Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The text avoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. "An Introduction to Systems Biology: Design Principles of Biological Circuits" builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.

Yeast has been a preeminent experimental organism of genetic research for more than 50 years. Progress in the field has provided the conceptual framework that has driven experiments in many areas of biology. Landmark Papers in Yeast Biology consists of essays by prominent scientists on the context and significance of 71 carefully selected research papers, which are reprinted on the accompanying CD. The papers include early, hard-to-find classics as well as more recent advances in areas such as signal transduction, membrane trafficking, protein turnover, and genomics. This collection has unique value for all scholars of yeast and could provide the foundation for a literature-based course on molecular cell biology. As Jasper Rine notes in his eloquent introduction, the editors and contributors share the belief that deep study of the agreed-on classics is the best training for learning how to recognize those contemporary papers worthy of our personal time...

Stephen Paddock and a highly skilled panel of experts lead the researcher using confocal techniques from the bench top, through the imaging process, to the journal page. They concisely describe all the key stages of confocal imaging-from tissue sampling methods, through the staining process, to the manipulation, presentation, and publication of the realized image. Written in a user-friendly, nontechnical style, the methods specifically cover most of the commonly used model organisms: worms, sea urchins, flies, plants, yeast, frogs, and zebrafish. The powerful hands-on methods collected here will help even the novice to produce first-class cover-quality confocal images.

“... This book provides the essentials in a simple and easy to understand way, suitable for use as a primer for researchers new to this highly studied microbe, especially if coming to yeast from another organism... very well referenced, providing both classic seminal studies as well as more recent publications, making it a useful text for those starting out in the field...” – Phenotype, 2010 Yeast is one of the oldest domesticated organisms and has both industrial and domestic applications. In addition, it is very widely used as a eukaryotic model organism in biological research and has offered valuable knowledge of genetics and basic cellular processes. In fact, studies in yeast have offered insight in mechanisms underlying ageing and diseases such as Alzheimers, Parkinsons and cancer. Yeast is also widely used in the lab as a tool for many technologies such as two-hybrid analysis, high throughput protein purification and localization and gene expression profiling. The broad range of uses and applications of this organism undoubtedly shows that it is invalubale in research, technology and industry. This book is an up-to date resource providing a comprehensive account of yeast biology and its use as a tool and model organism for understanding cellular and molecular processes of eukaryotes. Topics covered range from the fundamentals of yeast biology such as cell structure, biochemistry, genetics and signaling, to current approaches and applications such as metabolomics, disease models and uses in biotechnology. Written by a top expert in the field, this book offers an invaluable companion to beginners and experts in yeast research.

Unparalleled in size and scope, this new major reference integrates academic and industrial knowledge into a single resource, allowing for a unique overview of the entire field. Adopting a systematic and practice-oriented approach, and including a wide range of technical and methodological information, this highly accessible handbook is an invaluable 'toolbox' for any bioengineer. In two massive volumes, it covers the full spectrum of current concepts, methods and application areas.

Synthetic Biology provides a framework to examine key enabling components in the emerging area of synthetic biology. Chapters contributed by leaders in the field address tools and methodologies developed for engineering biological systems at many levels, including molecular, pathway, network, whole cell, and multi-cell levels. The book highlights exciting practical applications of synthetic biology such as microbial production of biofuels and drugs, artificial cells, synthetic viruses, and artificial photosynthesis. The roles of computers and computational design are discussed, as well as future prospects in the field, including cell-free synthetic biology and engineering synthetic ecosystems. Synthetic biology is the design and construction of new biological entities, such as enzymes, genetic circuits, and cells, or the redesign of existing biological systems. It builds on the advances in molecular, cell, and systems biology and seeks to transform biology in the same way that synthesis transformed chemistry and integrated circuit design transformed computing. The element that distinguishes synthetic biology from traditional molecular and cellular biology is the focus on the design and construction of core components that can be modeled, understood, and tuned to meet specific performance criteria and the assembly of these smaller parts and devices into larger integrated systems that solve specific biotechnology problems. It includes contributions from leaders in the field presents examples of ambitious synthetic biology efforts including creation of artificial cells from scratch, cell-free synthesis of chemicals, fuels, and proteins, engineering of artificial photosynthesis for biofuels production, and creation of unnatural living organisms. It describes the latest state-of-the-art tools developed for low-cost synthesis of ever-increasing sizes of DNA and efficient modification of proteins, pathways, and genomes. It highlights key technologies for analyzing biological systems at the genomic, proteomic, and metabolomic levels which are especially valuable in pathway, whole cell, and multi-cell applications. It details mathematical modeling tools and computational tools which can dramatically increase the speed of the design process as well as reduce the cost of development.

Our genome is the blueprint to our existence: it encodes all the information we need to develop from a single cell into a hugely complicated functional organism. But it is more than a static information store: our genome is a dynamic, tightly-regulated collection of genes, which switch on and off in many combinations to give the variety of cells from which our bodies are formed. But how do we identify the genes that make up our genome? How do we determine their function? And how do different genes form the regulatory networks that direct the processes of life? Introduction to Genomics is a fascinating insight into what can be revealed from the study of genomes: how organisms differ or match; how different organisms evolved; how the genome is constructed and how it operates; and what our understanding of genomics means in terms of our future health and wellbeing. Covering the latest techniques that enable us to study the genome in ever-increasing detail, the book explores what the genome tells us about life at the level of the molecule, the cell, and the organism. Learning features throughout make this book the ideal teaching and learning tool: extensive end of chapter exercises and problems help the student to fully grasp the concepts being presented, while end of chapter weblems (web-based problems) and lab assignments give the student the opportunity to engage with the subject in a hands-on manner. The field of genomics is enabling us to analyze life in more detail than ever before; Introduction to Genomics is the perfect guide to this enthralling subject. Online Resource Centre The Online Resource Centre to accompany Introduction to Genomics features For lecturers: Figures from the book in electronic format For students: Answers to end-of-chapter exercises Guided tour of web sites in genomics Hints to end-of-chapter problems Rotating figures

A comprehensive account of genomic rearrangement, focusing on the mechanisms of inversion, translocation, gene and genome duplication and gene transfer and on the patterns that result from them in comparative maps. Includes analyses of genomic sequences in organelles, prokaryotes and eukaryotes as well as comparative maps of the nuclear genomes in higher plants and animals. The book showcases a variety of algorithmic and statistical approaches to rearrangement and map data.

A comprehensive compendium of cutting-edge protocols for the generation of molecular diversity. Described in step-by-step detail to ensure experimental success, these protocols include readily reproducible methods for random mutagenesis of entire genes or segments of genes, for homologous and nonhomologus recombination, and for constructing in vivo libraries in bacteria and yeast. In addition to the various protocols for creating libraries, this volume also describes ways to analyze libraries, particularly those made by recombination. An accompanying volume, Directed Enzyme Evolution: Screening and Selection Methods (ISBN: 1-58829-286-X), is devoted entirely to selection and screening methods that can be applied to the directed evolution of enzymes. Copy for Both VolumesDirected Evolution Library Creation: Methods and Protocols and Directed Enzyme Evolution: Screening and Selection Methods constitute an extraordinary collection of all the key methods used today for directed evolution research. Described in step-by-step detail to ensure robust experimental results, these methods will enable both newcomers and more experienced investigators to design and implement directed evolution strategies for the engineering of novel proteins. The first volume describes methods for the creation of mutated DNA molecules, or DNA libraries, encoding variants of desired proteins. The second volume describes methods for screening DNA libraries to isolate mutant proteins that exhibit a specified function.

This volume and its companion, Volume 351, are specifically designed to meet the needs of graduate students and postdoctoral students as well as researchers, by providing all the up-to-date methods necessary to study genes in yeast. Procedures are included that enable newcomers to set up a yeast laboratory and to master basic manipulations. Relevant background and reference information given for procedures can be used as a guide to developing protocols in a number of disciplines. Specific topics addressed in this book include basic techniques, making mutants, genomics, and proteomics.

About This Book
Leverage Python' s most powerful open-source libraries for deep learning, data wrangling, and data visualization
Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets
Who This Book Is For
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
What You Will Learn
Explore how to use different machine learning models to ask different questions of your data
Learn how to build neural networks using Keras and Theano
Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
Discover how to embed your machine learning model in a web application for increased accessibility
Predict continuous target outcomes using regression analysis
Uncover hidden patterns and structures in data with clustering
Organize data using effective pre-processing techniques
Get to grips with sentiment analysis to delve deeper into textual and social media data
Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

这本书主要是用 pandas 连接 SciPy 和 NumPy，用pandas做数据处理是Pycon2012上一个很热门的话题。另一个功能强大的东西是Sage，它将很多开源的软件集成到统一的 Python 接口。
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.
Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.
Use the IPython interactive shell as your primary development environment
Learn basic and advanced NumPy (Numerical Python) features
Get started with data analysis tools in the pandas library
Use high-performance tools to load, clean, transform, merge, and reshape data
Create scatter plots and static or interactive visualizations with matplotlib
Apply the pandas groupby facility to slice, dice, and summarize datasets
Measure data by points in time, whether it’s specific instances, fixed periods, or intervals
Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.