Computational Approaches in Molecular Radiation Biology: Monte Carlo Methods by Matesh N. Varma

Computational Approaches in Molecular Radiation Biology: Monte Carlo Methods

EditorMatesh N. Varma, Aloke Chatterjee

Hardcover | February 28, 1995

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Proceedings of a DOE workshop on computational approach Monte Carlo methods in molecular radiation biology, held in Irvine, California, April 1993. Eighteen invited scientific papers document the progress in the field and point to new research directions. The papers are organized within five groups: significance of computational biology; initial physical and chemical studies; track structure code development; comparison of track structure codes; and modeling of biological effects. Annotation c. by Book News, Inc., Portland, Or.

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Title:Computational Approaches in Molecular Radiation Biology: Monte Carlo MethodsFormat:HardcoverDimensions:274 pages, 10 × 7.01 × 0.03 inPublished:February 28, 1995Publisher:Springer US

The following ISBNs are associated with this title:

ISBN - 10:0306449951

ISBN - 13:9780306449956

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Table of Contents

Significance of Computational Biology: Computational Biology Opportunity and Challenges for the Future; J.C. Wooley, M.N. Varma. Overview of Significant Challenges in Molecular Biology Amenable to Computational Methods; R.M. Glaeser. Initial Physical and Chemical Studies: Basic Physical and Chemical Information Needed for Development of Monte Carlo Codes; M. Inokuti. Interactions of Lowenergy Electrons with Condensed Matter: Relevance for Track Structure; R.H. Ritchie, et al. Track Structure Code Development: Chargedparticle Transport in Biomolecular Media: The Third Generation; M. Zaider, et al. Track Structure, Chromosome Geometry, and Chromosome Aberations; D.J. Brenner, et al. Comparison of Track Structure Codes: Comparisons of Various Monte Carlo Track Structure Codes for Energetic Electrons in Gaseous and Liquid Water; H. Nikjoo, S. Uehara. A Comparison between Two Monte Carlo Track Codes on Determination of Transient Chemical Yields; R.N. Hamm, et al. Modeling of Biological Effects: Three Statistical Technologies with High Potential in Biological Imaging and Modeling; M. Fridman, M. Steele. A Nucleosome Model for the Simulation of DNA Strand Break Experiments; M. Terrisol, E. Pomplun. Index.