Fundamentals Introduction Biological BasicsThe Cell-an Introduction Cell Division and Growth Basics of MetabolismReplication, Transcription, and TranslationFundamentals of Mathematical Modeling Definition-Overview of Different Model Classes Basics of Reaction Engineering Stochastic Description Deterministic Modeling Qualitative Modeling and AnalysisModeling on the Level of Single Cells-the Population Balance Data-Driven Modeling ThermodynamicsModel Calibration and Experimental Design RegressionModel and Parameter AccuracyDynamic SystemsIdentifiability of Dynamic SystemsModeling of Cellular ProcessesEnzymatic Conversion Fundamentals of Enzyme Kinetics Models for Allosteric Enzymes Influence of EffectorsThe Hill Equation Multi Substrate Kinetics Transport Processes The Wegscheider Condition Alternative Kinetic Approaches Thermodynamic of a Single ReactionPolymerization Processes Macroscopic View Microscopic View Influence of Regulatory Proteins (Transcription Factors, Repressors)Interaction of Several RegulatorsReplicationSignal Transduction and Genetically Regulated Systems Simple Schemes of Signal Transduction Oscillating Systems Genetically Regulated NetworksSpatial Gradients by Signal Transduction Analysis of Signaling Pathways by HeinrichAnalysis of Modules and MotifsGeneral Methods of Model AnalysisAnalysis of Time Hierarchies Sensitivity AnalysisRobustness in Stoichiometric Networks Metabolic Control AnalysisBiochemical Systems TheoryStructured Kinetic Modeling Model Reduction for Signal ProteinsAspects 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 TransductionFurther Signaling MotifsAnalysis of Cellular Networks Metabolic Engineering Reconstruction of Metabolic Network Tasks and Problem Definition Subspaces of Matrix N Methods to Determine Flux DistributionsStrain OptimizationTopological Characteristics Network Measures Topological Overlap Formation of Scale Free NetworksAppendixIndexExercises and Bibliography appear at the end of each chapter.

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.