Preface
Contents
About the Editors
Part I: Overview
Chapter 1: An Overview of the Translational Dilemma and the Need for Model-Based Precision Medicine
Introduction
Progress in Translational Systems Biology of Inflammation
Challenges and Future Perspectives
References
Part II: Computational Modeling Methods and Biomedical Applications
Chapter 2: Translational Equation-Based Modeling
Equation-Based Models of Biological Systems
Historical Perspective
Types of Equation-Based Models
Advantages of Equation-Based Models
Disadvantages of Equation-Based Models
Models Big and Small
Validating Equation-Based Models
Using Equation-Based Models as a Prediction Tool
Parameter Estimation and the Inverse Problem
Approaches to Solving the Ill-Posed Inverse Problem
Hybrid Models
Translational Applications
The Interdisciplinary Perspective
Enhancing Current Trial Design
In Silico Clinical Trials
Parameter Ensembles vs. Data: Different Worldviews
Novel Approaches to Personalized Therapies for the Critically Ill
Conclusion
References
Chapter 3: Agent-Based Modeling in Translational Systems Biology
The Translational Dilemma and the Need for Dynamic Knowledge Representation
Dynamic Knowledge Representation with Agent-Based Modeling
Related Modeling Methods
Agent-Based Models Versus Multiagent Systems
Properties of Agent-Based Models
Representation of Spatial Relationships
Representation of Parallelism and Concurrency
Incorporation of Stochasticity and Randomness
Modular Architecture
Generation of Nonintuitive System-Level Phenomenon
Readily Facilitates Useful and Detailed Abstraction
Tools for Agent-Based Modeling
Agent-Based Modeling of Inflammation
ABMs of Inflammation-Related Intracellular Processes
Cell-Level ABMs of Systemic Inflammation and Simulated Trials for Sepsis
ABMs of Multiorgan Inflammation and Failure
Moving Forward: Scaling Dynamic Knowledge Representation, the Agent-Based Modeling Format (ABMF)
Challenges to the Use of Agent-Based Modeling
Conclusion
References
Chapter 4: Integrating Data-Driven and Mechanistic Models of the Inflammatory Response in Sepsis and Trauma
Introduction
A Systems Approach to Inflammation
Data-Driven (Correlative) Approaches to Dynamic Inflammation Data
Dynamic, Mechanistic Modeling of Inflammation
Combining Data-Driven and Mechanistic Modeling of Inflammation
Conclusions and Future Prospects
References
Chapter 5: Therapeutics as Control: Model-Based Control Discovery for Sepsis
Introduction
Model-Based Control Discovery: Overcoming Limitations of Current Biomedical Research
Sepsis as a Control Problem
Insights from Model Predictive Control of Sepsis
Model-Based Control Discovery Using Agent-Based Models
Discussion
References
Part III: Translational Modeling of Sepsis and Trauma
Chapter 6: Disorder of Systemic Inflammation in Sepsis and Trauma: A Systems Perspective
Introduction
Sensing Mechanisms
Infection
Tissue Damage
Hypoxia/Ischemia
Cellular Factors of the Inflammatory Response
Effectors of the Inflammatory Response
ROS & RNS
Coagulation Cascade
Neuroendocrine
Cytokines and Chemokines
Complement
Consequences of the Inflammatory Response
Derangements of Systemic Inflammation
Excessive Inflammation from Severe Injury
Immunosuppression
Apoptosis
Th1 to Th2 Conversion
Immune Response with Age
Treatment Considerations
Mitigating the Hyperinflammatory Response
Reversing Immunosuppression
Conclusion
References
Chapter 7: Multiscale Equation-Based Models: Insights for Inflammation and Physiological Variability
Introduction
Multiscale Modeling of Human Endotoxemia
Immune Cells
Identification of Key Transcriptional Responses
Indirect Response Modeling
Central Control of Immunomodulatory Hormones
Circadian Rhythms
Ultradian Rhythms
Heart Rate and Heart Rate Variability
Autonomic Origins of Heart Rate Variability
Discrete-Continuous Modeling
Challenges in Translational Modeling of Heart Rate Variability in Endotoxemia
Conclusions
References
Chapter 8: In Silico Trials and Personalized Therapy for Sepsis and Trauma
Inflammatory Diseases: A Pox on All Our Houses
Insufficiencies in the Current Process of Drug/Device Design and Executing Clinical Trials
Inflammation in Critical Illness: Rational Systems Approaches for a Complex Therapeutic Target
Dynamic Knowledge Representation in the Context of In Silico Clinical Trials
Dynamic Knowledge Representation at the Individual Level: Optimization of Diagnosis and Therapy
Conclusions and Perspectives
References
Chapter 9: Computational Modeling of the Coagulation Response During Trauma
Introduction
Multiscale Modeling of Bleeding During Trauma
Coagulation Modeling
Coagulation During Bleeding
Data-Driven Development of Subject-Specific Platelet Function Profiles
Conclusion
References
Part IV: Translational Modeling of Organ/Tissue Specific Inflammatory Disease Processes
Chapter 10: Disorders of Localized Inflammation in Wound Healing
Introduction
Hemostasis
Inflammation
Epithelialization
Angiogenesis
Provisional Matrix Formation
Remodeling
Conclusion
References
Chapter 11: Equation-Based Models of Wound Healing and Collective Cell Migration
Introduction
Modeling
ODE Models
PDE Models
Agent-Based Models of Cell Migration
Applications of Wound Healing Models
Conclusion
References
Chapter 12: Agent-Based Modeling of Wound Healing: Examples for Basic and Translational Research
Introduction
Wound Healing and Inflammation
Agent-Based Modeling
Agent-Based Modeling of Wound Healing
Agent-Based Modeling for Basic Science Knowledge Integration: An ABM of Epithelial Restitution
Model Construction and Overall Architecture
Model Calibration: System-Level Dynamics
Simulation Experiments
Agent-Based Modeling as a ClinicalâTranslational Aid: An ABM of Pressure Ulcer Formation in Spinal Cord Injury Patients
Model Architecture
I/R Mechanism: Implementation and Validation
Inflammation Mechanism: Implementation and Validation
Sensitivity Analysis and In Silico Trials
Discussion and Conclusions
References
Chapter 13: Multiscale and Tissue Realistic Translational Modeling of Gut Inflammation
Introduction
The Spatially Explicit General-Purpose Model of Enteric Tissue (SEGMEnT)
General Description
Behaviors: Calibration and Validation
Anatomic Scale: Whole Organ Simulation with SEGMEnT_HPC
Challenges in Anatomic Scale Modeling: Buffering
Challenges in Anatomic Scale Modeling: Load Balancing
Conclusion
References
Chapter 14: Data-Driven Modeling of Liver Injury, Inflammation, and Fibrosis
Introduction
Data-Driven Modeling: Clinical Insights into Pediatric Acute Liver Failure
Data-Driven Modeling: Tissue-Scale Insights in Liver Tissue Preservation
Data-Driven Modeling: Cellular-Scale Insights into Hypoxia, Ischemia, and Hemorrhagic Shock
Traversing In Vitro and Clinical Data Using Computational Modeling
Conclusions and Future Prospects
References
Chapter 15: Temporal and Spatial Analyses of TB Granulomas to Predict Long-Term Outcomes
Introduction
Methods
GranSim: AÂ Hybrid Agent-Based Model
Generating a Repository of Simulated Granulomas
Immunohistochemical Staining of NHP Tissue Samples
Using GIS to Identify Locations of T Cells and Macrophages in an IHC Image of a Granuloma
Methods for Classification and Analysis of Granulomas
Temporal Classification
Spatial Analysis
Prediction of Future Temporal Behavior from Spatial Structure Analysis
Results
Temporal Classification of Simulated Granulomas Based on CFU and Lesion Size
Spatial Analysis of Simulated and Experimental Granulomas
Spatial Structure Predicts Future Temporal Granuloma Stability
Identifying Spatial Characteristics that Correlate with Granuloma Severity
Discussion
References
Part V: Future Perspectives: Translation to Implementation
Chapter 16: The Rationale and Implementation of Model-Based Precision Medicine for Inflammatory Diseases
References
Index