Methods for building QSARs / James Devillers. Accessing and using chemical databases / Nikolai Nikolov ... [et al.] From QSAR to QSIIR : searching for enhanced computational toxicology models / Hao Zhu. Mutagenicity, carcinogenicity, and other end points / Romualdo Benigni ... [et al.] Classification models for safe drug molecules / A.K. Madan, Sanjay Bajaj, and Harish Dureja. QSAR and metabolic assessment tools in the assessment of genotoxicity / Andrew P. Worth, Silvia Lapenna, and Rositsa Serafimova. Gene expression networks / Reuben Thomas and Christopher J. Portier. Construction of cell type-specific logic models of signaling networks using CellNOpt / Melody K. Morris, Ioannis Melas, and Julio Saez-Rodriguez. Regulatory networks / Gilles Bernot, Jean-Paul Comet, and Christine Risso-de Faverney. Computational reconstruction of metabolic networks from KEGG / Tingting Zhou. Biomarkers / Harmony Larson ... [et al.] Biomonitoring-based environmental public health indicators / Andrey I. Egorov, Dafina Dalbokova, and Michal Krzyzanowski. Modeling for regulatory purposes (risk and safety assessment) / Hisham El-Masri. Developmental toxicity prediction / Raghuraman Venkatapathy and Nina Ching Y. Wang. Predictive computational toxicology to support drug safety assessment / Luis G. Valerio Jr. Developing a practical toxicogenomics data analysis system utilizing open-source software / Takehiro Hirai and Naoki Kiyosawa. Systems toxicology from genes to organs / John Jack, John Wambaugh, and Imran Shah. Agent-based models of cellular systems / Nicola Cannata ... [et al.] Linear algebra / Kenneth Kuttler. Ordinary differential equations / Jiří Lebl. On the development and validation of QSAR models / Paola Gramatica. Principal components analysis / Detlef Groth ... [et al.] Partial least squares methods : partial least squares correlation and partial least square regression / Hervé Abdi and Lynne J. Williams. Maximum likelihood / Shuying Yang and Daniela De Angelis. Bayesian inference / Frederic Y. Bois.

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(t) 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.