Cancer, Complexity, Computation

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