دانلود کتاب مدل سازی و رویکردهای محاسباتی برای پدیده های چند مقیاسی در تحقیقات سرطان
Modelling and Computational Approaches for Multi-Scale Phenomena in Cancer Research, 1ed

Cancer development and progression is the result of biological phenomena that occur across multiple temporal and spatial scales. Recent years have seen a flurry of multi-scale mathematical models developed to generate and test new biological hypotheses related to cancer development, progression, and various treatment approaches. This has led to the development of new computational and analytical approaches aimed at investigating these multi-scale models.

This review volume summarises the state of the art related to the modelling, experimental investigation, and data assimilation of multi-scale phenomena during cancer development, evolution, and treatment, as well as the computational and analytical investigation of the multi-scale models developed to reproduce the biological phenomena. The book also identifies the experimental and theoretical open problems that will have to be addressed in the near future in order to advance this field. Modelling and Computational Approaches for Multi-Scale Phenomena in Cancer Research is an excellent resource for both early career and advanced researchers.

Contents

۱. Introduction: Brief Review of Multi-Scale Aspects in Cancer
۲. Using Mathematical Models to Predict Glioblastoma Growth and Response to Treatment in a Patient-Specific Manner
۳. Multi-Scale Aspects of Immune Responses to Solid Cancers: Mathematical and Computational Modelling Perspectives
۴. From Cancer Evolution to Treatment: Viscoelastic Aspects of Solid Cancers
۵. Modelling Cell Migration in Cancer Spread as a Response to Multi-Cue Heterogeneous Environments: A Kinetic Approach
۶. Multi-Scale Mathematical Modelling and Simulations of Chemo-Viro-Therapies for Cancer
۷. Spatio-Temporal-Structural Approaches in Cancer and Immunity Focussed on Oncolytic Viral Therapies
۸. Malignant Transformation of Low-Grade Astrocytomas through a Mechano-Biological Triggering Hypothesis: Imaging-Informed Modelling
۹. Brain Tumour Evolution Backwards in Time via Reaction–Diffusion Models and Sobolev Regularisat