Information Processing by Biochemical Systems describes fully delineated biochemical systems, organized as neural network--type assemblies. It explains the relationship between these two apparently unrelated fields, revealing how biochemical systems have the advantage of using the "language" of the physiological processes and, therefore, can be organized into the neural network--type assemblies, much in the way that natural biosystems are. A wealth of information is included concerning both the experimental aspects (such as materials and equipment used) and the computational procedures involved.
Preface. Terminology. List of Symbols and Acronyms. 1 Introduction
and Literature Survey. 1.1 Introduction. 1.2 Computational Processes Based
on Biological Principles. 1.2.1 Modeling Biological Processes. 1.2.2
Artificial Neural Networks. 1.3 Molecular and Biomolecular Electronics. 1.3.1
Motivation. 1.3.2 Molecular Electronics. 1.3.3 Biomolecular Electronics. 1.4
Biochemical Devices Based on Enzymic Reactions. 1.5 Oscillations in
Biochemical Systems. 1.6 Kinetic Characteristics of Cyclic Enzyme Systems. 2
Background and Goals of This Study. 3 Materials and Methods. 3.1
Materials. 3.2 Instruments. 3.3 Experimental Methods. 3.3.1 Determination of
Kinetic Constants. 3.3.2 Determination of the Inhibition Constant for
Inhibition of Glutathione Reductase by Glucose-6-Phosphate. 3.3.3
Immobilization on Affi-Gel
10. 3.3.4 Assay for Glucose-6-Phosphate
Dehydrogenase. 3.3.5 Assay for Glutathione Reductase. 3.4 Computational
Methods. 4 Results. 4.1 The Basic System: Theoretical Considerations and
Results. 4.1.1 Characteristics of the Basic System. 4.1.2 The Basic System as
an Information-Processing Unit. 4.1.3 Analytical Models for the Basic System.
4.1.4 Results of Numerical Simulations for the Basic System. 4.2 Neural
Network-Type Biochemical Systems for Information Processing. 4.2.1 Network A.
4.2.2 Network B. 4.2.3 Network C. 4.3 The Basic System: Experimental Results.
4.3.1 Deciding on the Experimental System. 4.3.2 Kinetic Study of the
Experimental System. 4.3.3 Control of the Input Signal. 4.3.4 The Basic
System in a Fed-Batch Reactor. 4.3.5 Internal Inhibition in the Basic System.
4.3.6 Prediction of the Analytical Model Considering Internal Inhibition in a
Fed-Batch Reactor. 4.3.7 Immobilization of G6PDH and GR. 4.3.8 The Basic
System in a Packed Bed Reactor. 4.4 The Extended Basic System: Theoretical
Considerations and Results. 4.4.1 Characteristics of the Extended Basic
System. 4.4.2 The Extended Basic System as an Information-Processing Unit.
4.4.3 Analytical Model for the Extended Basic System. 4.4.4 Results of
Numerical Simulations for the Extended Basic System. 5 Discussion. 5.1 The
Basic System. 5.1.1 Fed-Batch Reactor: Numerical Simulations. 5.1.2
Continuous Reactor: Numerical Simulations. 5.1.3 Assessment of Experimental
Results. 5.2 The Extended Basic System. 5.3 Biochemical Networks. 5.4
Comparing Artificial Neural Networks with Biochemical Networks. 5.5 Comparing
Biochemical Networks to Computational Models. 6 Conclusions. References.
Index.
ORNA FILO, PhD , has over ten years of experience in the medical equipment industry, developing various diagnostic imaging technologies. She held various positions in R&D and clinical affairs, including management of clinical studies, clinical support to R&D and marketing, development of clinical studies protocols, and data analysis. Dr. Filo currently owns a company that develops tools for assessment of spine deformities. NOAH LOTAN, PhD , is a chemical engineer by basic training. He was appointed Professor of Biomedical Engineering at the Technion - Israel Institute of Technology. Dr. Lotan has been a visiting scientist at universities in the United States, France, Germany, Italy, Switzerland, the United Kingdom, and Mexico.
