Drawing on the latest research in the field, Systems Biology: Mathematical Modeling and Model Analysis presents many methods for modeling and analyzing biological systems, in particular cellular systems. It shows how to use predictive mathematical models to acquire and analyze knowledge about cellular systems. It also explores how the models are systematically applied in biotechnology.
The first part of the book introduces biological basics, such as metabolism, signaling, gene expression, and control as well as mathematical modeling fundamentals, including deterministic models and thermodynamics. The text also discusses linear regression methods, explains the differences between linear and nonlinear regression, and illustrates how to determine input variables to improve estimation accuracy during experimental design.
The second part covers intracellular processes, including enzymatic reactions, polymerization processes, and signal transduction. The author highlights the process-function-behavior sequence in cells and shows how modeling and analysis of signal transduction units play a mediating role between process and function.
The third part presents theoretical methods that address the dynamics of subsystems and the behavior near a steady state. It covers techniques for determining different time scales, sensitivity analysis, structural kinetic modeling, and theoretical control engineering aspects, including a method for robust control. It also explores frequent patterns (motifs) in biochemical networks, such as the feed-forward loop in the transcriptional network of E. coli.
Moving on to models that describe a large number of individual reactions, the last part looks at how these cellular models are used in biotechnology. The book also explains how graphs can illustrate the link between two components in large networks with several interactions.
Fundamentals Introduction Biological Basics The Cell-an Introduction Cell Division and Growth Basics of Metabolism Replication, Transcription, and Translation Fundamentals of Mathematical Modeling Definition-Overview of Different Model Classes Basics of Reaction Engineering Stochastic Description Deterministic Modeling Qualitative Modeling and Analysis Modeling on the Level of Single Cells-the Population Balance Data-Driven Modeling Thermodynamics Model Calibration and Experimental Design Regression Model and Parameter Accuracy Dynamic Systems Identifiability of Dynamic Systems Modeling of Cellular Processes Enzymatic Conversion Fundamentals of Enzyme Kinetics Models for Allosteric Enzymes Influence of Effectors The Hill Equation Multi Substrate Kinetics Transport Processes The Wegscheider Condition Alternative Kinetic Approaches Thermodynamic of a Single Reaction Polymerization Processes Macroscopic View Microscopic View Influence of Regulatory Proteins (Transcription Factors, Repressors) Interaction of Several Regulators Replication Signal Transduction and Genetically Regulated Systems Simple Schemes of Signal Transduction Oscillating Systems Genetically Regulated Networks Spatial Gradients by Signal Transduction Analysis of Signaling Pathways by Heinrich Analysis of Modules and Motifs General Methods of Model Analysis Analysis of Time Hierarchies Sensitivity Analysis Robustness in Stoichiometric Networks Metabolic Control Analysis Biochemical Systems Theory Structured Kinetic Modeling Model Reduction for Signal Proteins Aspects of Control Theory Observability Monotone Systems Integral Feedback Robust Control Motifs in Cellular Networks Feed-Forward Loop (FFL) FFLs in Metabolic Networks FFL in Signaling Systems: Two-component Signal Transduction Further Signaling Motifs Analysis of Cellular Networks Metabolic Engineering Reconstruction of Metabolic Network Tasks and Problem Definition Subspaces of Matrix N Methods to Determine Flux Distributions Strain Optimization Topological Characteristics Network Measures Topological Overlap Formation of Scale Free Networks Appendix Index Exercises and Bibliography appear at the end of each chapter.