Computational Toxicology: Volume II

ISBN-13: 9781627030588 / Angielski / Twarda / 2012 / 648 str.

Brad Reisfeld; Arthur N. Mayeno

Opis

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.

Spis treści

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. Niemelä, 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

            Jiří 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

            Hervé Abdi and Lynne J. Williams

 

24. Maximum Likelihood

            Shuying Yang and Daniela De Angelis

 

25. Bayesian Inference

            Frédéric Y. Bois

Blurb

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 science, from streamlining drug efficacy and safety testing, to increasing the efficiency and effectiveness of risk assessment for environmental chemicals. Computational Toxicology provides biomedical and quantitative scientists with essential background, context, examples, useful tips, and an overview of current developments in the field. Divided into four sections, Volume I covers a wide array of methodologies and topics. Opening with an introduction to the field of computational toxicology and its current and potential applications, the volume continues with ’best practices’ in mathematical and computational modeling, followed by chemoinformatics and the use of computational techniques and databases to predict chemical properties and toxicity, as well as an overview of molecular dynamics.  The final section is a compilation of the key elements and main approaches used in pharmacokinetic and pharmacodynamic modeling, including the modeling of absorption, compartment and non-compartmental modeling, physiologically based pharmacokinetic modeling, interspecies extrapolation, and population effects. Written in the successful Methods in Molecular Biology™ series format where possible, chapters include introductions to their respective topics, lists of the materials and software tools used, methods, and notes on troubleshooting.

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.