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This work offers a guided walkthrough of one of the most promising research areas in modern life sciences, enabling a deeper understanding of involved concepts and methodologies via an interdisciplinary view, focusing on both well-established approaches and cutting-edge research. Highlighting what pathway analysis can offer to both the experimentalist and the modeler, the text opens with an introduction to a general methodology that outlines common workflows shared by several methods. This is followed by a review of pathway and sub-pathway based approaches for systems pharmacology. The work then presents an overview of pathway analysis methods developed to model the temporal aspects of drug- or disease-induced perturbations and extract relevant dynamic themes. The text concludes by discussing several state-of-the-art methods in pathway analysis, which address the important problem of identifying differentially expressed pathways and sub-pathways.
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
This book introduces drug researchers to the novel computational approaches of pathway analysis and explains the existing applications that can save time and money in the drug discovery process. It covers traditional computational methods and software for pathway analysis microarray, proteomics, and metabolomics. It explains pathway reconstruction of diseases and toxic states, pathway analysis in various phases, dynamic modeling of drug responses, and more. This is a core resource for drug discovery and pharmaceutical industry researchers, chemists, and biologists and for professionals in related fields.
This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms.The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics.The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.
This book constitutes the refereed proceedings of the International Conference on Computational Methods in Systems Biology, CMSB 2006, held in Trento, Italy, in October 2006. The 22 fully revised papers presented together with 2 invited talks were carefully reviewed and selected from 68 submissions. The papers present a variety of techniques from computer sciences, such as language design, concurrency theory, software engineering, and formal methods.
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
The Computational Methods in Systems Biology (CMSB) workshop series was established in 2003 by Corrado Priami. The purpose of the workshop series is to help catalyze the convergence between computer scientists interested in language design, concurrency theory, software engineering or program verification, and physicists, mathematicians and biologists interested in the systems-level understanding of cellular processes. Systems biology was perceived as being increasingly in search of sophisticated modeling frameworks whether for representing and processing syst- level dynamics or for model analysis, comparison and refinement. One has here a clear-cut case of a must-explore field of application for the formal methods developed in computer science in the last decade. This proceedings consists of papers from the CMSB 2003 workshop. A good third of the 24 papers published here have a distinct formal methods origin; we take this as a confirmation that a synergy is building that will help solidify CMSB as a forum for cross-community exchange, thereby opening new theoretical avenues and making the field less of a potential application and more of a real one. Publication in Springer's new Lecture Notes in Bioinformatics (LNBI) offers particular visibility and impact, which we gratefully acknowledge. Our keynote speakers, Alfonso Valencia and Trey Ideker, gave challenging and somewhat humbling lectures: they made it clear that strong applications to systems biology are still some way ahead. We thank them all the more for accepting the invitation to speak and for the clarity and excitement they brought to the conference.
This book constitutes the refereed proceedings of the International Workshop on Computational Methods in Systems Biology, CMSB 2003, held in Rovereto, Italy, in February 2003. The 11 revised full papers presented together with 2 invited papers, 7 position papers, and 11 abstracts were carefully reviewed and selected from 30 submissions. Among the topics addressed are modeling languages for systems biology, concurrency in biological systems, constraint programming, logical methods in systems biology, formal methods for the analysis of biomolecular systems, quantitative analysis of biomolecular systems, and simulation and modeling techniques for systems biology.
Computational methods are playing an ever increasing role in cell biology. This volume of Methods in Cell Biology focuses on Computational Methods in Cell Biology and consists of two parts: (1) data extraction and analysis to distill models and mechanisms, and (2) developing and simulating models to make predictions and testable hypotheses. Focuses on computational methods in cell biology Split into 2 parts--data extraction and analysis to distill models and mechanisms, and developing and simulating models to make predictions and testable hypotheses Emphasizes the intimate and necessary connection with interpreting experimental data and proposing the next hypothesis and experiment
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.