Cancer, Complexity, Computation (Emergence, Complexity and Computation, 46)

✍ Scribed by Igor Balaz (editor), Andrew Adamatzky (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 349
• Category: Library

✦ Synopsis

This book presents unique compendium of groundbreaking ideas where scientists from many different backgrounds are united in their interest in interdisciplinary approaches towards origins and development of cancers, innovative ways of searching for cancer treatment and the role of cancer in the evolution. Chapters give an unequivocal slice of all areas that relate to a quest for understanding cancer and its origin as many-fold nonlinear system, complexity of the cancer developments, a search for cancer treatment using artificial intelligence and evolutionary optimisation, novel modelling techniques, molecular origin of cancer, the role of cancer in evolution of species, interpretation of cancer in terms of artificial life and artificial immune systems, swarm intelligence, cellular automata, computational systems biology, genetic networks, cellular computing, validation through in vitro/vivo tumour models and tumour on chip devices. The book is an inspiring blend of theoretical and experimental results, concepts and paradigms.

Distinctive features

The book advances widely popular topics of cancer origin, treatment and understanding of its progress

The book is comprised of unique chapters written by world top experts in theoretical and applied oncology, complexity theory, mathematics, computer science.

The book illustrates attractive examples of mathematical and computer models and experimental setups.

✦ Table of Contents

Preface
Contents
What Cancer Is
1 Introduction
2 Cancer’s Essence and Ground
3 Cancer Hallmarks as Contingent Properties
4 Cancer’s Broken Symmetry
5 Cancer’s Intelligence
6 Conclusion
References
Complementarity, Complexity and the Fokker–Planck Equation; from the Microscale Quantum Stochastic Events to Fractal Dynamics of Cancer
1 Introduction
2 From Quantum Events to Phenotype Transformation
3 The Quantitative Measures of Biological Tumour Agressiveness
4 The Weibull Distribution of Cancer Incidence
5 Cell as a Complex Supramolecular Dynamic System
6 The Unique Interactions in Supramolecular Cellular System: Quantum Entanglement, Tunneling, Coherence and Chirality
7 Complementarity
8 Fractal-Probabilistic Dualism and Fractal Time–space
9 Appendix
9.1 The Chapman–Kolmogorov Approach
9.2 The Markovian Model of Tumour Growth
9.3 Fractal Time–Space
9.4 Entropy and Sigmoidal Dynamics
9.5 The Fibonacci Constant and Self-Organization of Cells
References
Quantitative In Vivo Imaging to Enable Tumour Forecasting and Treatment Optimization
1 Introduction to Tumour Forecasting
2 Relevant Data Types from Medical Imaging
2.1 Diffusion Weighted Magnetic Resonance Imaging
2.2 Dynamic Contrast-Enhanced Magnetic Resonance Imaging
2.3 Molecular Imaging with Positron Emission Tomography
3 Image-Based Mathematical Models of Cancer
3.1 Baseline Tumour Growth Models
3.2 Mechanically-Coupled Models
3.3 Vasculature-Coupled Models
3.4 Radiotherapy
3.5 Chemotherapy
4 Computational Methods to Solve Image-Based Cancer Models
4.1 The Finite Difference Method
4.2 Finite Element Analysis and Isogeometric Analysis
5 Calibrating Image-Based Mathematical Oncology Models
5.1 Inverse Problems for Oncology Models
5.2 Adjoint Methods for Inverse Problems
6 Model Selection and Identification of Relevant Parameters
6.1 Variance-Based Sensitivity Analysis
6.2 Model Calibration
6.3 Model Selection Criteria
6.4 The Occam Plausibility Algorithm
7 Towards the Optimization of Personalized Treatment Plans
7.1 Potential to Select Treatment Plans for Individual Patients
7.2 Optimal Control Theory for Personalized Treatment Planning
8 Barriers to Success
9 Conclusion
References
The Effect of Over-Feeding in a Computational Model of Tumour Growth
1 Introduction
2 Methods
3 Results
3.1 Malignant Cell Growth
4 Cytotoxic Treatment
5 Cytotoxic Treatment + Nutrient Reduction (NR)
6 Cytotoxic Treatment + Tolerance Normalisation (TN)
7 Cytotoxic Treatment + Nutrient Reduction + Tolerance Normalisation (NR-TN)
8 Discussion
References
Modeling Tumour Growth with a Modulated Game of Life Cellular Automaton Under Global Coupling
1 Introduction
2 Model
2.1 Lattice and States
2.2 Relation to Conway's Game of Life
2.3 Mean-Field Approximation
2.4 Initial Conditions for Simulations
3 Results and Discussion
3.1 Spatiotemporal Dynamics in the Absence of Global Coupling (γ=0)
3.2 Spatiotemporal Dynamics in the Presence of Global Coupling (γ> 0)
4 Conclusions
References
Pinning Control to Regulate Cellular Response in Cancer for the p53-Mdm2 Genetic Regulatory Network
1 Introduction
2 Methods
2.1 The p53-Mdm2 Genetic Regulatory Network
2.2 Mathematical Description
2.3 Pinning Control Methodology
2.4 p53, p300, and HDAC1 as Pinned Nodes
3 Results
3.1 Behaviors of the p53-Mdm2 Genetic Regulatory Network Without Control Action
3.2 Behaviors of the p53-Mdm2 Genetic Regulatory Network with Control Action
4 Discussion
4.1 Behaviors Induced Without Control Action
4.2 Behaviors Induced by the Pinning Control Technique
5 Conclusions
References
Heterogeneous Tumour Modeling Using PhysiCell and Its Implications in Precision Medicine
1 Introduction
1.1 Precision Medicine—General Introduction
1.2 Treatment Approaches in Precision Medicine
1.3 The Role of Microenvironment in the Future Development of Precision Medicine
1.4 Modelling in Precision Medicine
2 PhysiCell Based Simulator for Precision Medicine
2.1 Functionalities and Features
2.2 EvoNano PhysiCell Implementation
3 Results
4 Conclusions
References
Local Quantitative and Qualitative Sensitivity Analysis of CSC Dynamical Simulation
1 Introduction
2 Methodology
2.1 Model Description
2.2 Sensitivity Tests Design
2.3 Model Calibration and Tumour Growth Simulation
3 Results and Discussion
3.1 Sensitivity Tests
3.2 Tumour Growth Simulation
4 Conclusion
References
The Role of Molecular Dynamics Simulations in Multiscale Modeling of Nanocarriers for Cancer Treatment
1 Introduction
1.1 Role of Nanocarriers in Anticancer Treatment
1.2 Issues with a Nanocarrier Design
2 Multiscale Problem Requires Multiscale Solution
2.1 Tissue Scale—Generating a Virtual Tumour
2.2 Cell Scale
3 Molecular Dynamics
3.1 Atomistic Molecular Dynamics Simulations
3.2 Practical Aspects
3.3 Data Analysis
4 How Is It All Connected?—A Short Summary
5 Conclusion
References
A Haploid-Diploid Evolutionary Algorithm Optimizing Nanoparticle Based Cancer Treatments
1 Introduction
2 Eukaryotic Evolution and the Baldwin Effect
3 The NK Model
4 A Simple Haploid-Diploid Algorithm
5 PhysiCell: A Physics-based Multicellular Simulator
6 Results of HDEA Optimization on PhysiCell
7 Conclusion
References
Drawbacks of Bench to Bed Translation of Nanomedicines for Cancer Treatment
1 State of the Art
2 Cancer Biology
3 Biological Barriers
4 Immune Reaction
5 Targeting
6 Cost, Regulation, Scale Up
7 Future Perspectives of Nanomedicine for Cancer Treatment
References
Swarms: The Next Frontier for Cancer Nanomedicine
1 Cancer Nanomedicine
1.1 Cancer
1.2 Nanomedicine
1.3 Current State of the Art (Research and the Clinic)
2 Future Nanomedicines
2.1 Nanobots
2.2 Nanobot Design
2.3 Nanoswarms
2.4 Personalization
3 Challenges in the Clinical Pathway
3.1 Definitions
3.2 Size
3.3 Toxicity
3.4 Device or Drug
4 Ethical and Regulatory Aspects of Nanoswarms
4.1 Ethics
4.2 Towards a Regulatory Framework
References
Study of Tumour Induced Vessel Displacement in the Tumour Progression Rate with Advanced Bioinspired Computational Tools
1 Introduction
2 Cellular Automata and Modeling Aspects
3 Cellular Automata Model of Tumour Growth and Tumour-Induced Vessel Displacement
4 Simulation Results of Tumour Growth and Tissue Displacement
5 Conclusions
References
Complexities of Drug Resistance in Cancer: An Overview of Strategies and Mathematical Models
1 Introduction
2 The Problem of Drug Resistance
3 Mechanisms of Drug Resistance
4 Differential Equation Models Used to Represent Cellular Heterogeneity
5 Stochastic Models
6 Methods to Overcome Multidrug Resistance
7 Conclusions
References
The Immune System in Health and Disease: The Need for Personalised Longitudinal Monitoring
1 Introduction
2 The Vital Role of the Immune System in Health and Disease
3 Monitoring the Immune System: Challenges
4 Monitoring the Immune System: Opportunities
5 Longitudinal Analysis and Causal Pattern
6 From Dynamic Cellomics to Multidimensional Omics
7 From Statistical AI to AGI for Causation in Medicine
8 Algorithmic Information Dynamics
9 Conclusion
References
The Immune System in Health and Disease: The Need for Personalised Longitudinal Monitoring
1 Introduction
2 The Vital Role of the Immune System in Health and Disease
3 Monitoring the Immune System: Challenges
4 Monitoring the Immune System: Opportunities
5 Longitudinal Analysis and Causal Pattern
6 From Dynamic Cellomics to Multidimensional Omics
7 From Statistical AI to AGI for Causation in Medicine
8 Algorithmic Information Dynamics
9 Conclusion
References
Correction to: The Immune System in Health and Disease: The Need for Personalised Longitudinal Monitoring
Correction to: Chapter “The Immune System in Health and Disease: The Need for Personalised Longitudinal Monitoring” in: I. Balaz and A. Adamatzky (eds.), Cancer, Complexity, Computation, Emergence, Complexity and Computation 46, https://doi.org/10.1007/978-3-031-04379-615