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Preface xi
Terminology xiii
List of Symbols and Acronyms xv
1 Introduction and Literature Survey 1
1.1 Introduction 1
1.2 Computational Processes Based on Biological Principles 2
1.2.1 Modeling Biological Processes 2
1.2.2 Artificial Neural Networks 2
1.3 Molecular and Biomolecular Electronics 4
1.3.1 Motivation 4
1.3.2 Molecular Electronics 5
1.3.3 Biomolecular Electronics 5
1.4 Biochemical Devices Based on Enzymic Reactions 6
1.5 Oscillations in Biochemical Systems 23
1.6 Kinetic Characteristics of Cyclic Enzyme Systems 23
2 Background and Goals of This Study 27
3 Materials and Methods 31
3.1 Materials 31
3.2 Instruments 35
3.3 Experimental Methods 35
3.3.1 Determination of Kinetic Constants 35
3.3.2 Determination of the Inhibition Constant for Inhibition of Glutathione Reductase by Glucose-6-Phosphate 40
3.3.3 Immobilization on Affi-Gel 10 40
3.3.4 Assay for Glucose-6-Phosphate Dehydrogenase 41
3.3.5 Assay for Glutathione Reductase 41
3.4 Computational Methods 42
4 Results 43
4.1 The Basic System: Theoretical Considerations and Results 43
4.1.1 Characteristics of the Basic System 43
4.1.2 The Basic System as an Information-Processing Unit 44
4.1.3 Analytical Models for the Basic System 45
4.1.4 Results of Numerical Simulations for the Basic System 53
4.2 Neural Network-Type Biochemical Systems for Information Processing 78
4.2.1 Network A 80
4.2.2 Network B 86
4.2.3 Network C 93
4.3 The Basic System: Experimental Results 97
4.3.1 Deciding on the Experimental System 98
4.3.2 Kinetic Study of the Experimental System 99
4.3.3 Control of the Input Signal 102
4.3.4 The Basic System in a Fed-Batch Reactor103
4.3.5 Internal Inhibition in the Basic System 05
4.3.6 Prediction of the Analytical Model Considering Internal Inhibition in a Fed-Batch Reactor 107
4.3.7 Immobilization'ofG6PDH and GR 111
4.3.8 The Basic System in a Packed Bed Reactor 112
4.4 The Extended Basic System: Theoretical Considerations and Results 115
4.4.1 Characteristics of the Extended Basic System 115
4.4.2 The Extended Basic System as an Information-Processing Unit 115
4.4.3 Analytical Model for the Extended Basic System 116
4.4.4 Results of Numerical Simulations for the Extended Basic System 117
5 Discussion 125
5.1 The Basic System 125
5.1.1 Fed-Batch Reactor: Numerical Simulations 126
5.1.2 Continuous Reactor: Numerical Simulations 126
5.1.3 Assessment of Experimental Results 126
5.2 The Extended Basic System 127
5.3 Biochemical Networks 127
5.4 Comparing Artificial Neural Networks with Biochemical Networks 129
5.5 Comparing Biochemical Networks to Computational Models 129
6 Conclusions 135
References 137
Index 147
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Jesica Stepp
reviewed Information Processing by Biochemical Systems : Neural Network-Type Configurations on December 01, 2019
Information Processing by Biochemical Systems : Neural Network-Type Configurations
This book grew out of a set of course notes for a neural networks module given as part of a Masters degree in "Intelligent Systems". The people on this course came from a wide variety of intellectual backgrounds (from philosophy, through psychology to computer science and engineering) and the author knew that he could not count on their being able to come to grips with the largely technical and mathematical approach which is often used (and in some ways easier to do). As a result he was forced to look carefully at the basic conceptual principles at work in the subject and try to recast these using ordinary language, drawing on the use of physical metaphors or analogies, and pictorial or graphical representations. I was pleasantly surprised to find that, as a result of this process, my own understanding was considerably deepened; I had now to unravel, as it were, condensed formal descriptions and say exactly how these were related to the "physical" world of artificial neurons, signals, computational processes, etc. However, the author was acutely aware that, while a litany of equations does not constitute a full description of fundamental principles, without some mathematics, a purely descriptive account runs the risk of dealing only with approximations and cannot be sharpened up to give any formulaic prescriptions. Therefore, the book introduced what author believed was just sufficient mathematics to bring the basic ideas into sharp focus.
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Add Information Processing by Biochemical Systems : Neural Network-Type Configurations, A Research-Driven Resource on Building Biochemical Systems to Perform Information Processing Functions Information Processing by Biochemical Systems describes fully delineated biochemical systems, organized as neural network–type assemblies. , Information Processing by Biochemical Systems : Neural Network-Type Configurations to the inventory that you are selling on WonderClubX
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Add Information Processing by Biochemical Systems : Neural Network-Type Configurations, A Research-Driven Resource on Building Biochemical Systems to Perform Information Processing Functions Information Processing by Biochemical Systems describes fully delineated biochemical systems, organized as neural network–type assemblies. , Information Processing by Biochemical Systems : Neural Network-Type Configurations to your collection on WonderClub |