1st Edition
Fundamentals of Systems Biology From Synthetic Circuits to Whole-cell Models
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.
BUILDING INTUITION
Variations on a Theme of Control
Learning Objectives
Variations
Autoregulation
Our Theme: A Typical Negative Autoregulatory Circuit
Summary
Recommended Reading
Variation: Boolean Representations
Learning Objectives
Boolean Logic and Rules
State Matrices
State Transitions
Dynamics
Timescales
Advantages and Disadvantages of Boolean Analysis
Summary
Recommended Reading
Problems
Variation: Analytical Solutions of Ordinary Differential Equations
Learning Objectives
Synthetic Biological Circuits
From Compartment Models to ODEs
Specifying and Simplifying ODEs with Assumptions
The Steady-State Assumption
Solving the System without Feedback: Removal of Activator
Key Properties of the System Dynamics
Solving the System without Feedback: Addition of Activator
Comparison of Modeling to Experimental Measurements
Addition of Autoregulatory Feedback
Comparison of the Regulated and Unregulated Systems
Summary
Recommended Reading
Problems
Variation: Graphical Analysis
Learning Objectives
Revisiting the Protein Synthesis ODEs
Plotting X versus dX/dt
Fixed Points and Vector Fields
From Vector Fields to Time-Course Plots
Nonlinearity
Bifurcation Analysis
Adding Feedback
Two-Equation Systems
Summary
Recommended Reading
Problems
Variation: Numerical Integration
Learning Objectives
The Euler Method
Accuracy and Error
The Midpoint Method
The Runge-Kutta Method
Summary
Recommended Reading
Problems
Variation: Stochastic Simulation
Learning Objectives
Single Cells and Low Molecule Numbers
Stochastic Simulations
The Probability that Two Molecules Interact and React in a Given Time Interval
The Probability of a Given Molecular Reaction Occurring over Time
The Relationship between Kinetic and Stochastic Constants
Gillespie's Stochastic Simulation Algorithm
Stochastic Simulation of Unregulated Gene Expression
Stochastic Simulations versus Other Modeling Approaches
Summary
Recommended Reading
Problems
FROM CIRCUITS TO NETWORKS
Transcriptional Regulation
Learning Objectives
Transcriptional Regulation and Complexity
More Complex Transcriptional Circuits
The Transcriptional Regulatory Feed-Forward Motif
Boolean Analysis of the Most Common Internally Consistent Feed-Forward Motif Identified in E. coli
An ODE-Based Approach to Analyzing the Coherent Feed-Forward Loop
Robustness of the Coherent Feed-Forward Loop
Experimental Interrogation of the Coherent Feed-Forward Loop
Changing the Interaction from an AND to an OR Relationship
The Single-Input Module
Just-in-Time Gene Expression
Generalization of the Feed-Forward Loop
An Example of a Multigene Feed-Forward Loop: Flagellar Biosynthesis in E. coli
Other Regulatory Motifs
Summary
Recommended Reading
Problems
Signal Transduction
Learning Objectives
Receptor-Ligand Binding to Form a Complex
Application to Real Receptor-Ligand Pairs
Formation of Larger Complexes
Protein Localization
The NF-kB Signaling Network
A Detailed Model of NF-kB Activity
Alternative Representations for the Same Process
Specifying Parameter Values from Data
Bounding Parameter Values
Model Sensitivity to Parameter Values
Reducing Complexity by Eliminating Parameters
Parameter Interactions
Summary
Recommended Reading
Problems
Metabolism
Learning Objectives
Cellular Metabolism
Metabolic Reactions
Compartment Models of Metabolite Concentration
The Michaelis-Menten Equation for Enzyme Kinetics
Determining Kinetic Parameters for the Michaelis-Menten System
Incorporating Enzyme Inhibitory Effects
Flux Balance Analysis
Steady-State Assumption and Exchange Fluxes
Solution Spaces
The Objective Function
Defining the Optimization Problem
Solving FBA Problems Using MATLAB
Applications of FBA to Large-Scale Metabolic Models
Using FBA for Metabolic Engineering
Summary
Recommended Reading
Problems
Integrated Models
Learning Objectives
Dynamic FBA: External versus Internal Concentrations
Environmental Constraints
Integration of FBA Simulations over Time
Comparing Dynamic FBA to Experimental Data
FBA and Transcriptional Regulation
Transcriptional Regulatory Constraints
Regulatory FBA: Method
REGULATORY FBA: Application
Toward Whole-Cell Modeling
Summary
Recommended Reading
Problems
Glossary
Biography
Markus Covert is an Associate Professor of Bioengineering and, by courtesy, Chemical and Systems Biology at Stanford University. He has received the National Institute of Health Director’s Pioneer Award and an Allen Distinguished Investigator Award from the Paul Allen Family Foundation. He is best known for the development of the first “whole-cell” computational model of a bacterial cell.
"Author has excellent command of both aspects of systems biology."
—Joel Bader, Johns Hopkins University, Baltimore, Maryland, USA"… an excellent introduction to systems thinking and modeling in the context of complex biological problems. … uses concrete biological examples to develop systems concepts and model step by step, thus enabling the reader to understand the power of systems biology in the study of complex biological phenomena. … develops a deep intuition of systems thinking in the context of complex biological phenomena. This intuition is then translated into concrete systems modeling approaches enabling readers to apply the systems approach to their own problems."
—Prof Werner Dubitzky, University of Ulster
