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Elements of Computational Systems Biology

Book Description

Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems

Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology.

  • Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology

  • Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine

  • Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems

  • Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology

Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. CONTENTS
  5. PREFACE
  6. CONTRIBUTORS
  7. PART I: OVERVIEW
    1. CHAPTER 1: ADVANCES IN COMPUTATIONAL SYSTEMS BIOLOGY
      1. 1.1 INTRODUCTION
      2. 1.2 MULTISCALE COMPUTATIONAL MODELING
      3. 1.3 PROTEOMICS
      4. 1.4 COMPUTATIONAL SYSTEMS BIOLOGY AND AGING
      5. 1.5 COMPUTATIONAL SYSTEMS BIOLOGY IN DRUG DESIGN
      6. 1.6 SOFTWARE TOOLS FOR SYSTEMS BIOLOGY
      7. 1.7 CONCLUSION
      8. REFERENCES
  8. PART II: BIOLOGICAL NETWORK MODELING
    1. CHAPTER 2: MODELS IN SYSTEMS BIOLOGY: THE PARAMETER PROBLEM AND THE MEANINGS OF ROBUSTNESS
      1. 2.1 INTRODUCTION
      2. 2.2 MODELS AS DYNAMICAL SYSTEMS
      3. 2.3 THE PARAMETER PROBLEM
      4. 2.4 THE LANDSCAPES OF DYNAMICS
      5. 2.5 THE MEANINGS OF ROBUSTNESS
      6. 2.6 CONCLUSION
      7. REFERENCES
    2. CHAPTER 3: IN SILICO ANALYSIS OF COMBINED THERAPEUTICS STRATEGY FOR HEART FAILURE
      1. 3.1 INTRODUCTION
      2. 3.2 MATERIALS AND METHODS
      3. 3.3 RESULTS
      4. 3.4 DISCUSSION
      5. ACKNOWLEDGMENT
      6. 3A.1 APPENDIX
      7. REFERENCES
    3. CHAPTER 4: RULE BASED MODELING AND MODEL REFINEMENT
      1. 4.1 KAPPA, BRIEFLY
      2. 4.2 REFINEMENT, PRACTICALLY
      3. 4.3 RULE-BASED MODELING
      4. 4.4 REFINEMENT, THEORETICALLY
      5. 4.5 CONCLUSION
      6. REFERENCES
    4. CHAPTER 5: A (NATURAL) COMPUTING PERSPECTIVE ON CELLULAR PROCESSES
      1. 5.1 NATURAL COMPUTING AND COMPUTATIONAL BIOLOGY
      2. 5.2 MEMBRANE COMPUTING
      3. 5.3 FORMAL LANGUAGES PRELIMINARIES
      4. 5.4 MEMBRANE OPERATIONS WITH PERIPHERAL PROTEINS
      5. 5.5 MEMBRANE SYSTEMS WITH PERIPHERAL PROTEINS
      6. 5.6 CELL CYCLE AND BREAST TUMOR GROWTH CONTROL
      7. REFERENCES
    5. CHAPTER 6: SIMULATING FILAMENT DYNAMICS IN CELLULAR SYSTEMS
      1. 6.1 INTRODUCTION
      2. 6.2 BACKGROUND: THE ROLES OF FILAMENTS WITHIN CELLS
      3. 6.3 EXAMPLES OF FILAMENT SIMULATIONS
      4. 6.4 OVERVIEW OF FILAMENT SIMULATION
      5. 6.5 CHANGING FILAMENT LENGTH
      6. 6.6 FORCES ON FILAMENTS
      7. 6.7 IMPOSING CONSTRAINTS
      8. 6.8 SOLVER
      9. 6.9 CONCLUSION
      10. REFERENCES
  9. PART III: BIOLOGICAL NETWORK INFERENCE
    1. CHAPTER 7: RECONSTRUCTION OF BIOLOGICAL NETWORKS BY SUPERVISED MACHINE LEARNING APPROACHES
      1. 7.1 INTRODUCTION
      2. 7.2 GRAPH RECONSTRUCTION AS A PATTERN RECOGNITION PROBLEM
      3. 7.3 EXAMPLES
      4. 7.4 DISCUSSION
      5. REFERENCES
    2. CHAPTER 8: SUPERVISED INFERENCE OF METABOLIC NETWORKS FROM THE INTEGRATION OF GENOMIC DATA AND CHEMICAL INFORMATION
      1. 8.1 INTRODUCTION
      2. 8.2 MATERIALS
      3. 8.3 SUPERVISED NETWORK INFERENCE WITH METRIC LEARNING
      4. 8.4 ALGORITHMS FOR SUPERVISED NETWORK INFERENCE
      5. 8.5 DATA INTEGRATION
      6. 8.6 EXPERIMENTS
      7. 8.7 DISCUSSION AND CONCLUSION
      8. REFERENCES
    3. CHAPTER 9: INTEGRATING ABDUCTION AND INDUCTION IN BIOLOGICAL INFERENCE USING CF-INDUCTION
      1. 9.1 INTRODUCTION
      2. 9.2 LOGICAL MODELING OF METABOLIC FLUX DYNAMICS
      3. 9.3 CF-INDUCTION
      4. 9.4 EXPERIMENTS
      5. 9.5 RELATED WORK
      6. 9.6 CONCLUSION AND FUTURE WORK
      7. ACKNOWLEDGMENTS
      8. REFERENCES
    4. CHAPTER 10: ANALYSIS AND CONTROL OF DETERMINISTIC AND PROBABILISTIC BOOLEAN NETWORKS
      1. 10.1 INTRODUCTION
      2. 10.2 BOOLEAN NETWORK
      3. 10.3 IDENTIFICATION OF ATTRACTORS
      4. 10.4 CONTROL OF BOOLEAN NETWORK
      5. 10.5 PROBABILISTIC BOOLEAN NETWORK
      6. 10.6 COMPUTATION OF STEADY STATES OF PBN
      7. 10.7 CONTROL OF PROBABILISTIC BOOLEAN NETWORKS
      8. 10.8 CONCLUSION
      9. ACKNOWLEDGMENTS
      10. REFERENCES
    5. CHAPTER 11: PROBABILISTIC METHODS AND RATE HETEROGENEITY
      1. 11.1 INTRODUCTION TO PROBABILISTIC METHODS
      2. 11.2 SEQUENCE EVOLUTION IS DESCRIBED USING MARKOV CHAINS
      3. 11.3 AMONG-SITE RATE VARIATION
      4. 11.4 DISTRIBUTION OF RATES ACROSS SITES
      5. 11.5 SITE-SPECIFIC RATE ESTIMATION
      6. 11.6 TREE RECONSTRUCTION USING AMONG-SITE RATE VARIATION MODELS
      7. 11.7 DEPENDENCIES OF EVOLUTIONARY RATES AMONG SITES
      8. 11.8 RELATED WORKS
      9. REFERENCES
  10. PART IV: GENOMICS AND COMPUTATIONAL SYSTEMS BIOLOGY
    1. CHAPTER 12: FROM DNA MOTIFS TO GENE NETWORKS: A REVIEW OF PHYSICAL INTERACTION MODELS
      1. 12.1 INTRODUCTION
      2. 12.2 FUNDAMENTALS OF GENE TRANSCRIPTION
      3. 12.3 PHYSICAL INTERACTION ALGORITHMS
      4. 12.4 CONCLUSION
      5. ACKNOWLEDGMENTS
      6. REFERENCES
    2. CHAPTER 13: THE IMPACT OF WHOLE GENOME IN SILICO SCREENING FOR NUCLEAR RECEPTOR-BINDING SITES IN SYSTEMS BIOLOGY
      1. 13.1 INTRODUCTION
      2. 13.2 NUCLEAR RECEPTORS
      3. 13.3 THE PPAR SUBFAMILY
      4. 13.4 METHODS FOR IN SILICO SCREENING OF TRANSCRIPTION FACTOR-BINDING SITES
      5. 13.5 BINDING DATASET OF PPREs AND THE CLASSIFIER METHOD
      6. 13.6 CLUSTERING OF KNOWN PPAR TARGET GENES
      7. 13.7 CONCLUSION
      8. ACKNOWLEDGMENTS
      9. REFERENCES
    3. CHAPTER 14: ENVIRONMENTAL AND PHYSIOLOGICAL INSIGHTS FROM MICROBIAL GENOME SEQUENCES
      1. 14.1 SOME BACKGROUND, MOTIVATION, AND OPEN QUESTIONS
      2. 14.2 A FIRST STATISTICAL GLIMPSE TO GENOMIC SEQUENCES
      3. 14.3 AN AUTOMATIC DETECTION OF CODON BIAS IN GENES
      4. 14.4 GENOMIC SIGNATURES AND A SPACE OF GENOMES FOR GENOME COMPARISON
      5. 14.5 STUDY OF METABOLIC NETWORKS THROUGH SEQUENCE ANALYSIS AND TRANSCRIPTOMIC DATA
      6. 14.6 FROM GENOME SEQUENCES TO GENOME SYNTHESIS: MINIMAL GENE SETS AND ESSENTIAL GENES
      7. 14.7 A CHROMOSOMAL ORGANIZATION OF ESSENTIAL GENES
      8. 14.8 VIRAL ADAPTATION TO MICROBIAL HOSTS AND VIRAL ESSENTIAL GENES
      9. 14A.1 APPENDIX
      10. REFERENCES
  11. PART V: SOFTWARE TOOLS FOR SYSTEMS BIOLOGY
    1. CHAPTER 15: A LI B ABA : A TEXT MINING TOOL FOR SYSTEMS BIOLOGY
      1. 15.1 INTRODUCTION TO TEXT MINING
      2. 15.2 A LI B ABA AS A TOOL FOR MINING BIOLOGICAL FACTS FROM LITERATURE
      3. 15.3 COMPONENTS AND USAGE OF A LI B ABA
      4. 15.4 ALI BABA'S APPROACH TO TEXT MINING
      5. 15.5 RELATED BIOMEDICAL TEXT MINING TOOLS
      6. 15.6 CONCLUSIONS AND FUTURE PERSPECTIVES
      7. ACKNOWLEDGMENTS
      8. REFERENCES
    2. CHAPTER 16: VALIDATION ISSUES IN REGULATORY MODULE DISCOVERY
      1. 16.1 INTRODUCTION
      2. 16.2 DATA TYPES
      3. 16.3 DATA INTEGRATION
      4. 16.4 VALIDATION APPROACHES
      5. 16.5 CONCLUSIONS
      6. REFERENCES
    3. CHAPTER 17: COMPUTATIONAL IMAGING AND MODELING FOR SYSTEMS BIOLOGY
      1. 17.1 BIOINFORMATICS
      2. 17.2 BIOIMAGE INFORMATICS OF HIGH-CONTENT SCREENING
      3. 17.3 CONNECTING BIOINFORMATICS AND BIOMEDICAL IMAGING
      4. 17.4 SUMMARY
      5. ACKNOWLEDGMENTS
      6. REFERENCES
  12. INDEX
  13. WILEY SERIES ON BIOINFORMATICS: COMPUTATIONAL TECHNIQUES AND ENGINEERING