suitable for readers in both the computational and life sciences, this self-contained guide assumes very limited background in biology, mathematics, and computer science. It explores how computational systems biology can help fight cancer in three essential aspects: Categorising tumours Finding new targets Designing improved and tailored therapeutic strategies Each chapter introduces a problem, presents applicable concepts and state-of-the-art methods, describes existing tools, illustrates applications using real cases, lists publically available data and software, and includes references to further reading. Some chapters also contain exercises. Figures from the text and scripts/data for reproducing a breast cancer data analysis are available at www.cancer-systems-biology.net.

Computational Systems Biology Approaches in Cancer Research

Computational Systems Biology Approaches in Cancer Research
Author : Inna Kuperstein,Emmanuel Barillot
Publisher : CRC Press
Release Date : 2019-09-09
Category : Computers
Total pages :167
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Praise for Computational Systems BiologyApproaches in Cancer Research: "Complex concepts are written clearly and with informative illustrations and useful links. The book is enjoyable to read yet provides sufficient depth to serve as a valuable resource for both students and faculty." — Trey Ideker, Professor of Medicine, UC Xan Diego, School of Medicine "This volume is attractive because it addresses important and timely topics for research and teaching on computational methods in cancer research. It covers a broad variety of approaches, exposes recent innovations in computational methods, and provides acces to source code and to dedicated interactive web sites." — Yves Moreau, Department of Electrical Engineering, SysBioSys Centre for Computational Systems Biology, University of Leuven With the availability of massive amounts of data in biology, the need for advanced computational tools and techniques is becoming increasingly important and key in understanding biology in disease and healthy states. This book focuses on computational systems biology approaches, with a particular lens on tackling one of the most challenging diseases - cancer. The book provides an important reference and teaching material in the field of computational biology in general and cancer systems biology in particular. The book presents a list of modern approaches in systems biology with application to cancer research and beyond. It is structured in a didactic form such that the idea of each approach can easily be grasped from the short text and self-explanatory figures. The coverage of topics is diverse: from pathway resources, through methods for data analysis and single data analysis to drug response predictors, classifiers and image analysis using machine learning and artificial intelligence approaches. Features Up to date using a wide range of approaches Applicationexample in each chapter Online resources with useful applications’

Computational Systems Biology

Computational Systems Biology
Author : Paola Lecca,Angela Re,Adaoha Elizabeth Ihekwaba,Ivan Mura,Thanh-Phuong Nguyen
Publisher : Woodhead Publishing
Release Date : 2016-07-29
Category : Science
Total pages :180
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Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems. This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling. The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists. Provides a unified presentation of network inference, analysis, and modeling Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systems Includes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications

Computational Systems Biology

Computational Systems Biology
Author : Andres Kriete,Roland Eils
Publisher : Elsevier
Release Date : 2005-11-10
Category : Computers
Total pages :424
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Systems Biology is concerned with the quantitative study of complex biosystems at the molecular, cellular, tissue, and systems scales. Its focus is on the function of the system as a whole, rather than on individual parts. This exciting new arena applies mathematical modeling and engineering methods to the study of biological systems. This book is the first of its kind to focus on the newly emerging field of systems biology with an emphasis on computational approaches. The work covers new concepts, methods for information storage, mining and knowledge extraction, reverse engineering of gene and metabolic networks, as well as modelling and simulation of multi-cellular systems. Central themes include strategies for predicting biological properties and methods for elucidating structure-function relationships.

Computational Systems Biology

Computational Systems Biology
Author : Roland Eils,Andres Kriete
Publisher : Elsevier Inc. Chapters
Release Date : 2013-11-26
Category : Medical
Total pages :548
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Computational Systems Biology of Pathogen-Host Interactions

Computational Systems Biology of Pathogen-Host Interactions
Author : Saliha Durmuş,Tunahan Çakır,Reinhard Guthke,Emrah Nikerel,Arzucan Özgür
Publisher : Frontiers Media SA
Release Date : 2016-05-30
Category : Electronic book
Total pages :198
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A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions: - Computational Inference of PHI Networks using Omics Data - Computational Prediction of PHIs - Text Mining of PHI Data from the Literature - Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data.

Computational Systems Biology

Computational Systems Biology
Author : Juergen Eilsa,Elena Herzoga,Baerbel Felder,Christian Lawerenza,Roland Eils
Publisher : Elsevier Inc. Chapters
Release Date : 2013-11-26
Category : Medical
Total pages :548
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Systems biology combines experimental and computational research to facilitate understanding of complex biological processes. In this chapter we describe data repositories, data standards, modeling, and visualization tools as prerequisites for systems biology research in order to help us to better study and understand biological processes. In addition, we propose improvements of these tools providing an example application (JUMMP) developed in our laboratory. We suggest that flexibility, interoperability, and modularity of novel applications contribute to better acceptance and further development of these tools. We also emphasize that having flexible and extendable standards describing complex and incomplete biological data allow new discoveries to be incorporated in a seamless way into systems biology tools. Overall, we discuss here advances, challenges and perspectives of data, and other platforms in systems biology which we believe will continue to make an impact on biomedical research.

Transactions on Computational Systems Biology I

Transactions on Computational Systems Biology I
Author : Corrado Priami
Publisher : Springer Science & Business Media
Release Date : 2005
Category : Computers
Total pages :110
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Thisisthe?rstissueofanewjournaloftheLNCSjournalsubline.Theaimofthe journal is to encourage inter- and multidisciplinary research in the ?elds of c- puter science and life sciences. The recent paradigmatic shift in biology towards a system view of biological phenomena requires a corresponding paradigmatic shift in the techniques from computer science that can face the new challenges. Classical tools usually used in bioinformatics are no longer up to date and new ideas are needed. The convergence of sciences and technologies we are experiencing these days is changing the classical terms of reference for research activities. In fact clear distinctions between disciplines no longer exist because advances in one ?eld permit advances in others and vice versa, thus establishing a positive feedback loop between sciences. The potential impact of the convergence of sciences and technologies is so huge that we must consider how to control and correctly drive our future activities. International and national funding agencies are looking at interdisciplinary research as a key issue for the coming years, especially in the intersection of life sciences and information technology. To speed up this process, we surely need to establish relationships between researchers of di?erent communities and to de?ne a common language that will allow them to exchange ideas and - sults. Furthermore, expectations of di?erent communities can be merged only by running activities like common projects and experiences. TheTransactionsonComputationalSystemsBiologycouldbeagoodforumto helplifescientistsandcomputerscientiststodiscusstogethertheircommongoals.

Transactions on Computational Systems Biology III

Transactions on Computational Systems Biology III
Author : Corrado Priami,Emanuela Merelli,Pedro Pablo Gonzalez,Andrea Omicini
Publisher : Springer Science & Business Media
Release Date : 2005-12-12
Category : Computers
Total pages :169
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The LNCS journal Transactions on Computational Systems Biology is devoted to inter- and multidisciplinary research in the fields of computer science and life sciences and supports a paradigmatic shift in the techniques from computer and information science to cope with the new challenges arising from the systems-oriented point of view of biological phenomena. This, the third Transactions on Computational Systems Biology volume, edited by Emanuela Merelli, Pedro Pablo Gonzalez and Andrea Omicini, is devoted to considerably extended versions of selected papers presented at the International Workshop on Network Tools and Applications in Biology (NETTAB 2004), held at the University of Camerino, in Camerino, Italy, in September 2004. Dedicated especially to models and metaphors from biology to bioinformatics tools, the 10 papers selected for the special issue cover a wide range of bioinformatics research such as data visualisation, protein/RNA structure prediction, motif finding, modelling and simulation of protein interaction, genetic linkage analysis, and notations and models for systems biology.

Computational Systems Biology

Computational Systems Biology
Author : Jean-Christophe Leloup,Didier Gonze,Albert Goldbeter
Publisher : Elsevier Inc. Chapters
Release Date : 2013-11-26
Category : Medical
Total pages :548
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Circadian rhythms originate from intertwined feedback processes in genetic regulatory networks. Computational models of increasing complexity have been proposed for the molecular mechanism of these rhythms, which occur spontaneously with a period on the order of 24h. We show that deterministic models for circadian rhythms in Drosophila account for a variety of dynamical properties, such as phase shifting or long-term suppression by light pulses and entrainment by light/dark cycles. Stochastic versions of these models allow us to examine how molecular noise affects the emergence and robustness of circadian oscillations. Finally, we present a deterministic model for the mammalian circadian clock and use it to address the dynamical bases of physiological disorders of the sleep/wake cycle in humans.

Computational Systems Biology

Computational Systems Biology
Author : Reinhard Laubenbacher,Pedro Mendes
Publisher : Elsevier Inc. Chapters
Release Date : 2013-11-26
Category : Medical
Total pages :548
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Mathematical and statistical network modeling is an important step toward uncovering the organizational principles and dynamic behavior of biological networks. This chapter focuses on methods to construct discrete dynamic models of gene regulatory networks from experimental data sets, also sometimes referred to as top-down modeling or reverse engineering. Time-discrete dynamical systems models have long been used in biology, particularly in population dynamics. The models mainly focused on here are also assumed to have a finite set of possible states for each variable. That is, the modeling framework discussed in this chapter is that of time-discrete dynamical systems over a finite state set.

Computational Systems Biology

Computational Systems Biology
Author : Jean-Luc Bouchot,William L. Trimble,Gregory Ditzler,Yemin Lan,Steve Essinger,Gail Rosen
Publisher : Elsevier Inc. Chapters
Release Date : 2013-11-26
Category : Medical
Total pages :548
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Recent advances in next-generation sequencing have enabled high-throughput determination of biological sequences in microbial communities, also known as microbiomes. The large volume of data now presents the challenge of how to extract knowledge—recognize patterns, find similarities, and find relationships—from complex mixtures of nucleic acid sequences currently being examined. In this chapter we review basic concepts as well as state-of-the-art techniques to analyze hundreds of samples which each contain millions of DNA and RNA sequences. We describe the general character of sequence data and describe some of the processing steps that prepare raw sequence data for inference. We then describe the process of extracting features from the data, assigning taxonomic and gene labels to the sequences. Then we review methods for cross-sample comparisons: (1) using similarity measures and ordination techniques to visualize and measure differences between samples and (2) feature selection and classification to select the most relevant features for discriminating between samples. Finally, in conclusion, we outline some open research problems and challenges left for future research.

Computational Systems Biology

Computational Systems Biology
Author : Ursula Klingmüller,Marcel Schilling,Sofia Depner,Lorenza A. D’Alessandro
Publisher : Elsevier Inc. Chapters
Release Date : 2013-11-26
Category : Medical
Total pages :548
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Cellular communication is mediated by extracellular stimuli that bind cellular receptors and activate intracellular signaling pathways. Principal biochemical reactions used for signal transduction are protein or lipid phosphorylation, proteolytic cleavage, protein degradation and complex formation mediated by protein-protein interactions. Within the nucleus, signaling pathways regulate transcription factor activity and gene expression. Cells differ in their competence to respond to extracellular stimuli. A deeper understanding of complex biological responses cannot be achieved by traditional approaches but requires the combination of experimental data with mathematical modeling. Following a systems biology approach, data-based mathematical models describing sub-modules of signaling pathways have been established. By combining computer simulations with experimental verification systems properties of signaling pathway including cycling behavior or threshold response could be identified. Yet, to analyze complex growth and maturation processes at a systems level and quantitatively predict the outcome of perturbations further advances in experimental and theoretical methodologies are required.