rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Targeted Cancer Treatment in Silico
    sub title: Small Molecule Inhibitors and Oncolytic Viruses
  name:
    name part: Komarova, Natalia L.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Wodarz, Dominik.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 227 p. 71 illus., 26 illus. in color. online resource.
  abstract: This monograph provides the first in-depth study of how mathematical and computational approaches can be used to advance our understanding of cancer therapies and to improve treatment design and outcome. Over the past century, the search for a cancer cure has been a primary occupation of medical researchers. So far, it has led to a wide range of treatment techniques, including surgery, chemo- and radiotherapy, antiangiogenic drugs, and most recently, small molecule inhibitors and oncolytic viruses. Each treatment tends to have a certain effectiveness in a specific class of patients, but it is often unclear what exactly causes it to succeed or fail. Recent technological advances have given rise to an ever increasing pool of data and information that highlight the complexity underlying the cancers and their response to treatment. Next to experimental and clinical research, mathematical and computational approaches are becoming an indispensible tool to understand this complexity. Targeted Cancer Treatment in Silico is organized into two parts, corresponding to two types of targeted cancer treatment: small molecule inhibitors and oncolytic viruses. In each part, the authors provide a brief overview of the treatment’s biological basis and present the mathematical methods most suitable for modeling it. Additionally, they discuss how these methods can be applied to answer relevant questions about treatment mechanisms and propose modifications to treatment approaches that may potentially increase success rates. The book is intended for both the applied mathematics and experimental oncology communities, as mathematical models are becoming an increasingly important supplement to laboratory biology in the fight against cancer. Written at a level that generally requires little technical background, it will be a valuable resource for scientists and graduate students alike, and can also serve as an upper-division undergraduate or graduate textbook.
  table of contents: Background and Scope of the Book -- Part I Treatment of Cancer with Small Molecule Inhibitors -- An Introduction to Small Molecule Inhibitors and Chronic Myeloid Leukemia -- Basic Dynamics of Chronic Myeloid Leukemia During Imatinib Treatment -- Stochastic Modeling of Cellular Growth, Treatment, and Resistance Generation -- Evolutionary Dynamics of Drug Resistant Mutants in Targeted Treatment of CML -- Effect of Cellular Quiescence on the Evolution of Drug Resistance in CML -- Combination Therapies: Short term versus Long term Strategies -- Cross Resistance: Treatment and Modeling -- Mathematical Modeling of Cyclic Cancer Treatments -- Part II Treatment of Cancer with Oncolytic Viruses -- Introduction to Oncolytic Viruses -- Basic Dynamics of Oncolytic Viruses -- Mitotic Virus Transmission and Immune Responses -- Axiomatic Approaches to Oncolytic Virus Modeling -- Spatial Oncolytic Virus Dynamics -- Oncolytic Viruses and the Eradication of Drug-resistant Tumor Cells.
  note: by Natalia L. Komarova, Dominik Wodarz.
  subject:
    topic: Mathematics
  subject:
    topic: Cancer research
  subject:
    topic: Oncology
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Biomathematics
  subject:
    topic: Mathematics
  subject:
    topic: Physiological, Cellular and Medical Topics
  subject:
    topic: Cancer Research
  subject:
    topic: Mathematical and Computational Biology
  subject:
    topic: Oncology
  subject:
    topic: Genetics and Population Dynamics
  subject:
    topic: Applications of Mathematics
  classification: QH323.5
  classification: 570.285
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Modeling and Simulation in Science, Engineering and Technology
  identifier: 9781461483014
  identifier: http://dx.doi.org/10.1007/978-1-4614-8301-4
  record info:
    record creation date: 131115
    record change date: 20151204161724.0
    record identifier: 978-1-4614-8301-4