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MORE ABOUT THIS BOOK
Main description:
Rapid advances in computer science, biology, chemistry, and other disciplines are enabling powerful new computational tools and models for toxicology and pharmacology. These computational tools hold tremendous promise for advancing applied and basic science, from streamlining drug efficacy and safety testing, to increasing the efficiency and effectiveness of risk assessment for environmental chemicals. Computational Toxicology was conceived to provide both experienced and new biomedical and quantitative scientists with essential background, context, examples, useful tips, and an overview of current developments in the field. This two-volume set serves as a resource to help introduce and guide readers in the development and practice of these tools to solve problems and perform analyses in this area.
Divided into six sections, Volume II covers a wide array of methodologies and topics. The volume begins by exploring the critical area of predicting toxicological and pharmacological endpoints, as well as approaches used in the analysis of gene, signaling, regulatory, and metabolic networks. The next section focuses on diagnostic and prognostic molecular indicators (biomarkers), followed by the application of modeling in the context of government regulatory agencies. Systems toxicology approaches are also introduced. The volume closes with primers and background on some of the key mathematical and statistical methods covered earlier, as well as a list of other resources. Written in a format consistent with the successful Methods in Molecular Biology (TM) series where possible, chapters include introductions to their respective topics, lists of the necessary materials and software tools used, methods, and notes on troubleshooting and avoiding known pitfalls.
Authoritative and easily accessible, Computational Toxicology will allow motivated readers to participate in this exciting field and undertake a diversity of realistic problems of interest.
Contents:
Part 1. Toxicological/Pharmacological Endpoint Prediction
1. Methods for Building QSARs
James Devillers
2. Accessing and Using Chemical Databases
Nikolai Nikolov, Todor Pavlov, Jay R. Niemela, and Ovanes Mekenyan
3. From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models
Hao Zhu
4. Mutagenicity, Carcinogenicity and Other Endpoints
Romualdo Benigni, Chiara Laura Battistelli, Cecilia Bossa, Mauro Colafranceschi, and Olga Tcheremenskaia
5. Classification Models for Safe Drug Molecules
A.K. Madan, Sanjay Bajaj, and Harish Dureja
6. QSAR and Metabolic Assessment Tools in the Assessment of Genotoxicity
Andrew P. Worth, Silvia Lapenna, and Rositsa Serafimova
Part II. Biological Network Modeling
7. Gene Expression Networks
Reuben Thomas and Christopher J. Portier
8. Construction of Cell Type-Specific Logic Models of Signaling Networks Using CellNetOptimizer
Melody K. Morris, Ioannis Melas, and Julio Saez-Rodriguez
9. Regulatory Networks
Gilles Bernot, Jean-Paul Comet, and Christine Risso- de Faverney
10. Computational Reconstruction of Metabolic Networks from KEGG
Tingting Zhou
Part III. Biomarkers
11. Biomarkers
Harmony Larson, Elena Chan, Sucha Sudarsanam, and Dale E. Johnson
12. Biomarkers: Environmental Public Health Indicators
Andrey I. Egorov, Dafina Dalbokova, and Michal Kryzanowski
Part IV. Modeling for Regulatory Purposes (Risk and Safety Assessment)
13. Modeling for Regulatory Purposes (Risk and Safety Assessment)
Hisham El-Masri
14. Developmental Toxicity Prediction
Raghuraman Venkatapathy and Nina Ching Y. Wang
15. Predictive Computational Toxicology to Support Drug Safety Assessment
Luis G. Valerio, Jr.
Part V. Integrated Modeling/Systems Toxicology Approaches
16. Developing a Practical Toxicogenomics Data Analysis System Utilizing Open-Source Software
Takehiro Hirai and Naoki Kiyosawa
17. Systems Toxicology from Genes to Organs
John Jack, John Wambaugh, and Imran Shah
18. Agent Based Models of Cellular Systems
Nicola Cannata, Flavio Corradini, Emanuela Merelli, and Luca Tesei
Part VI. Mathematical and Statistical Background
19. Linear Algebra
Kenneth Kuttler
20. Ordinary Differential Equations
Jiri Lebl
21. On the Development and Validation of QSAR Models
Paola Gramatica
22. Principal Components Analysis
Detlef Groth, Stefanie Hartmann, Sebastian Klie, and Joachim Selbig
23. Partial Least Square Methods: Partial Least Squares Correlation and Partial Least Square Regression
Herve Abdi and Lynne J. Williams
24. Maximum Likelihood
Shuying Yang and Daniela De Angelis
25. Bayesian Inference
Frederic Y. Bois
PRODUCT DETAILS
Publisher: Springer (Humana Press Inc.)
Publication date: October, 2012
Pages: 659
Weight: 652g
Availability: Available
Subcategories: Pharmacology
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