Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource.
This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements.
The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research.
1. Chapter 1. Introduction to Big Mechanisms in Systems Biology - p. 1 2. Chapter 2. System Modeling and System Identification Methods for Big Mechanisms in Biological Systems - p. 9 3. Chapter 3. Procedure for Exploring Big Mechanisms of Systems Biology Through System Identification and Big Database Mining - p. 29 4. Chapter 4. Big Cellular Mechanisms in the Cell Cycle by System Identification and Big Data Mining - p. 39 5. Chapter 5. Big Regulatory Mechanisms in the Transcriptional Regulation Control of Gene Expression Using a Stochastic System Model and Genome-Wide Experimental Data - p. 87 6. Abstract - p. 87 7. Introduction - p. 87 8. 5.1 Identification of TF Cooperativity in Gene Regulation of the Cell Cycle via the Stochastic System Model - p. 88 9. Appendix A Methods in Identifying the TF Cooperativity - p. 102 10. 5.2 Cis-Regulatory Mechanisms for Gene Expression via Cross-Gene Identification and Data Mining - p. 105 11. 5.3 Nonlinear Dynamic Trans/Cis-Regulatory Mechanisms for Gene Transcription via Microarray Data - p. 128 12. Appendix B Figures - p. 150 13. References - p. 152 14. Chapter 6. Big Mechanisms of Information Flow in Cellular Systems in Response to Environmental Stress Signals via System Identification and Data Mining - p. 155 15. Abstract - p. 155 16. Introduction - p. 155 17. 6.1 Constructing Stress-Response Mechanisms via Dynamic Gene Regulatory Modeling and Data Mining - p. 156 18. 6.2 Identifying Protective Mechanisms of Gene and Protein Networks in Response to a Broad Range of Environmental Stress Signals - p. 167 19. 6.3 Constructing GRNs for Control Mechanisms of Photosynthetic Light Acclimation in Response to Different Light Signals - p. 194 20. 6.4 Constructing IGECN for Investigating Whole Cellular Signal Flow Mechanisms in Response to Environmental Stress Signals Using High-Throughput NGS - p. 213 21. References - p. 237 22. Chapter 7. Big Offensive and Defensive Mechanisms in Systems Immunity From System Modeling and Big Data Mining - p. 249 23. Abstract - p. 249 24. Introduction - p. 249 25. 7.1 A Systems Biology Approach to Construct the GRN of Systemic Inflammation Mechanisms via Microarray and Databases Mining - p. 250 26. Appendix A Tables and Figures - p. 276 27. 7.2 Identification of Infection and Defense-Related Mechanisms via a Dynamic Host–Pathogen Interaction Network Using C. albicans-Zebrafish Infection Model - p. 295 28. Appendix B Methods, Tables, and Figures - p. 321 29. 7.3 Investigating Host–Pathogen Interaction Networks to Reveal the Pathogenic Mechanism in HIV Infection: A Systems Biology Approach - p. 329 30. Appendix C Figures - p. 359 31. References - p. 362 32. Chapter 8. Big Regeneration Mechanisms via Systems Biology and Big Database Mining Methods - p. 373 33. Chapter 9. Big Tumorigenesis Mechanisms in Systems Cancer Biology via Big Database Mining and Network Modeling - p. 431 34. Abstract - p. 431 35. Introduction - p. 431 36. 9.1 Construction and Clarification of Dynamic Networks of the Cancer Cell Cycle via Microarray Data - p. 433 37. Appendix A Methods - p. 450 38. 9.2 Investigating Tumorigenesis Mechanisms by Cancer-Perturbed PPINs - p. 453 39. Appendix B Methods of Constructing Cancer-Perturbed PPINs - p. 467 40. 9.3 A Network-Based Biomarker Approach for Molecular Investigation and Diagnosis of Lung Cancer - p. 474 41. Appendix C Tables and Figures - p. 493 42. 9.4 Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived From NGS Data - p. 494 43. References - p. 518 44. Chapter 10. Big Evolutionary Mechanisms of Network Robustness and Signaling Transductivity in Aging and Carcinogenic Process by System Modeling and Database Mining - p. 527 45. Abstract - p. 527 46. Introduction - p. 527 47. 10.1 New Measurement Methods of Network Robustness and Response Ability in Aging and Carcinogenic Process via Microarray Data and Dynamic System Model - p. 529 48. Appendix A Methods and Figures - p. 552 49. 10.2 Evolution of Signal Transductivities of Coupled Signal Pathways in the Carcinogenic Process - p. 560 50. Appendix B Figures - p. 617 51. 10.3 Nonlinear Stochastic Game Strategy for Evolution Mechanisms of Organ Carcinogenesis Under a Natural Selection Scheme - p. 617 52. Appendix C - p. 656 53. References - p. 662 54. Chapter 11. Big Mechanisms of Aging via System Identification and Big Database Mining - p. 671 55. Abstract - p. 671 56. Introduction - p. 671 57. 11.1 On the Systematic Mechanism of GRN in the Aging Process: A Systems Biology Approach via Microarray Data - p. 672 58. 11.2 Investigating Specific Core GEN for Cellular Mechanisms of Human Aging via NGS Data - p. 696 59. References - p. 729 60. Chapter 12. Big Drug Design Mechanisms via Systems Biology and Big Database Mining - p. 737 61. Abstract - p. 737 62. Introduction - p. 737 63. 12.1 Overview of Drug Discovery Using Systems Biology - p. 738 64. 12.2 Investigating Core and Specific Network Markers of Cancers for Multiple Drug Targets - p. 750 65. Appendix A Methods, Tables, and Figures - p. 785 66. 12.3 Systems Drug Design Mechanisms for Multiple Drug Targets - p. 796 67. Appendix B Method and Table - p. 828 68. References - p. 833 69. Index - p. 847
Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource.
This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements.
The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research.
1. Chapter 1. Introduction to Big Mechanisms in Systems Biology - p. 1 2. Chapter 2. System Modeling and System Identification Methods for Big Mechanisms in Biological Systems - p. 9 3. Chapter 3. Procedure for Exploring Big Mechanisms of Systems Biology Through System Identification and Big Database Mining - p. 29 4. Chapter 4. Big Cellular Mechanisms in the Cell Cycle by System Identification and Big Data Mining - p. 39 5. Chapter 5. Big Regulatory Mechanisms in the Transcriptional Regulation Control of Gene Expression Using a Stochastic System Model and Genome-Wide Experimental Data - p. 87 6. Abstract - p. 87 7. Introduction - p. 87 8. 5.1 Identification of TF Cooperativity in Gene Regulation of the Cell Cycle via the Stochastic System Model - p. 88 9. Appendix A Methods in Identifying the TF Cooperativity - p. 102 10. 5.2 Cis-Regulatory Mechanisms for Gene Expression via Cross-Gene Identification and Data Mining - p. 105 11. 5.3 Nonlinear Dynamic Trans/Cis-Regulatory Mechanisms for Gene Transcription via Microarray Data - p. 128 12. Appendix B Figures - p. 150 13. References - p. 152 14. Chapter 6. Big Mechanisms of Information Flow in Cellular Systems in Response to Environmental Stress Signals via System Identification and Data Mining - p. 155 15. Abstract - p. 155 16. Introduction - p. 155 17. 6.1 Constructing Stress-Response Mechanisms via Dynamic Gene Regulatory Modeling and Data Mining - p. 156 18. 6.2 Identifying Protective Mechanisms of Gene and Protein Networks in Response to a Broad Range of Environmental Stress Signals - p. 167 19. 6.3 Constructing GRNs for Control Mechanisms of Photosynthetic Light Acclimation in Response to Different Light Signals - p. 194 20. 6.4 Constructing IGECN for Investigating Whole Cellular Signal Flow Mechanisms in Response to Environmental Stress Signals Using High-Throughput NGS - p. 213 21. References - p. 237 22. Chapter 7. Big Offensive and Defensive Mechanisms in Systems Immunity From System Modeling and Big Data Mining - p. 249 23. Abstract - p. 249 24. Introduction - p. 249 25. 7.1 A Systems Biology Approach to Construct the GRN of Systemic Inflammation Mechanisms via Microarray and Databases Mining - p. 250 26. Appendix A Tables and Figures - p. 276 27. 7.2 Identification of Infection and Defense-Related Mechanisms via a Dynamic Host–Pathogen Interaction Network Using C. albicans-Zebrafish Infection Model - p. 295 28. Appendix B Methods, Tables, and Figures - p. 321 29. 7.3 Investigating Host–Pathogen Interaction Networks to Reveal the Pathogenic Mechanism in HIV Infection: A Systems Biology Approach - p. 329 30. Appendix C Figures - p. 359 31. References - p. 362 32. Chapter 8. Big Regeneration Mechanisms via Systems Biology and Big Database Mining Methods - p. 373 33. Chapter 9. Big Tumorigenesis Mechanisms in Systems Cancer Biology via Big Database Mining and Network Modeling - p. 431 34. Abstract - p. 431 35. Introduction - p. 431 36. 9.1 Construction and Clarification of Dynamic Networks of the Cancer Cell Cycle via Microarray Data - p. 433 37. Appendix A Methods - p. 450 38. 9.2 Investigating Tumorigenesis Mechanisms by Cancer-Perturbed PPINs - p. 453 39. Appendix B Methods of Constructing Cancer-Perturbed PPINs - p. 467 40. 9.3 A Network-Based Biomarker Approach for Molecular Investigation and Diagnosis of Lung Cancer - p. 474 41. Appendix C Tables and Figures - p. 493 42. 9.4 Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived From NGS Data - p. 494 43. References - p. 518 44. Chapter 10. Big Evolutionary Mechanisms of Network Robustness and Signaling Transductivity in Aging and Carcinogenic Process by System Modeling and Database Mining - p. 527 45. Abstract - p. 527 46. Introduction - p. 527 47. 10.1 New Measurement Methods of Network Robustness and Response Ability in Aging and Carcinogenic Process via Microarray Data and Dynamic System Model - p. 529 48. Appendix A Methods and Figures - p. 552 49. 10.2 Evolution of Signal Transductivities of Coupled Signal Pathways in the Carcinogenic Process - p. 560 50. Appendix B Figures - p. 617 51. 10.3 Nonlinear Stochastic Game Strategy for Evolution Mechanisms of Organ Carcinogenesis Under a Natural Selection Scheme - p. 617 52. Appendix C - p. 656 53. References - p. 662 54. Chapter 11. Big Mechanisms of Aging via System Identification and Big Database Mining - p. 671 55. Abstract - p. 671 56. Introduction - p. 671 57. 11.1 On the Systematic Mechanism of GRN in the Aging Process: A Systems Biology Approach via Microarray Data - p. 672 58. 11.2 Investigating Specific Core GEN for Cellular Mechanisms of Human Aging via NGS Data - p. 696 59. References - p. 729 60. Chapter 12. Big Drug Design Mechanisms via Systems Biology and Big Database Mining - p. 737 61. Abstract - p. 737 62. Introduction - p. 737 63. 12.1 Overview of Drug Discovery Using Systems Biology - p. 738 64. 12.2 Investigating Core and Specific Network Markers of Cancers for Multiple Drug Targets - p. 750 65. Appendix A Methods, Tables, and Figures - p. 785 66. 12.3 Systems Drug Design Mechanisms for Multiple Drug Targets - p. 796 67. Appendix B Method and Table - p. 828 68. References - p. 833 69. Index - p. 847
