Preface -- Overview of the Book -- Why are we putting these two domains together? -- Contents -- CHAPTER 1 A MULTI-DISCIPLINARY SURVEY OF BIOCOMPUTING: 1. MOLECULAR AND CELLULAR LEVELS* -- 1. Introduction -- 2. Lock-Key Paradigm versus Switch-Based Processing -- 3. Absolute versus Relative Determinism -- 4. Nested Hierarchy of Biocomputing Dynamics -- 5. Membrane as a Mesoscopic Substrate -- 5.1. Localized and delocalized potentials in biomembranes -- 5.2. Role of membrane fluidity in the mesoscopic dynamics -- 5.3. Electrostatic interactions as a molecular switching mechanism -- 5.4. Lateral mobility of protons on membrane surfaces: the "Pacific Ocean" effect -- 5.5. Role and specificity of phospholipid polar head-groups -- 5.6. Effect of transmembrane diffusion potentials and compartmentalization -- 5.7. Vesicular transport, exocytosis and synaptic transmission -- 6. Shape-Based Molecular Recognition -- 6.1. Role of short-range non-covalent bond interactions in molecular recognition -- 6.2. Molecular recognition between ferredoxin and FNR -- 6.3. Comparison of plastocyanin and cytochrome c6 -- 6.4. Molecular recognition of transducin and arrestin -- 6.5. Electronic-conformational interactions -- 7. Intracellular and Intramolecular Dynamics -- 7.1. Electrostatic interactions between a small molecule and a macromolecule -- 7.2. Effect of phosphorylation -- 7.3. Concept of intelligent materials -- 7.4. Concept of calcium-concentration microdomain -- 7.5. Errors, gradualism and evolution -- 7.6. Protein folding -- 8. Stochastic Nature of Neural Events: Controlled Randomness of Macroscopic Dynamics -- 9. Long-Term Potentiation and Synaptic Plasticity -- 10. Role of Dendrites in Information Processing -- 11. Efficiency of Biocomputing -- 12. General Discussion and Conclusion -- Acknowledgments -- References.

CHAPTER 2 A MULTI-DISCIPLINARY SURVEY OF BIOCOMPUTING: 2. SYSTEMS AND EVOLUTIONARY LEVELS, AND TECHNOLOGICAL APPLICATIONS* -- 1. Introduction -- 2. Background -- 2.1. Key conclusions of Part 1 -- 2.2. Element of non-equilibrium thermodynamics -- 2.3. Element of cellular automata -- 2.4. Element of nonlinear dynamic analysis -- 3. Biocomputing at the Evolutionary Level -- 3.1. Is evolution deterministic? -- 3.2. Explanatory power of evolution -- 3.3. Evolution as problem solving -- 3.4. Random search, exhaustive search and heuristic search -- 3.5. Enigma of homochirality of biomolecules -- 3.6. Damage control and opportunistic invention -- 3.7. Analogues and homologues -- 3.8. Co-evolution and perpetual novelty -- 3.9. Punctuated equilibrium and Cambrian explosion -- 4. Cognitive Aspects of Biocomputing -- 4.1. Models of creative problem solving -- 4.2. Parallel processing versus sequential processing in pattern recognition -- 4.3. Random search versus heuristic search -- 4.4. Dogmatism and self-imposed constraint -- 4.5. Retention phase: the need of sequential verification -- 4.6. Picture-based reasoning versus rule-based reasoning in pattern recognition -- 4.7. Advantages and disadvantages of rule-based reasoning -- 4.8. Contemporary interpretation of Freud's concept of the unconscious and Poincaré's introspective account -- 4.9. Interpretation of hypnagogia and serendipity -- 4.10. Gray scale of understanding and interpretation of intuition and "aha" experience -- 4.11. Pseudo-parallel processing -- 4.12. Need of conceptualization and structured knowledge -- 4.13. Koestler's bisociation versus Medawar's hypothetico-deduction scheme -- 4.14. Behaviorism versus cognitivism -- 4.15. Cerebral lateralization -- 4.16. Innovation versus imitation: gray scale of creativity -- 4.17. Elements of anticipation and notion of planning ahead.

4.18. Intelligence of nonhuman animals: planning ahead, versatility and language capability -- 4.19. Multiple intelligences: role of working memory -- 4.20. Creativity in music, art and literary works -- 4.21. Complex and interacting factors in the creative process: role of motivation, hard work and intelligence -- 4.22. Education and training: present educational problem -- 4.23. Substituted targets and goals in social engineering -- 4.24. Cognitive development: nature versus nurture -- 4.25. Is the crisis in the U.S. science education false? -- 4.26. Simulations of Gestalt phenomena in creativity -- 5. Consciousness and Free Will -- 5.1. Consciousness -- 5.2. Controversy of the free will problem -- 5.3. Conflict between free will and classical determinism -- 5.4. One-to-one versus one-to-many temporal mapping -- 5.5. Compatibilists versus incompatibilists -- 5.6. Randomness and determinism in microscopic dynamics -- 5.7. Randomness and determinism in mesoscopic and macroscopic dynamics -- 5.8. Endogenous noise -- 5.9. "Controlled" randomness in a hierarchical biocomputing system -- 5.10. Impossibility of proving or disproving the existence of free will -- 5.11. Quantum indeterminacy at the biological level -- 5.12. Microscopic reversibility and physical determinism -- 5.13. Incompatibility of microscopic reversibility and macroscopic irreversibility -- 5.14. Origin of macroscopic irreversibility -- 5.15. Enigmas of alternativism, intelligibility and origination -- 5.16. Laplace's "hidden cause" argument -- 5.17. Physical determinism and cosmology -- 5.18. Free will and simulations of consciousness -- 5.19. Critique of the new-mysterian view -- 5.20. Readiness potential and subjective feeling of volition -- 6. Digression on Philosophy and Sociology of Science -- 6.1. Falsifiability and non-uniqueness of scientific theories.

6.2. Rise of postmodernism -- 6.3. Gauch's analysis -- 6.4. Fallibility of falsification -- 6.5. Science of conjecture -- 6.6. Role of subjectivity in creative problem solving and value judgment -- 6.7. Critiques of science fundamentalism and postmodernism -- 6.8. Level of confidence in scientific knowledge -- 6.9. Sociological aspects of science -- 6.10. Logical inconsistencies of antirealism -- 6.11. Objective knowledge: Popper's third world -- 6.12. Method of implicit falsification: Is psychoanalysis unscientific? -- 6.13. Life itself: epistemological considerations -- 6.14. Unity of knowledge or great divide: the case of Harris versus Edwards -- 7. Technological Applications -- 7.1. Expert systems in artificial intelligence -- 7.2. Neural network computing -- 7.3. Animat path to artificial intelligence -- 7.4. Agent technology -- 7.5. Neuromolecular brain model: multi-level neural network -- 7.6. Embryonics: evolvable hardware -- 7.7. A successful example of molecular computing: solving the direct Hamiltonian path problem -- 7.8. Prospects of molecular electronics in biocomputing -- 8. General Discussion and Conclusion -- Acknowledgments -- References -- CHAPTER 3 MODELS FOR COMPLEX EUKARYOTIC REGULATORY DNA SEQUENCES -- 1. Introduction -- 2. Some Biology of Transcription Regulation -- 2.1. The Basal Transcription Machinery -- 2.2. Chromatin Structure in Regulatory Regions -- 2.3. Specific Gene Regulation: Sequence Elements and Transcription Factors -- 3. Core Promoter Recognition -- 3.1. Ab initio Prediction -- 3.2. Alignment Approaches -- 4. Prediction of Regulatory Regions by Cross-species Conservation -- 5. Searching for Motif Clusters -- 6. Perspective -- Acknowledgements -- References -- CHAPTER 4 AN ALGORITHM FOR AB-INITIO DNA MOTIF DETECTION -- 1. Introduction -- 2. Algorithm -- 3. Experiments -- References.

CHAPTER 5 DETECTING MOLECULAR EVIDENCE OF POSITIVE DARWINIAN SELECTION -- 1. Introduction -- 1.1. Molecular Evolution Research in a Time of Genomes -- 1.2. Some Examples -- 1.3. Chapter Overview -- 2. Types of Adaptive Evolution -- 2.1. Episodic Positive Selection -- 2.2. Diversifying Selection - The Biological Arms Races -- 3. The Neutral Theory of Molecular Evolution -- 3.1. Cost of Natural Selection -- 3.2. Recent Tests of the Neutral Theory -- 3.3. Detecting Departures from Neutrality -- 4. Selective Sweeps and Genetic Hitchhiking -- 4.1. Detecting Selective Sweeps -- 4.2. Correlation Between Local Recombination Rates and Diversity -- 4.3. Distinguishing Complex Demographic Histories or Background Selection from Positive Selection -- 5. Codon-based Methods to Detect Positive Selection -- 5.1. Counting Methods -- 5.2. Probabilistic Methods -- 5.3. Comparison of Counting and Probabilistic Approaches to Comparative Methods -- 5.4. Codon Volatility -- 5.5. Codon-based Methods that use Polymorphism Data -- 6. Discussion and Future Prospects -- References -- CHAPTER 6 MOLECULAR PHYLOGENETIC ANALYSIS: UNDERSTANDING GENOME EVOLUTION -- 1. What is Phylogenetics? -- 2. What is a Phylogenetic Tree? -- 3. Identifying Duplicate Genes -- 3.1. Generate Protein Families -- 3.2. Multiple Sequence Alignments -- 3.3. Reconstructing Phylogenetic Trees -- 4. Assessing the Accuracy of Phylogenetic Trees -- 5. High-throughput Screening of Tree Topologies -- 6. Concluding Remarks -- References -- CHAPTER 7 CONSTRUCTING BIOLOGICAL NETWORKS OF PROTEIN-PROTEIN INTERACTIONS -- 1. Introduction -- 2. Bioinformatic Approaches -- 2.1. Homology -- 2.2. Fusion events -- 2.3. Co-localization -- 2.4. Co-evolution -- 2.5. Literature mining -- 3. From Interactions to Networks -- 3.1. False negatives -- 3.2. False positives -- 4. Conclusion -- References.

CHAPTER 8 COMPUTATIONAL MODELLING OF GENE REGULATORY NETWORKS.