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Computational Systems Biology Of Synaptic Plasticity: Modelling Of Biochemical Pathways Related To Memory Formation And Impairement [Kietas viršelis]

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(Lincoln Univ, New Zealand), (Lincoln Univ, New Zealand)
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Computational Systems Biology Of Synaptic Plasticity: Modelling Of Biochemical Pathways Related To Memory Formation And ImpairementDidesnis paveikslėlis
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Raktažodžiai:
In order to explore memory from a systems biology perspective, Kulasiri and He survey recent research on modeling biochemical pathways related to intra-cellular networks within the synapses, introduce the biology of synapses at an elementary level, and provide a succinct background to the mathematical methods related to system biology in general. Their topics include a theoretical model of state transition of CaM-dependent protein kinase II, the bi-directionality of synaptic pathways related to long-term potentiation and long-term depression, the uncertainty quantification of models related to synaptic plasticity, and synaptic plasticity in dementia: Alzheimer's disease and the role of calcium. Annotation ©2017 Ringgold, Inc., Portland, OR (protoview.com)

This book demonstrates the power of mathematical thinking in understanding the biological complexity that exists within the brain. It looks at the latest research on modelling of biochemical pathways within synapses, and provides a clear background for the study of mathematical models related to systems biology. Discussion then focusses on developments in computational models based on networks linked to synaptic plasticity. The models are used to understand memory formation and impairment and they provide a mathematical basis for memory research.Computational Systems Biology of Synaptic Plasticity is a valuable source of knowledge to postgraduate students and researchers in computational systems biology, and as a reference book for various techniques that are needed in modelling biological processes.
Dedication v
Preface vii
About the Authors xi
List of Tables xix
Chapter 1 Introduction 1(18)
1.1 Synaptic Plasticity and Related Dynamics
1(7)
1.2 Importance of Synaptic Plasticity
8(3)
1.3 Modelling Single Cell Dynamics Associated with Synaptic Plasticity
11(3)
References
14(5)
Chapter 2 Mathematical Methods in Modelling Biochemical Networks in Systems Biology 19(68)
2.1 Relevant Background
21(3)
2.1.1 Cooperativity
22(1)
2.1.2 Feedback loops
23(1)
2.2 Mathematical Concepts
24(17)
2.2.1 Mass action rate law
24(5)
2.2.2 Michaelis-Menten rate law
29(3)
2.2.3 Hill rate law
32(3)
2.2.4 Parameter estimation using Markov chain Monte Carlo (MCMC)
35(4)
2.2.5 Parameter sensitivity analysis
39(2)
2.3 Intracellular Fluctuations
41(11)
2.3.1 "Noise" in networks
41(2)
2.3.2 Intrinsic noise
43(1)
2.3.3 Extrinsic noise
44(1)
2.3.4 Notable experiments
44(3)
2.3.5 Noise: nuisance or necessity?
47(1)
2.3.6 Noise propagation in networks
48(2)
2.3.7 Noise and robustness
50(1)
2.3.8 Noise controlling mechanisms
51(1)
2.4 Modelling Networks
52(5)
2.4.1 Importance of mathematical modelling
52(2)
2.4.2 Nonlinearity issues in modelling
54(1)
2.4.3 Multi-scale issues in modelling biochemical systems
55(2)
2.5 Approaches in Modelling
57(21)
2.5.1 Bottom-up modelling
58(20)
2.5.2 Top-down: Reverse engineering of networks
78(1)
References
78(9)
Chapter 3 Proteins, Mechanisms and Networks in NMDAR-dependent Synaptic Plasticity 87(38)
3.1 Introduction
87(2)
3.2 NMDAR-Dependent Synaptic Plasticity and Memory
89(14)
3.2.1 NMDAR
93(1)
3.2.2 Ca2+ and CaM interaction
94(1)
3.2.3 CaMKII
94(4)
3.2.4 Cyclic adenosine monophosphate (cAMP) and PKA
98(1)
3.2.5 A-kinase anchoring proteins
99(1)
3.2.6 Interactions among modulators
100(2)
3.2.7 Summary of the alterations on AMPAR
102(1)
3.3 Mathematical Models of Synaptic Plasticity
103(12)
3.3.1 Generating Ca2+ signals
105(2)
3.3.2 Single component models
107(3)
3.3.3 Simplified models
110(1)
3.3.4 Complete pathway models
111(4)
References
115(10)
Chapter 4 A Theoretical Model of State Transition of CaMKII 125(44)
4.1 Introduction
125(1)
4.2 Model Development
126(18)
4.2.1 Model assumptions
127(1)
4.2.2 The states of CaMKII
128(1)
4.2.3 A probabilistic framework of the CaMKII-NMDAR binding
129(6)
4.2.4 IST of CaMKII
135(4)
4.2.5 A new model of HST of CaMKII
139(4)
4.2.6 Complete model and constraints
143(1)
4.3 Simulations with MoHST
144(7)
4.3.1 Input: Generation of Ca2+
144(2)
4.3.2 Estimation of parameters
146(3)
4.3.3 Computational implementation and experiments with MoHST
149(2)
4.4 Parameter Perturbation
151(5)
4.4.1 Methods of parameter perturbation
151(2)
4.4.2 Factors related to the formation of CaMKII-NMDAR complex
153(1)
4.4.3 Discussion and summary
154(2)
4.5 Implication of the Autophosphorylation in LTP
156(13)
4.5.1 Computational experiments
157(1)
4.5.2 Role of the autophosphorylation related to CaMKII translocation
157(2)
4.5.3 Role of the autophosphorylation related to the formation of CaMKII-NMDAR complex
159(4)
4.5.4 Discussion and summary 163 References
163(6)
Chapter 5 Bidirectionality of Synaptic Pathways Related to LTP and LTD 169(86)
5.1 Introduction
169(6)
5.2 Background of the Key Modulators
175(1)
5.2.1 Proteins related to LTP
175(1)
5.2.2 Proteins related to LTD
176(1)
5.3 The Overview of MoNP
176(3)
5.3.1 General assumptions
176(1)
5.3.2 Schematic diagram of MoNP
177(2)
5.4 Development of MoNP
179(22)
5.4.1 Sub-model A-Ca2+/CaM complex formation
179(1)
5.4.2 Sub-model B-PKA activation pathway
180(8)
5.4.3 Sub-model C-PP2B activation pathway
188(2)
5.4.4 Sub-model D-PP1 activation pathway
190(2)
5.4.5 Sub-model E-CaMKII activation and autophosphorylation
192(2)
5.4.6 The complete model of MoNP
194(7)
5.5 Computational Procedures
201(9)
5.5.1 Simulating the sub-models
201(1)
5.5.2 Simulating MoNP
202(1)
5.5.3 Development of indices for the effects of kinases and phosphatases
202(2)
5.5.4 Theoretical conditions for the bidirectionality
204(1)
5.5.5 GSA
204(6)
5.6 Parameter Estimation and Validation of MoNP
210(12)
5.6.1 Kinetic characteristics of essential modulators
210(5)
5.6.2 Parameter estimation
215(2)
5.6.3 Validating the sub-models
217(3)
5.6.4 Validation against previous models
220(2)
5.7 Evaluating the Sub-models
222(4)
5.7.1 Sub-model B: Switch behaviour of PKA
223(1)
5.7.2 Sub-model C: Two levels of PP2B activation
224(1)
5.7.3 Sub-model D: Competition on PP1 activation
225(1)
5.8 Understanding Synaptic Plasticity Based on deltaKBI and deltaI1P
226(9)
5.8.1 Effective time scales for the transient activation of modulators
229(3)
5.8.2 Computational study on removal of PKA by PP2B
232(2)
5.8.3 Summary
234(1)
5.9 Significant Factors Revealed by GSA
235(10)
5.9.1 Summary of key findings from GSA
236(1)
5.9.2 Ca2+/CaM complex formation
237(8)
References
245(10)
Chapter 6 Uncertainty Quantification of Models Related to Synaptic Plasticity 255(24)
6.1 A Summary of STM and ESTM Models
258(3)
6.2 Nature of Inputs
261(1)
6.3 Global Sensitivity Analysis (GSA) of STM and ESTM
262(3)
6.3.1 Monotonicity plots
264(1)
6.4 Interpretation of PRCC Results
265(3)
6.4.1 STM model
265(2)
6.4.2 ESTM
267(1)
6.5 A Summary of the Possible Simplifications of STM based on GSA and Biological Reasoning
268(3)
6.6 Summary
271(1)
References
272(7)
Chapter 7 Synaptic Plasticity in Dementia: Alzheimer's Disease and Role of Calcium 279(44)
7.1 Introduction
279(3)
7.2 NMDAR-Mediated Ca2+ Transients
282(4)
7.3 Amyloid Hypothesis
286(3)
7.4 Ca2+ Hypothesis
289(2)
7.5 Dysregulation on NMDAR in AD
291(2)
7.6 Dysregulation of ER Ca2+ Handling in AD
293(4)
7.7 ER Alteration may Influence Abeta Production
297(2)
7.8 Modelling Ca2+ Dynamics in Dendritic Spines
299(5)
7.9 Modelling Intracellular Signalling Related to AD
304(2)
References
306(17)
Index 323