Systems biology and in-depth applications for unlocking diseases : principles, tools, and application to disease /

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.

Salvato in:

Dettagli Bibliografici
Altri autori:	Sokouti, Babak, 1978-
Natura:	Elettronico eBook
Lingua:	inglese
Pubblicazione:	London : Academic Press, [2025]
Soggetti:	Systems biology. Diseases > Causes and theories of causation. Biologie systémique. Diseases > Causes and theories of causation Systems biology
Accesso online:	ScienceDirect
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Sommario:

Front Cover
Systems Biology and In-Depth Applications for Unlocking Diseases
Systems Biology and In-Depth Applications for Unlocking Diseases
Copyright
Dedication
Contents
Contributors
Foreword
Preface
I
Introduction
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
Acknowledgments
Conflict of interests statement
References
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
2.2.2.1 G protein-coupled receptors (GPCRs)
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
References
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
References
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
AI disclosure
References
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
References.
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
References
II
Tools and resources
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
References
8
High-throughput data analysis in systems biology: Techniques, challenges, and applications in modern scientific ...
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
3.1.1.1 DNA sequencing
3.1.1.2 Single-nucleotide polymorphism
3.1.1.3 DNA amplification
3.1.1.4 Linkage and association analysis
3.1.1.5 Fluorescence in Situ Hybridization (FISH)
3.1.2 Transcriptomics
3.1.2.1 Imaging-based spatial transcriptomics
3.1.2.2 Sequencing-based spatial 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
References
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&amp
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
Acknowledgments
References
10
The significance and evolution of biological databases in systems biology
1. Historical perspective of biological databases.

Systems biology and in-depth applications for unlocking diseases : principles, tools, and application to disease /

Documenti analoghi