Systems Biology and In-Depth Applications for Unlocking Diseases

Principles, Tools, and Application to Disease

1st Edition - November 5, 2024
Imprint: Academic Press
Editor: Babak Sokouti
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 2 3 2 6 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 2 3 2 7 - 3

Systems Biology and In-Depth Applications for Unlocking Diseases provides the essence of systems biology approaches in a practical manner illustrating the basic principles essen… Read more

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Systems Biology and In-Depth Applications for Unlocking Diseases provides the essence of systems biology approaches in a practical manner illustrating the basic principles essential to develop and model in real life science applications. Methodologies covered show how to interrogate biological data, with the purpose of obtaining insight about disease diagnosis, prognosis, and treatment.

Systematically written in 4 parts, this book first provides an introduction and history of systems biology; second, it provides the tools and resources needed for the structure and function of biological systems; next, it provides the evidence of systems biology in action to better understand disease connections; and finally, it provides the extensions of systems biology in various scientific fields including pharmacology, immunology, vaccinology, neuroscience, virology, and medicine. Examples include big data techniques, scale networks, mathematical model development, and much more.

This is the perfect reference to provide the fundamental base of knowledge needed for systems biologists, professionals in systems medicine, computational biologists, and bioinformaticians, whether needed for immediate application or for building a comprehensive understanding of the field.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
Foreword
Preface
Part I. Introduction
Chapter 1. Systems biology at a glance
1 Introduction
2 Importance of system biology
3 Properties of system biology
3.1 System structure
3.2 System dynamics
3.3 Control method
3.4 Design method
4 Approaches of system biology
4.1 Computational biology-based analysis
4.2 Robustness of system biology
4.3 Metabolic engineering in system biology
4.4 Drug-designing based analysis
5 Futuristic approach
6 Conclusion
Chapter 2. Brief information on cellular biology and signaling pathways
1 Introduction
2 Mediators for cell signaling
2.1 Signaling molecules
2.1.1 Small molecules and gases
2.1.2 Peptide hormones
2.1.3 Steroid hormones
2.1.4 Cytokines
2.1.5 Neurotransmitters
2.2 Receptors
2.2.1 Intracellular receptors
2.2.2 Cell-surface receptors
2.2.3 Enzyme-linked receptors
2.2.4 Ligand-gated ion channels
3 Types of cell signaling
3.1 Autocrine signaling
3.2 Endocrine signaling
3.3 Paracrine signaling
3.4 Juxtacrine signaling
4 Cell signaling pathways
4.1 Intracellular pathways
4.1.1 cAMP pathway
4.1.2 MAPK pathway
4.1.3 JAK/STAT pathway
4.2 Intercellular signaling pathways
4.2.1 NOTCH signaling pathway
4.2.2 Wnt signaling pathways
5 Systems biology and cell signaling
5.1 What is systems biology
5.2 Experimental methodologies
5.2.1 Cell signaling databases
5.2.2 Computational methodologies
5.2.3 Modeling signaling pathways
5.2.4 Network biology
6 Conclusions
Chapter 3. The multiscale causality nature of human cancer: A systemic approach
1 Introduction
2 What is a system?
3 Isolated, closed, and open systems
4 The open system in action
5 System regulation in action
6 The components of a living system
7 The dynamics of open systems
8 Cancer as a complex dynamical system
9 The multiscale causality of cancer
10 Conclusions
Chapter 4. From sketch to landscape: Transforming neuronal concepts across technological change
1 Introduction
2 Neurons across technological change
2.1 The birth of neurology at the dawn of the scientific revolution
2.1.1 Scientific context: Empirical inquiries in the scientific revolution
2.1.2 Technological innovations: Anatomical theaters and the first microscopes
2.1.3 Scientific breakthroughs: An anatomical approach to brain function
2.1.4 Neuronal concepts: A novel name for brain studies is born
2.2 Nerves at the microscale and the body as a machine
2.2.1 Scientific context: Empty chambers and minute creatures
2.2.2 Technological innovations: Beyond the naked eye
2.2.3 Scientific breakthroughs: Neuroanatomy at the microscale
2.2.4 Neuronal concepts: Microanatomical insights and mechanistic hypotheses
2.3 The reticular theory: How the nervous system eludes the cell theory
2.3.1 Scientific context: Building theories of biology
2.3.2 Technological innovations: Achromatic lenses, oil-immersions, and colored dyes
2.3.3 Scientific breakthroughs: The validation of cell theory and the electrical nature of impulses
2.3.4 Neuronal concepts: Contrasting findings on the reticular theory
2.4 The neuron doctrine: Neurons as individual cells
2.4.1 Scientific context: The foundations of modern biology
2.4.2 Technological innovations: A black dye illuminating the nature of neurons
2.4.3 Scientific breakthroughs: Individual neurons, multiple synapses
2.4.4 Neuronal concepts: Neurons as the building blocks of the nervous system
2.5 Networks of interconnected neuronal cells
2.5.1 Scientific context: Decoding the secret of genes
2.5.2 Technological innovations: Microscopy and electrophysiology to unravel cellular processes
2.5.3 Scientific breakthroughs: Synaptic connections and information processing in neuronal networks
2.5.4 Neuronal concepts: Neuronal networks processing information
2.6 Neuronal heterogeneity and emergent behaviors
2.6.1 Scientific context: Systems biology harmonizes holism and reductionism
2.6.2 Technological innovations: Imaging and electrophysiology for systems neuroscience
2.6.3 Scientific breakthroughs: Unveiling neuronal heterogeneity
2.6.4 Neuronal concepts: Heterogeneity of roles in neuronal networks
2.7 A data-driven dynamic neuronal landscape
2.7.1 Scientific context: The rise of computational systems biology
2.7.2 Technological innovations: High-throughput data and computational methods
2.7.3 Scientific breakthroughs: Data integration unlocks joint analysis of structural and functional properties of neurons
2.7.4 Neuronal concepts: A dynamic landscape of neuronal types and states
3 Conclusions
Chapter 5. Challenges in evolutionary computing in the context of integrated bioinformatics
1 Introduction
2 Expectation–maximization algorithm
2.1 EM algorithm in biology
2.2 Variations of EM algorithms
3 Gene regulatory network
3.1 GRNs are also used for spatial domain analyses
3.2 Development of new genetic algorithms that are reinstantiable
3.3 Genotype–phenotype correlation
3.4 Scalability for computing
Chapter 6. Kinetics and dynamics of biological systems
1 Introduction
2 Fundamentals of kinetics and dynamics
3 Mathematical modeling of biological systems
4 Experimental techniques for studying kinetics and dynamics
5 Molecular simulation and computational approaches
6 Applications of kinetics and dynamics in biological systems
7 Conclusion and future directions
Part II. Tools and resources
Chapter 7. Biological data sources for advancements in systems biology
1 Introduction
2 Biological data and resources
2.1 Genomic database
2.2 Transcriptomics database
2.3 Proteomics database
2.4 Metabolomics database
2.5 Biological pathway database
3 Challenges in biological data sources
3.1 Biological data volume
3.2 Handling unstructured and structured biological data
3.3 Data heterogeneity
3.4 Data quality
3.5 Data accessibility
3.6 Data integration
3.7 Data analysis
4 Future directions
5 Conclusion
Chapter 8. High-throughput data analysis in systems biology: Techniques, challenges, and applications in modern scientific research
1 Introduction
2 Types of high throughput data in system biology
2.1 Sequences data
2.2 Molecular structure data
2.3 Gene expression data
2.4 Binding sites and domain data
2.5 Protein–protein interaction
2.6 Mass spectrometry data
2.7 Metabolic pathway data
3 Types of high-throughput technologies based high-throughput data
3.1 Sequencing-Based Omics
3.1.1 Genomics
3.1.2 Transcriptomics
3.1.3 Epigenomics
3.1.4 Epitranscriptomics
3.2 MS-based omics
3.2.1 Proteomics
3.2.2 Metabolomics
4 Challenges in handling and analyzing high-throughput data
4.1 Volume and complexity of data
4.2 Storage challenges
4.3 Big data managing
4.4 Challenges in the integration of omics and non-omics data (OnO)
4.5 High-throughput data and result analysis
5 Applications of high-throughput data
5.1 Personalized medicine
5.2 Proteomics
5.3 Gene sequencing
5.4 Healthcare
5.5 Drug discovery
5.6 Translational research
6 Future aspects of high-throughput data in systems biology
7 Conclusion
Chapter 9. High throughput data: Single-nucleotide polymorphisms in depth
1 Introduction
2 Insights into the SNP
2.1 Causes of SNP
2.2 Types of SNP
2.3 Clinical implications of SNP
3 Detection of SNP
4 SNP discovery through bioinformatics approach
5 Databases for SNP mining
5.1 Animal-SNPAtlas
5.2 BioMart
5.3 dbSNP
5.4 Drug-SNPing
5.5 gnomAD
5.6 HapMap
5.7 HGBASE
5.8 JSNP
5.9 MITOMAP
5.10 SEQtools
5.11 SGVP
6 SNP related in silico tools and implications
6.1 Sequence based tools
6.1.1 Meta-SNP
6.1.2 Mutation assessor
6.1.3 PhD-SNP
6.1.4 PolyBayes
6.1.5 PolyPhen v2
6.1.6 PolyPhred
6.1.7 PredictSNP2
6.1.8 PROVEAN
6.1.9 SIFT
6.1.10 SNAP2
6.1.11 SNiPlay
6.1.12 SNPnexus
6.1.13 SNPRanker 2.0
6.1.14 SNPs&GO
6.1.15 UMD-Predictor
6.2 Structure-based tools
6.2.1 CUPSAT
6.2.2 DUET
6.2.3 I-Mutant
6.2.4 MUpro
6.2.5 INPS-MD
6.2.6 PoPMuSiC
6.2.7 HoTMuSiC
6.2.8 SNPMuSiC
7 Limitations of computational SNP discovery
8 Conclusion
Chapter 10. The significance and evolution of biological databases in systems biology
1 Historical perspective of biological databases
2 Architecture and design considerations for modern databases
2.1 Database types
2.2 Database models
2.3 Schema design for biological data
3 The role of databases in integrating multiomics data
4 Challenges faced in database management and potential solutions
4.1 Data quality
4.2 Integration, interoperation, and federation are fundamental elements throughout diverse academic disciplines
4.3 The subject matter under consideration belongs to the domains of ontologies and semantic definitions
4.4 Ensuring robust security measures, safeguarding privacy, and implementing ethical access rules
4.5 The incorporation of data analysis tools into databases
5 Future directions, including the role of machine learning and artificial intelligence in database querying and data integration
Chapter 11. Programming, tools, and software
1 Introduction
2 Computer-aided drug design
3 Target protein selection
4 Determination of protein model
5 Homology modeling
6 Secondary structure prediction
7 Protein structure validation tools
8 Identification of active sites
9 Molecular dynamics simulation
10 Molecular docking
11 Conclusion
Part III. Systems biology in action — to understand diseases
Chapter 12. Big data analysis techniques
1 Introduction
2 Big data in science and health
3 Omic studies related to big data
4 Big data analysis techniques related to disease management
4.1 Genomic sequencing
4.2 Electronic health records
4.3 Machine learning and predictive analytics
4.4 Natural Language Processing
4.5 Image processing
4.6 Network analysis
5 Conclusions
Chapter 13. Reconstruction of genomic and proteomic scale network structures and functions
1 Introduction
2 Computational tools and algorithms
2.1 Network reconstruction methods
2.1.1 Correlation-based approach
2.1.2 Bayesian network
2.1.3 Machine-learning models
2.2 Integration of genomic and proteomic data
3 Genome-scale network reconstruction
3.1 Gene regulatory networks
3.2 Protein–protein interaction networks
3.3 Metabolic networks
3.4 Signaling pathway networks
4 Proteomic scale network analysis
4.1 Protein interaction dynamics
4.2 Network visualization and analysis tools
4.2.1 The force-directed layout
4.2.2 PageRank
4.2.3 The Louvain community detection algorithm
5 Functional annotations and biological significance
5.1 The process of network representation
5.2 The field of mathematics
5.2.1 The hypergeometric distribution
5.2.2 Hypergeometric distribution
5.2.3 Fisher's exact test
5.2.4 Graph theory
6 Challenges in reconstruction genome and proteomic scale network
6.1 Data integration challenges
6.2 Integrating heterogeneous data types
6.3 Integration of gene expression with genotype data
6.4 Integration of gene expression microarray studies
6.5 Computational and algorithmic challenges
7 Network interface and reconstruction
8 Future scope
9 Conclusion
Chapter 14. Validation strategies in systems biology research
1 Introduction
2 Validation strategies
3 Multiscale model validation
4 Case studies
4.1 Validating a model of MAPK signaling pathway
4.2 Validating microbiome community model
4.3 Integration of multiple validation approaches
5 Conclusion
Part IV. Applications of systems biology extensions in diseases
Chapter 15. Systems pharmacology – principles, methods and applications
1 History of systems pharmacology: Uncovering historical milestones
1.1 Evolution of drug discovery models: A comparison
1.2 The classical model: Traditional foundations
1.3 Systems biology model: A holistic revolution
1.4 Systems pharmacology model
2 Interplay between genomics, transcriptomics, proteomics, metabolomics, and systems pharmacology
3 Principles and methods in systems pharmacology
3.1 Network pharmacology
3.2 Systems biology
3.3 Pharmacokinetics and pharmacodynamics
3.4 Clinical trial simulation
4 Role of bioinformatics in systems pharmacology
5 Computer-aided drug design tools in systems pharmacology
5.1 Drug discovery and systems pharmacology
5.2 Computer-aided drug design (CADD)
5.3 Target identification and optimization
5.4 Future directions of CADD in systems pharmacology
6 Key computational tools
7 Integrated systems pharmacology: A stepwise example
8 Systems pharmacology applications in human diseases
Chapter 16. Systems immunology
1 Introduction
2 Networks and pathways
3 Computational modeling
4 Multiscale modeling
5 Multiomics data integration
6 Clinical and translational applications
7 Conclusion
Chapter 17. Systems virology
1 Introduction
2 Viral classification/taxonomy
3 Viral evolution
4 Viral genome structure composition
5 Pathogenicity/viral disease
6 Lifecycle
7 Therapeutic targets against virus
8 Conclusion
Chapter 18. Systems vaccinology
1 Introduction
2 Techniques related to systems vaccinology
2.1 Genomics
2.2 Reverse vaccinology and pan-genomics
2.3 Transcriptomics
2.4 Proteomics
2.5 Proteomics types
2.5.1 Expression proteomics
2.5.2 Structural proteomics
2.5.3 Functional proteomics
2.6 Miscellaneous techniques
3 Data analysis and bioinformatics
4 Application of systems vaccinology
5 Future directions for systems vaccinology
Chapter 19. Systems neuroscience
1 Background
2 Bioinformatics, systems biology and systems neuroscience
2.1 Data integration and hypothesis generation
2.2 Systems neuroscience and personalized medicine
3 Systems neuroscience: Key aspects
3.1 Neural circuits and systems
3.2 Bain mapping
3.3 Sensory and motor systems
3.4 Memory, learning, and management
3.5 Cognition and perception
3.6 Plasticity of the neural system
3.7 Brain disorders and computational neuroscience
4 Systems neuroscience and Alzheimer's disease
4.1 Neuroimaging and connectivity studies
4.2 Neural circuit dysfunction and neurotransmitter systems
4.3 Modeling and simulation
4.4 Biomarker discovery and drug development for AD
5 Systems neuroscience and other diseases
5.1 Parkinson's disease and schizophrenia
5.2 Epilepsy, depression, and anxiety disorders
5.3 Autism, stroke and traumatic brain injury (TBI)
5.4 Other neurodegenerative diseases (e.g., Huntington's disease, amyotrophic lateral sclerosis), addictions and neuropsychiatric disorders
6 Computational methods in systems neuroscience
6.1 Neural modeling and connectivity analysis
6.2 Electrophysiological and functional imaging analysis
6.3 Machine learning, deep learning, and data mining
6.4 Biophysical modeling, neural network simulations, and neuroinformatics
7 Artificial intelligence (AI) and systems neuroscience
7.1 Neural data analysis, pattern recognition and classification
7.2 Brain-computer interfaces (BCIs) and predictive modeling
7.3 Functional neuroimaging and synaptic connectivity mapping
7.4 Drug discovery and treatment options
8 Conclusion
Chapter 20. Implications of systems biology in understanding the pathophysiology of neurological diseases
1 Introduction
2 Application of system biology in understanding the pathophysiology of neurological diseases
2.1 Systems immunology
2.1.1 Interrelation between systems immunology and nervous system
2.1.2 Investigating neurological disorder mechanism
2.1.3 Experimental approaches of systems immunology
2.2 Systems microbiology
2.2.1 Investigating neurological disorders caused by microbiome
2.2.2 Diverse network of microbes in controlling human physiology
2.2.3 Impact of systems microbiology on neurological disorders
2.3 Systems vaccinology
2.3.1 To determine the efficacy of the vaccine
2.3.2 Interconnection of systems vaccinology and immune system
2.4 Systems pharmacology
2.4.1 Diverse network models utilized in the analysis of drug mechanisms
2.4.2 Experimental methodologies within the domain of systems pharmacology
2.4.3 Investigating disease mechanisms via regulatory networks
2.4.4 Investigating the connections between drug targets and regulatory networks
3 Challenges of system biology
4 The future aspect of system biology
Chapter 21. Systems medicine
1 Introduction
2 Defining systems medicine
3 Systems biomedicine
4 Molecular systems medicine
5 Personalized/precision medicine
6 Network medicine
7 From P3 to P7 systems medicine
7.1 P3 and P4 systems medicine
7.2 P5 and P6 systems medicine
7.3 P7 systems medicine
8 Conclusion
Chapter 22. From systems thinking to P4 medicine
1 Introduction
1.1 Linear reductionist biomedicine (LRM)
1.2 Clinical systems medicine
2 Global living systems theory
2.1 A brief history
2.2 Tenets of global living systems theory
2.2.1 Metabolism is the focus of life
2.2.2 The whole person is the basic unit of study
2.2.3 The endocrine system is the object of study
2.3 Clinical elements of global living systems theory
2.4 Construction of meta-markers
2.5 Construction of epi-markers
2.6 Treatment selection
3 Application of global living system theory to anxiety
3.1 Overview
3.2 Genetics: Prediction and participation
3.3 Epigenetics, personality, psychosocial, environment: Prevention and participation
3.4 Hormones: Prediction, personalization
3.5 Neurophysiology: Prediction, personalization
3.6 Immunity: Prediction, personalization
3.7 Microbiome: Prediction, participation, personalization
3.8 Nutrition: Prediction, participation, personalization
3.9 Lifestyle and environment: Prediction, participation, personalization
3.10 Biological modeling: Prediction, personalization
3.11 Treatment: Personalization, participation
3.12 Level: Microscopic: Genetics, Epigenetics
3.13 Level: Mesoscopic: Physiologic
3.13.1 Neurotransmitter regulation
3.13.2 Cortisol regulation
3.13.3 Immunity: Histamine and inflammation
3.13.4 Nutrition and microbiome
3.14 Macroscopic: Self and environment
3.14.1 Lifestyle and diet
3.14.2 Psychology and personality
3.15 Macroscopic: Self and networked relationships
4 Case study in anxiety
4.1 Chief complaint
4.2 Past medical history
4.3 Review of systems, nutrition, lifestyle, social and environmental
4.4 Summary
4.5 Laboratory studies
4.6 Intervention
4.7 Outcomes
5 Conclusions
Chapter 23. Systems biology of cancer
1 Introduction
2 Complex signaling networks
2.1 At genomic level
2.2 At protein and post-translational levels
2.3 At tissue level
3 Cancer systems biology
3.1 Systems biology to study intricate mechanism of carcinogenesis
3.2 Systems biology for cancer marker development
3.3 Systems biology approach for cancer therapeutic intervention
4 Challenges and prospects
5 Conclusion
Chapter 24. Repurposing, effects, design, and discovery of drugs in systems biology
1 Introduction
2 Systems pharmacology and drug adverse events
3 Signal transduction pathways and cross talks
4 Computational approaches in drug discovery
5 Network-based approaches in drug repositioning
6 Systems biology methods in drug discovery and translational biomedicine
7 Artificial intelligence in drug discovery
8 Systems biology methods for antiviral drug discovery
9 Identifying macromolecular targets and drug design
10 Conclusion
Chapter 25. Future directions on systems biology
1 Introduction
2 Challenges in systems biology
3 Systems biology and multi-omics integration
4 Future scope of systems biology
4.1 Systems biology in food production
4.2 Systems biology in agriculture
4.2.1 Systems biology for crop improvement
4.2.2 Systems biology in bioremediation
4.2.3 Soil remediation
4.3 Single cell systems biology
4.4 Systems biology in healthcare and medicine
4.4.1 Systems biology in drug discovery and development
4.4.2 Systems biology in disease modeling and control
4.4.3 Systems biology in personalized medicine
4.4.4 Cancer systems biology
4.4.5 Systems pharmacology
4.4.6 Systems virology
4.5 Machine learning in systems biology
5 Conclusion
Index

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Systems Biology and In-Depth Applications for Unlocking Diseases

Principles, Tools, and Application to Disease

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