|
 |
 |
 |
Caltech and UCSD researchers shed light on how proteins find their shapes
February 24, 2009
The researchers bring together theoretical models and experimental data to explain protein folding PASADENA, Calif.--Researchers from the California Institute of Technology (Caltech) and the University of California at San Diego (UCSD) have brought together UCSD theoretical modeling and Caltech experimental data to show just how amino-acid chains might fold up into unique, three-dimensional functional proteins. Their insights were recently published in the February 10 issue of the Proceedings of the National Academy of Sciences (PNAS). The paper details the matching of a series of protein-folding models created by the UCSD team (led by Peter Wolynes, UCSD professor of chemistry and biochemistry and physics) with experimental data gathered using a novel technique created by the Caltech team (led by Faculty Associate in Chemistry Jay Winkler and Harry Gray, Caltech's Arnold O. Beckman Professor of Chemistry and founding director of the Beckman Institute). The Winkler-Gray method of watching proteins as they crumple and fold involves the use of a picosecond camera that captures fluorescent flashes as a laser pulse excites a donor probe, which emits light and transfers that light to an acceptor probe. The distance between the donor and acceptor change as the amino-acid chain transforms itself into a three-dimensional protein. In the PNAS paper, the two groups combined the Caltech experimental technique--first described in a 2002 paper published in the Journal of the American Chemical Society--with Wolynes's protein-folding models to see if they could come up with the precise folding pattern of cytochrome c, a protein that is part of the mitochondrial electron-transfer chain that turns food into cellular energy. At first the models and the experimental data seemed to be describing two entirely different things, according to Winkler. "The researchers had to account for charge-charge interactions between amino acids that appear to be important--the way that like charges repel and opposite charges attract," he explains. "And they had to consider the hydrophobic interactions--the way that oily parts of the proteins like to stick together but are repelled by the watery parts. When their models took account of these interactions, it fit the experimental data." "It was the first time anyone has been able to develop a theoretical model able to account for the results we've been getting with our time-resolved energy-transfer experiments," adds Gray. California Institute of Technology

|
The Statistical Analysis of Experimental Data (Dover Books on Engineering)
by John Mandel (Author)
First half of book presents fundamental mathematical definitions, concepts, and facts while remaining half deals with statistics primarily as an interpretive tool. Well-written text and numerous worked examples with step-by-step presentation. 116 tables.
|

|
Experimental Design and Data Analysis for Biologists
by Gerry P. Quinn (Author), Michael J. Keough (Author)
This essential textbook is designed for students or researchers in biology who need to design experiments, sampling programs, or analyze resulting data. The text begins with a revision of estimation and hypothesis testing methods, before advancing to the analysis of linear and generalized linear models. The chapters include such topics as linear and logistic regression, simple and complex ANOVA models, log-linear models, and multivariate techniques. The main analyses are illustrated with many examples from published papers and an extensive reference list to both the statistical and biological literature is also included. The book is supported by a web-site that provides all data sets, questions for each chapter and links to software.
|

|
Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis
by Colleen McCue Ph.D. Experimental Psychology (Author)
It is now possible to predict the future when it comes to crime. In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions. In this book Dr. McCue describes her use of "off the shelf" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity.
Knowledge of advanced...
|

|
Data Analysis for Experimental Design
by Richard Gonzalez PhD (Author)
This engaging text shows how statistics and methods work together, demonstrating a variety of techniques for evaluating statistical results against the specifics of the methodological design. Richard Gonzalez elucidates the fundamental concepts involved in analysis of variance (ANOVA), focusing on single degree-of-freedom tests, or comparisons, wherever possible. Potential threats to making a causal inference from an experimental design are highlighted. With an emphasis on basic between-subjects and within-subjects designs, Gonzalez resists presenting the countless "exceptions to the rule" that make many statistics textbooks so unwieldy and confusing for students and beginning researchers. Ideal for graduate courses in experimental design or data analysis, the text may also be used by...
|

|
Cross Section and Experimental Data Analysis Using EViews
by I. Gusti Ngurah Agung (Author)
A practical guide to selecting and applying the most appropriate model for analysis of cross section data using EViews."This book is a reflection of the vast experience and knowledge of the author. It is a useful reference for students and practitioners dealing with cross sectional data analysis ... The strength of the book lies in its wealth of material and well structured guidelines ..." Prof. Yohanes Eko Riyanto, Nanyang Technological University, Singapore"This is superb and brilliant. Prof. Agung has skilfully transformed his best experiences into new knowledge ... creating a new way of understanding data analysis." Dr. I Putu Gede Ary Suta, The Ary Suta Center, JakartaBasic theoretical concepts of statistics as well as sampling methods are often misinterpreted by students and less...
|

|
Identification of Parametric Models: from Experimental Data (Communications and Control Engineering)
by Eric Walter (Author), Luc Pronzato (Author), J. Norton (Assistant)
The presentation of a coherent methodology for the estimation of the parameters of mathematical models from experimental data is examined in this volume. Many topics are covered including the choice of the structure of the mathematical model, the choice of a performance criterion to compare models, the optimization of this performance criterion, the evaluation of the uncertainty in the estimated parameters, the design of experiments so as to get the most relevant data and the critical analysis of results. There are also several features unique to the work such as an up-to-date presentation of the methodology for testing models for identifiability and distinguishability and a comprehensive treatment of parametric optimization which includes greater consider ation of numerical aspects and...
|

|
Statistical Treatment of Experimental Data: An Introduction to Statistical Methods
by Hugh D. Young (Author)
A concise, highly readable introduction to statistical methods! Even with a limited mathematics background, readers can understand what statistical methods are and how they may be used to obtain the best possible results from experimental measurements and data. The author describes the physical bases on which statistical theories are developed, and derives from them useful mathematical results and formulas for the evaluation and analysis of experimental data. Special mathematical techniques are explained as they are needed.
|

|
Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics)
by Mark J. van der Laan (Author), Sherri Rose (Author)
Establishes causal inference methodology that incorporates the benefits of machine learning with statistical inferencePresentation combines accessibility with the method's rigorous grounding in statistical theoryDemonstrates targeted learning in epidemiological, medical, and genomic experimental and observational studies that include informative dropout, missingness, time-dependent confounding, and case-control sampling The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full...
|

|
Designing Experiments and Analyzing Data: A Model Comparison Perspective, Second Edition
by Scott E. Maxwell (Author), Harold D. Delaney (Author)
Through this book's unique model comparison approach, students and researchers are introduced to a set of fundamental principles for analyzing data. After seeing how these principles can be applied in simple designs, students are shown how these same principles also apply in more complicated designs. Drs. Maxwell and Delaney believe that the model comparison approach better prepares students to understand the logic behind a general strategy of data analysis appropriate for various designs; and builds a stronger foundation, which allows for the introduction of more complex topics omitted from other books. Several learning tools further strengthen the reader's understanding: *flowcharts assist in choosing the most appropriate technique; *an equation...
|
|
|
Statistical Treatment of Experimental Data
by Hugh D. Young (Author)
Dealing with statistical treatment of experimental data, this text covers topics such as errors, probability, the binomial distribution, the Poisson distribution, the Gauss distribution, method of least squares and standard deviation of the mean.
|
|