Fundamentals of Systems Biology

Fundamentals of Systems Biology

by Markus W. Covert
Released October 2017
Publisher(s): CRC Press
ISBN: 9781000055016

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Book description

For decades biology has focused on decoding cellular processes one gene at a time, but many of the most pressing biological questions, as well as diseases such as cancer and heart disease, are related to complex systems involving the interaction of hundreds, or even thousands, of gene products and other factors. How do we begin to understand this complexity?

Fundamentals of Systems Biology: From Synthetic Circuits to Whole-cell Models introduces students to methods they can use to tackle complex systems head-on, carefully walking them through studies that comprise the foundation and frontier of systems biology. The first section of the book focuses on bringing students quickly up to speed with a variety of modeling methods in the context of a synthetic biological circuit. This innovative approach builds intuition about the strengths and weaknesses of each method and becomes critical in the book’s second half, where much more complicated network models are addressed—including transcriptional, signaling, metabolic, and even integrated multi-network models.

The approach makes the work much more accessible to novices (undergraduates, medical students, and biologists new to mathematical modeling) while still having much to offer experienced modelers--whether their interests are microbes, organs, whole organisms, diseases, synthetic biology, or just about any field that investigates living systems.

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

Product information

  • Title: Fundamentals of Systems Biology
  • Author(s): Markus W. Covert
  • Release date: October 2017
  • Publisher(s): CRC Press
  • ISBN: 9781000055016