Targeted Cancer Treatment in Silico: Small Molecule Inhibitors and Oncolytic Viruses

ISBN-13: 9781493942497 / Angielski / Miękka / 2016 / 227 str.

Natalia L. Komarova; Dominik Wodarz

Opis

Countless medical researchers over the past century have been occupied by the search for a cure of cancer. So far, they have developed and implemented a wide range of treatment techniques, including surgery, chemo- and radiotherapy, antiangiogenic drugs, small molecule inhibitors, and oncolytic viruses. However, patterns of these treatments' effectiveness remain largely unclear, and a better understanding of how cancer therapies work has become a key research goal. Cancer Treatment in Silico provides the first in-depth study of approaching this understanding by modeling cancer treatments, both mathematically and through computer simulations.The main goal of this book is to help expose students and researchers to in silico methods of studying cancer. It is intended for both the applied mathematics and experimental oncology communities, as mathematical models are playing an increasingly important role to supplement laboratory biology in the fight against cancer. Written at a level that generally requires little technical background, the work will be a valuable resource for scientists and students alike.

Recenzje

From the reviews:

"The intended audience includes advanced undergraduate and graduate students in applied mathematics as well as in the field of biology with a special interest in oncology. It could serve as a guide to scientists and researchers in the field of oncology and therapeutics. ... This is the first in-depth study of mathematical and computational approaches used to understand cancer therapies. The crisp language, well-structured chapters, and precise examples are the highlights of this book." (Parthiv Amin, Doody's Book Reviews, March, 2014)

Spis treści

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.

Blurb

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.