Download Free Spatiotemporal Modeling Of Cancer Immunotherapy Book in PDF and EPUB Free Download. You can read online Spatiotemporal Modeling Of Cancer Immunotherapy and write the review.

The focus of this book is a detailed discussion of a dual cancer vaccine (CV)-immune checkpoint inhibitor (ICI) mathematical model formulated as a system of partial differential equations (PDEs) defining the spatiotemporal distribution of cells and biochemicals during tumor growth. A computer implementation of the model is discussed in detail for the quantitative evaluation of CV-ICI therapy. The coding (programming) consists of a series of routines in R, a quality, open-source scientific computing system that is readily available from the internet. The routines are based on the method of lines (MOL), a general PDE algorithm that can be executed on modest computers within the basic R system. The reader can download and use the routines to confirm the model solutions reported in the book, then experiment with the model by varying the parameters and modifying/extending the equations, and even studying alternative models with the PDE methodology demonstrated by the CV-ICI model. Spatiotemporal Modeling of Cancer Immunotherapy: Partial Differential Equation Analysis in R facilitates the use of the model, and more generally, computer- based analysis of cancer immunotherapy mathematical models, as a step toward the development and quantitative evaluation of the immunotherapy approach to the treatment of cancer. To download the R routines, please visit: http://www.lehigh.edu/~wes1/ci_download
The focus of this book is a detailed discussion of a dual cancer vaccine (CV)-immune checkpoint inhibitor (ICI) mathematical model formulated as a system of partial differential equations (PDEs) defining the spatiotemporal distribution of cells and biochemicals during tumor growth. A computer implementation of the model is discussed in detail for the quantitative evaluation of CV-ICI therapy. The coding (programming) consists of a series of routines in R, a quality, open-source scientific computing system that is readily available from the internet. The routines are based on the method of lines (MOL), a general PDE algorithm that can be executed on modest computers within the basic R system. The reader can download and use the routines to confirm the model solutions reported in the book, then experiment with the model by varying the parameters and modifying/extending the equations, and even studying alternative models with the PDE methodology demonstrated by the CV-ICI model. Spatiotemporal Modeling of Cancer Immunotherapy: Partial Differential Equation Analysis in R facilitates the use of the model, and more generally, computer- based analysis of cancer immunotherapy mathematical models, as a step toward the development and quantitative evaluation of the immunotherapy approach to the treatment of cancer.
The book aims to provide an introduction to mathematical models that describe the dynamics of tumor growth and the evolution of tumor cells. It can be used as a textbook for advanced undergraduate or graduate courses, and also serves as a reference book for researchers. The book has a strong evolutionary component and reflects the viewpoint that cancer can be understood rationally through a combination of mathematical and biological tools. It can be used both by mathematicians and biologists. Mathematically, the book starts with relatively simple ordinary differential equation models, and subsequently explores more complex stochastic and spatial models. Biologically, the book starts with explorations of the basic dynamics of tumor growth, including competitive interactions among cells, and subsequently moves on to the evolutionary dynamics of cancer cells, including scenarios of cancer initiation, progression, and treatment. The book finishes with a discussion of advanced topics, which describe how some of the mathematical concepts can be used to gain insights into a variety of questions, such as epigenetics, telomeres, gene therapy, and social interactions of cancer cells.
This book covers multi-scale biomechanics for oncology, ranging from cells and tissues to whole organ. Topics covered include, but not limited to, biomaterials in mechano-oncology, non-invasive imaging techniques, mechanical models of cell migration, cancer cell mechanics, and platelet-based drug delivery for cancer applications. This is an ideal book for graduate students, biomedical engineers, and researchers in the field of mechanobiology and oncology. This book also: Describes how mechanical properties of cancer cells, the extracellular matrix, tumor microenvironment and immuno-editing, and fluid flow dynamics contribute to tumor progression and the metastatic process Provides the latest research on non-invasive imaging, including traction force microscopy and brillouin confocal microscopy Includes insight into NCIs’ role in supporting biomechanics in oncology research Details how biomaterials in mechano-oncology can be used as a means to tune materials to study cancer
Mathematical Modeling and Immunology An enormous amount of human effort and economic resources has been directed in this century to the fight against cancer. The purpose, of course, has been to find strategies to overcome this hard, challenging and seemingly endless struggle. We can readily imagine that even greater efforts will be required in the next century. The hope is that ultimately humanity will be successful; success will have been achieved when it is possible to activate and control the immune system in its competition against neoplastic cells. Dealing with the above-mentioned problem requires the fullest pos sible cooperation among scientists working in different fields: biology, im munology, medicine, physics and, we believe, mathematics. Certainly, bi ologists and immunologists will make the greatest contribution to the re search. However, it is now increasingly recognized that mathematics and computer science may well able to make major contributions to such prob lems. We cannot expect mathematicians alone to solve fundamental prob lems in immunology and (in particular) cancer research, but valuable sup port, however modest, can be provided by mathematicians to the research aspirations of biologists and immunologists working in this field.
Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scientific disciplines and a resurgence bf interest in the modern as well as the clas sical techniques of applied mathematics. This renewal of interest, both in research and teaching, has led to the establishment of the series: Texts in Applied Mat!!ematics (TAM). The development of new courses is a natural consequence of a high level of excitement oil the research frontier as newer techniques, such as numerical and symbolic cotnputer systems, dynamical systems, and chaos, mix with and reinforce the traditional methods of applied mathematics. Thus, the purpose of this textbook series is to meet the current and future needs of these advances and encourage the teaching of new courses. TAM will publish textbooks suitable for use in advanced undergraduate and beginning graduate courses, and will complement the Applied Math ematical Sciences (AMS) series, which will focus on advanced textbooks and research level monographs. Preface to the Second Edition This book covers those topics necessary for a clear understanding of the qualitative theory of ordinary differential equations and the concept of a dynamical system. It is written for advanced undergraduates and for beginning graduate students. It begins with a study of linear systems of ordinary differential equations, a topic already familiar to the student who has completed a first course in differential equations.
Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections: • Reviews of Computational Methods • Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels • The Interface of Biotic and Abiotic Processes • Processing of Large Data Sets for Enhanced Analysis • Parameter Optimization and Measurement
This book systematically reviews the most important findings on cancer immune checkpoints, sharing essential insights into this rapidly evolving yet largely unexplored research topic. The past decade has seen major advances in cancer immune checkpoint therapy, which has demonstrated impressive clinical benefits. The family of checkpoints for mediating cancer immune evasion now includes CTLA-4, PD-1/PD-L1, CD27/CD70, FGL-1/LAG-3, Siglec-15, VISTA (PD-1L)/VSIG3, CD47/SIRPA, APOE/LILRB4, TIGIT, and many others. Despite these strides, most patients do not show lasting remission, and some cancers have been completely resistant to the therapy. The potentially lethal adverse effects of checkpoint blockade represent another major challenge, the mechanisms of which remain poorly understood. Compared to the cancer signaling pathways, such as p53 and Ras, mechanistic studies on immune checkpoint pathways are still in their infancy. To improve the responses to checkpoint blockade therapy and limit the adverse effects, it is essential to understand the molecular regulation of checkpoint molecules in both malignant and healthy cells/tissues. This book begins with an introduction to immune checkpoint therapy and its challenges, and subsequently describes the regulation of checkpoints at different levels. In closing, it discusses recent therapeutic developments based on mechanistic findings, and outlines goals for future translational studies. The book offers a valuable resource for researchers in the cancer immunotherapy field, helping to form a roadmap for checkpoint regulation and develop safer and more effective immunotherapies.
Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of dat
This book provides readers an extensive overview of recent progress in basic and clinical research on cancer immunotherapy. Thanks to rapid advances in molecular biology and immunology, it has become increasingly evident that cancer growth is influenced by host immune responses. With the success of a number of clinical trials, immunotherapy has become a promising treatment modality of cancer. This book covers five major topics, including monoclonal antibodies, biological response modifiers, cancer vaccines, adoptive cellular therapy and oncolytic viruses. It also examines the combination of different immune strategies as well as the combination of immunotherapy with other treatments to increase anti-tumor effects. Through the comprehensive discussion of the topic, the book sheds valuable new light on the treatment of tumors.