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.
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| Format: | Electronic eBook |
| Language: | English |
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London :
Academic Press,
[2025]
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| Online Access: | ScienceDirect |
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| 001 | on1467283463 | ||
| 003 | OCoLC | ||
| 005 | 20260915095212.0 | ||
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| 008 | 241108s2025 enk ob 001 0 eng d | ||
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| 020 | |a 0443223270 |q electronic book | ||
| 020 | |a 9780443223273 |q (electronic bk.) | ||
| 020 | |z 9780443223266 | ||
| 020 | |z 0443223262 | ||
| 035 | |a (OCoLC)1467283463 | ||
| 050 | 4 | |a QH324.2 |b .S97 2025 | |
| 082 | 0 | 4 | |a 570.285 |2 23/eng/20241120 |1 https://id.oclc.org/worldcat/ddc/E3JdqMqWjJHKw8qbq73M734tfj |
| 245 | 0 | 0 | |a Systems biology and in-depth applications for unlocking diseases : |b principles, tools, and application to disease / |c edited by Babak Sokouti. |
| 264 | 1 | |a London : |b Academic Press, |c [2025] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on March 03, 2025). | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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& -- 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Systems biology. | |
| 650 | 0 | |a Diseases |x Causes and theories of causation. | |
| 650 | 6 | |a Biologie systémique. |0 (CaQQLa)000260964 | |
| 650 | 7 | |a Diseases |x Causes and theories of causation |2 fast |0 (OCoLC)fst00895163 | |
| 650 | 7 | |a Systems biology |2 fast |0 (OCoLC)fst01745552 | |
| 700 | 1 | |a Sokouti, Babak, |d 1978- |1 https://id.oclc.org/worldcat/entity/E39PCjFXbfYDvt4Q7MhmFHktw3 | |
| 776 | 0 | 8 | |i Print version: |z 0443223262 |z 9780443223266 |w (OCoLC)1423132317 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443223266 |