Fundamentals of Systems Biology

Table of contents

1. Cover
2. Half Title
3. Title
4. Copyright
5. Contents
6. Preface
7. Acknowledgments
8. About the Author
9. Section I Building Intuition
   1. Chapter 1 ■ Variations on a Theme of Control
      1. LEARNING OBJECTIVES
      2. VARIATIONS
      3. AUTOREGULATION
      4. OUR THEME: A TYPICAL NEGATIVE AUTOREGULATORY CIRCUIT
      5. CHAPTER SUMMARY
      6. RECOMMENDED READING
   2. Chapter 2 ■ Variation: Boolean Representations
      1. LEARNING OBJECTIVES
      2. BOOLEAN LOGIC AND RULES
      3. STATE MATRICES
      4. STATE TRANSITIONS
      5. DYNAMICS
      6. TIMESCALES
      7. ADVANTAGES AND DISADVANTAGES OF BOOLEAN ANALYSIS
      8. CHAPTER SUMMARY
      9. RECOMMENDED READING
      10. PROBLEMS
   3. Chapter 3 ■ Variation: Analytical Solutions of Ordinary Differential Equations
      1. LEARNING OBJECTIVES
      2. SYNTHETIC BIOLOGICAL CIRCUITS
      3. FROM COMPARTMENT MODELS TO ODES
      4. SPECIFYING AND SIMPLIFYING ODES WITH ASSUMPTIONS
      5. THE STEADY-STATE ASSUMPTION
      6. SOLVING THE SYSTEM WITHOUT FEEDBACK: REMOVAL OF ACTIVATOR
      7. KEY PROPERTIES OF THE SYSTEM DYNAMICS
      8. SOLVING THE SYSTEM WITHOUT FEEDBACK: ADDITION OF ACTIVATOR
      9. COMPARISON OF MODELING TO EXPERIMENTAL MEASUREMENTS
      10. ADDITION OF AUTOREGULATORY FEEDBACK
      11. COMPARISON OF THE REGULATED AND UNREGULATED SYSTEMS
      12. CHAPTER SUMMARY
      13. RECOMMENDED READING
      14. PROBLEMS
   4. Chapter 4 ■ Variation: Graphical Analysis
      1. LEARNING OBJECTIVES
      2. REVISITING THE PROTEIN SYNTHESIS ODES
      3. PLOTTING X VERSUS DX/DT
      4. FIXED POINTS AND VECTOR FIELDS
      5. FROM VECTOR FIELDS TO TIME-COURSE PLOTS
      6. NONLINEARITY
      7. BIFURCATION ANALYSIS
      8. ADDING FEEDBACK
      9. TWO-EQUATION SYSTEMS
      10. CHAPTER SUMMARY
      11. RECOMMENDED READING
      12. PROBLEMS
   5. Chapter 5 ■ Variation: Numerical Integration
      1. LEARNING OBJECTIVES
      2. THE EULER METHOD
      3. ACCURACY AND ERROR
      4. THE MIDPOINT METHOD
      5. THE RUNGE–KUTTA METHOD
      6. CHAPTER SUMMARY
      7. RECOMMENDED READING
      8. PROBLEMS
   6. Chapter 6 ■ Variation: Stochastic Simulation
      1. LEARNING OBJECTIVES
      2. SINGLE CELLS AND LOW MOLECULE NUMBERS
      3. STOCHASTIC SIMULATIONS
      4. THE PROBABILITY THAT TWO MOLECULES INTERACT AND REACT IN A GIVEN TIME INTERVAL
      5. THE PROBABILITY OF A GIVEN MOLECULAR REACTION OCCURRING OVER TIME
      6. THE RELATIONSHIP BETWEEN KINETIC AND STOCHASTIC CONSTANTS
      7. GILLESPIE’S STOCHASTIC SIMULATION ALGORITHM
      8. STOCHASTIC SIMULATION OF UNREGULATED GENE EXPRESSION
      9. STOCHASTIC SIMULATIONS VERSUS OTHER MODELING APPROACHES
      10. CHAPTER SUMMARY
      11. RECOMMENDED READING
      12. PROBLEMS
10. Section II From Circuits to Networks
    1. Chapter 7 ■ Transcriptional Regulation
       1. LEARNING OBJECTIVES
       2. TRANSCRIPTIONAL REGULATION AND COMPLEXITY
       3. MORE COMPLEX TRANSCRIPTIONAL CIRCUITS
       4. THE TRANSCRIPTIONAL REGULATORY FEED-FORWARD MOTIF
       5. BOOLEAN ANALYSIS OF THE MOST COMMON INTERNALLY CONSISTENT FEED-FORWARD MOTIF IDENTIFIED IN E. COLI
       6. AN ODE-BASED APPROACH TO ANALYZING THE COHERENT FEED-FORWARD LOOP
       7. ROBUSTNESS OF THE COHERENT FEED-FORWARD LOOP
       8. EXPERIMENTAL INTERROGATION OF THE COHERENT FEED-FORWARD LOOP
       9. CHANGING THE INTERACTION FROM AN AND TO AN OR RELATIONSHIP
       10. THE SINGLE-INPUT MODULE
       11. JUST-IN-TIME GENE EXPRESSION
       12. GENERALIZATION OF THE FEED-FORWARD LOOP
       13. AN EXAMPLE OF A MULTIGENE FEED-FORWARD LOOP: FLAGELLAR BIOSYNTHESIS IN E. COLI
       14. OTHER REGULATORY MOTIFS
       15. CHAPTER SUMMARY
       16. RECOMMENDED READING
       17. PROBLEMS
    2. Chapter 8 ■ Signal Transduction
       1. LEARNING OBJECTIVES
       2. RECEPTOR-LIGAND BINDING TO FORM A COMPLEX
       3. APPLICATION TO REAL RECEPTOR-LIGAND PAIRS
       4. FORMATION OF LARGER COMPLEXES
       5. PROTEIN LOCALIZATION
       6. THE NF-κB SIGNALING NETWORK
       7. A DETAILED MODEL OF NF-κB ACTIVITY
       8. ALTERNATIVE REPRESENTATIONS FOR THE SAME PROCESS
       9. SPECIFYING PARAMETER VALUES FROM DATA
       10. BOUNDING PARAMETER VALUES
       11. MODEL SENSITIVITY TO PARAMETER VALUES
       12. REDUCING COMPLEXITY BY ELIMINATING PARAMETERS
       13. PARAMETER INTERACTIONS
       14. CHAPTER SUMMARY
       15. RECOMMENDED READING
       16. PROBLEMS
    3. Chapter 9 ■ Metabolism
       1. LEARNING OBJECTIVES
       2. CELLULAR METABOLISM
       3. METABOLIC REACTIONS
       4. COMPARTMENT MODELS OF METABOLITE CONCENTRATION
       5. THE MICHAELIS–MENTEN EQUATION FOR ENZYME KINETICS
       6. DETERMINING KINETIC PARAMETERS FOR THE MICHAELIS–MENTEN SYSTEM
       7. INCORPORATING ENZYME INHIBITORY EFFECTS
       8. FLUX BALANCE ANALYSIS
       9. STEADY-STATE ASSUMPTION AND EXCHANGE FLUXES
       10. SOLUTION SPACES
       11. THE OBJECTIVE FUNCTION
       12. DEFINING THE OPTIMIZATION PROBLEM
       13. SOLVING FBA PROBLEMS USING MATLAB
       14. APPLICATIONS OF FBA TO LARGE-SCALE METABOLIC MODELS
       15. USING FBA FOR METABOLIC ENGINEERING
       16. CHAPTER SUMMARY
       17. RECOMMENDED READING
       18. PROBLEMS
    4. Chapter 10 ■ Integrated Models
       1. LEARNING OBJECTIVES
       2. DYNAMIC FBA: EXTERNAL VERSUS INTERNAL CONCENTRATIONS
       3. ENVIRONMENTAL CONSTRAINTS
       4. INTEGRATION OF FBA SIMULATIONS OVER TIME
       5. COMPARING DYNAMIC FBA TO EXPERIMENTAL DATA
       6. FBA AND TRANSCRIPTIONAL REGULATION
       7. TRANSCRIPTIONAL REGULATORY CONSTRAINTS
       8. REGULATORY FBA: METHOD
       9. REGULATORY FBA: APPLICATION
       10. TOWARD WHOLE-CELL MODELING
       11. CHAPTER SUMMARY
       12. RECOMMENDED READING
       13. PROBLEMS
11. GLOSSARY
12. INDEX