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El. knyga: Cybernetic Modeling for Bioreaction Engineering

(Pacific Northwest National Laboratory, Washington), (Purdue University, Indiana)
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Offers dynamic models of varying detail, serving a spectrum of needs, ranging from optimization and control of bioprocesses to detailed treatment of metabolism for rational design of new strains.

Uniquely focusing on dynamic modeling, this volume incorporates metabolic regulation as a survival mechanism for cells, by driving metabolism through optimal investment of its resources for control of enzyme synthesis and activity. Consequently, the models have a proven record of describing various uptake patterns of mixed carbon substrates that have become significant in modern applications of biomass for the production of bioenergy. The models accurately describe dynamic behavior of microbes in nutrient environments with mixtures of complementary substrates, such as carbon and nitrogen. Modeling of large metabolic networks (including prospects for extension to genome scale) is enabled by lumped hybrid cybernetic models with an unparalleled capacity to predict dynamic behavior of knockout strains. This is an invaluable, must-have reference for bio-researchers and practicing engineers.

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Describes dynamic state of metabolic systems, while paving the way for fully predictive modeling frameworks.
Preface xi
General Notations xv
1 Introduction
1(2)
2 Enzymatic Adaptation
3(10)
2.1 Enzyme Balance
4(1)
2.2 Metabolic Reaction Rate
5(1)
2.3 The Cybernetic Variables
6(7)
2.3.1 The Control of Enzyme Synthesis
6(3)
2.3.2 The Control of Enzyme Activity
9(4)
3 Early Development of Cybernetic Models
13(51)
3.1 Modeling of Diauxic Growth
13(5)
3.2 Growth and Maintenance in Low Substrate Environments
18(9)
3.3 A Model for the Production of a Bacterial Metabolite
27(9)
3.4 More on Growth on Mixed Carbon Substrates: Simultaneous Utilization
36(6)
3.4.1 Cybernetic Models of Mixed Substrate Growth: Sequential and Simultaneous Utilization of Substrates
37(5)
3.5 Toward Metabolic Networks
42(21)
3.5.1 Elementary Pathways
42(4)
3.5.2 Growth on Complementary Nutrients: Interactive and Noninteractive Substrates
46(4)
3.5.3 Modeling of Bacterial Growth under Multiple Nutrient Limitations
50(13)
3.6 Concluding Remarks
63(1)
4 Revisiting Cybernetic Laws via Optimal Control Theory
64(22)
4.1 System Variables and the Optimal Control Problem
64(2)
4.2 The Matching Law
66(3)
4.3 The Proportional Law
69(2)
4.4 Tandem Treatment of Matching and Proportional Laws
71(1)
4.5 Retrospection of Past Cybernetic Models
72(2)
4.6 Computational Assessment of Different Cybernetic Control Laws
74(11)
4.6.1 Comparison of Different Cybernetic Models
76(6)
4.6.2 Analysis of an Evolutionary Scenario
82(3)
4.7 Concluding Remarks
85(1)
5 Toward Modeling of Metabolic Networks
86(19)
5.1 Cybernetic Modeling of Metabolic Networks
88(15)
5.1.1 Model Formulation
88(4)
5.1.2 Modeling of a Simple Linear Pathway
92(3)
5.1.3 Modeling of Anaerobic Metabolism of Escherichia coli
95(8)
5.2 Concluding Remarks
103(2)
6 The Hybrid Cybernetic Model (HCM)
105(45)
6.1 Modeling of Regulation
106(4)
6.2 Anaerobic Growth of E. coli
110(14)
6.2.1 HCM Simulations for Glucose Limited Growth
111(7)
6.2.2 HCM Simulations for Growth on Glucose-Pyruvate Mixtures
118(6)
6.3 A Mode Reduction Technique for Lower Order HCM
124(7)
6.3.1 A General Formulation of Metabolic Yield Analysis
126(5)
6.4 HCM of Yeast Co-Consuming Glucose and Xylose for Ethanol Production
131(9)
6.4.1 Parameter Determination
135(1)
6.4.2 HCM simulations of Co-Consumption of Glucose and Xylose by Recombinant Yeast. Comparison with Other Models
136(4)
6.5 HCM of Carbon Storage Molecule Accumulation: Poly(β-hydroxybutyrate)
140(4)
6.6 HCM for a Mixed Culture of Yeasts for Bioethanol Production
144(5)
6.7 Concluding Remarks
149(1)
7 The Lumped Hybrid Cybernetic Model (L-HCM)
150(36)
7.1 Modeling Concept
151(7)
7.1.1 Elementary Mode (EM) Families: A Classification of EMs
151(2)
7.1.2 Uptake Flux Distribution to EM Families
153(1)
7.1.3 Modeling of Regulation in L-HCM
154(3)
7.1.4 Nature of Flux Distribution in L-HCM
157(1)
7.2 L-HCM for Aerobic Growth of Saccharomyces cerevisiae: The Crabtree Effect
158(9)
7.2.1 Metabolic Network for S. cerevisiae
159(1)
7.2.2 EMs and EM Lumps
159(2)
7.2.3 L-HCM Equations
161(1)
7.2.4 A Lumped Cybernetic Model (LCM) for the Crabtree Effect
162(2)
7.2.5 Performance of L-HCM on Aerobic Growth of S. cerevisiae
164(3)
7.3 More on Lumping EMs
167(2)
7.4 L-HCM of Multiple Strains of E. coli
169(7)
7.4.1 EM Lumping: Anaerobic Growth of E. coli on Glucose
170(1)
7.4.2 L-HCM Equations: Anaerobic Growth of E. coli on Glucose
170(1)
7.4.3 Dynamics of Anaerobic Growth of E. coli on Glucose: L-HCM Predictions
171(1)
7.4.4 Effect of Yield Data on EM Lumping
171(5)
7.4.5 On Other EM Lumpings in the Literature
176(1)
7.5 L-HCM of Aerobic Growth of Shewanella oneidensis
176(8)
7.5.1 Metabolic Network for S. oneidensis
178(2)
7.5.2 L-HCM Equations for S. oneidensis
180(4)
7.6 Concluding Remarks
184(2)
8 Predicting Dynamic Behavior of Mutant Strains with L-HCM
186(27)
8.1 Prolegomena
186(5)
8.1.1 L-HCM Approach to Predicting KO Strain Behavior
187(2)
8.1.2 Illustration with a Toy Example
189(2)
8.2 L-HCM Predictions of Single Gene Knockouts of E. coli: Anaerobic Growth
191(7)
8.2.1 Reflections on L-HCM Predictions of Single KO Strains
196(2)
8.3 Toward Genome Scale Modeling
198(14)
8.3.1 Optimization-Based Approaches for EM Computation
200(1)
8.3.2 Basic Formulation
201(1)
8.3.3 Typical MILP-Based Approach
202(1)
8.3.4 AILP-Based Algorithm
203(2)
8.3.5 Basic Properties of AILP
205(3)
8.3.6 Computation of EMs from Genome-Scale Networks
208(1)
8.3.7 EM Sampling by AILP
209(3)
8.3.8 Summary
212(1)
8.4 Concluding Remarks
212(1)
9 Nonlinear Analysis of Cybernetic Models
213(22)
9.1 Introduction
213(16)
9.1.1 Multiple Steady States in a Continuous Bioreactor: The Chemostat
215(6)
9.1.2 HCM Prediction of Steady-State Multiplicity in a Continuous Reactor Fed with Pyruvate-Glucose Mixtures
221(1)
9.1.3 LCM Prediction of Steady-State Multiplicity in Hybridoma Cultures
222(7)
9.2 Oscillatory Behavior with Cybernetic Models
229(5)
9.2.1 Oscillations in Continuous Cultures of Yeast (S. cerevisiae)
230(1)
9.2.2 Oscillations in Bacterial Cultures
230(4)
9.3 Concluding Remarks
234(1)
10 Metabolic Modeling Landscape
235(17)
10.1 Introduction
235(1)
10.2 Fully Structured Dynamic Models
236(3)
10.2.1 Conventional Approaches. Kinetic Formalisms
237(1)
10.2.2 The Cybernetic Model: Young's Model
238(1)
10.3 Quasi Steady State (QSS) Models
239(5)
10.3.1 Steady-State Network Analysis: FBA and EM Analysis
240(1)
10.3.2 Conventional Approaches: DFBA and MBM
241(1)
10.3.3 The Cybernetic Approach: HCM and L-HCM
242(2)
10.4 Unstructured Dynamic Models
244(1)
10.5 Nexus of Metabolic Models
245(1)
10.6 Model Selection
246(5)
10.6.1 Modeling Goals
247(1)
10.6.2 Systematic Model Evaluation Based on Information Theoretic Tools
247(2)
10.6.3 Prediction of Emergent Properties
249(2)
10.7 Concluding Remarks
251(1)
References 252(14)
Index 266
Doraiswami Ramkrishna is the Harry Creighton Peffer Distinguished Professor of Chemical Engineering at Purdue University, Indiana. He pioneered the development of dynamic metabolic modeling, and has been active in the area for over thirty years. He is a member of the National Academy of Engineering, coined the term 'cybernetic modeling', and has authored several academic books. Hyun-Seob Song is a Senior Research Scientist at Pacific Northwest National Laboratory, Washington (PNNL). His new development of metabolic pathway analysis enabled dynamic metabolic modeling for complex, large-scale networks. He is also active in the areas of network inference and microbial community modeling.
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