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Immune system modelling and simulation / Filippo Castiglione and Franco Celada. -- Boca Raton : CRC Press, Taylor & Francis Group, c2015. – (63.1705/C351) |
Contents
Preface
Acknowledgments
1. Immunology for Aliens
1.1 Leo Szilard paradox: Grand Central Terminal
1.2 Non-specific defence
1.3 Levels of evolution
1.4 How is a trait selected if it was not needed?
1.5 Luck or foresight?
1.6 Blood brothers
1.7 The members of the winning team are rewarded
1.8 Somatic recombination produces diversity among equals
1.9 The workshop of the adaptive response
1.10 Repertoires
1.11 A battalion of one
1.12 Self-inflicted damage threatens survival
1.13 Don't think, but reason and understand
1.14 Full cover comes at a cost
1.15 A bet, not a fantasy, about earliest happenings
1.16 Cooperation among very different cells
multiplies the number of jobs that can be done
1.17 Thymus deeds
1.18 Hypothesis is a bet: Tips for the Aliens
1.19 To grow and to change
1.20 Philosophy storm
1.21 Suicide
1.23 The linear transmission of activation
1.24 Recipe for complete Freund Adjuvant
1.25 An antibody is an antibody (or two?)
1.26 Scientific Myths: A god interferes with human cognition
1.28 Scientific Myths 2: Apollo roams about the
hills of Tennessee
1.29 Plain reasoning breaks the Spell
1.30 Of wolves, dogs and shepherds
1.31 How to block anti-self
1.32 Controlling self-damage needs structures: the lymphoid
organs
1.33 Time of strengthening
1.34 Maturity
1.36 Cellular Automaton makes sense
1.37 Memory is in the numbers
1.38 The power rests with whoever issues regulatory
signals
1.39 The seed is prepared during the primary
response
1.40 The challenge
1.41 Competition in the minefield of cross-reactions
1.42 The compensated immune system wants to understand
1.43 The two immune systems
1.44 The cellular branch confronts a virus
infection
1.45 How to model the cellular and the humoral
together
1.46 Body and soul, actions and philosophy
1.47 The selective modeller
1.48 Attrition and cross-reaction
1.49 Memory is strong, sometimes too strong
1.50 IMMSIM reveals MaN (Memory anti Naive) as a cause
of aging
1.53 Protein Conformation and the immune system
1.54 Antibody-mediated activation of a mutant enzyme
1.55 More fields for models, and where to find them
1.57 Modelling to learn immunology
1.58 An unconventional glossary for aliens
2. Aliens for Immunology
2.1 The unusual mix of immunology and computer
science
2.2 Classical modeling techniques versus the 'new
kind of science'
2.3 From spin-like to agent-based models
2.4 The ancestors
2.5 The others
3. C-ImmSim Unveiled
3.1 Model compartments
3.2 The cells
3.3 The molecules
3.4 The repertoire
3.5 The molecular affinity
3.6 Reshaping the affinity landscape
3.7 Haematopoiesis and cell homeostasis
3.8 The selection of ceils in the thymus
3.9 The Hayflick limit
3.10 Cell aging and death
3.11 A (dynamic) immune memory
3.12 The hyper-mutation of antibodies
3.13 Immune activation
3.14 Anergy
3.15 Interactions among entities
3.16 Antigen digestion and presentation
3.17 Cell motion and diffusion of molecules
3.18 Main procedures
3.19 Notes on the expressed repertoire
3.20 Scaling the system size
3.22 The choice of the MHC molecules
3.23 The parameters
3.24 What to monitor?
4. Benchmarks, aka Qualitative Model Validation
4.1 Primary and secondary response
4.2 Exhaustion
4.3 Bacterial Infection
4.4 Viral infection
4.5 Modelling idiotypes and the idiotype network
5. Specific Applications
5.1 A multi-scale approach to model
hypersensitivity
5.2 HIV infection and AIDS
5.3 Immunodominance in cancer immunotherapies
5.4 Embedding immunoinformatics predictions
6. One Last Word
7. Bibliography
Subject Index
Index of Persons Named
Colour Plate Section
