Information Processing and Living Systems

1860945635......Page 1
Preface......Page 6
Overview of the Book......Page 7
Why are we putting these two domains together?......Page 8
Contents......Page 10
CHAPTER 1 A MULTI-DISCIPLINARY SURVEY OF BIOCOMPUTING: 1. MOLECULAR AND CELLULAR LEVELS*......Page 22
1. Introduction......Page 23
2. Lock-Key Paradigm versus Switch-Based Processing......Page 24
3. Absolute versus Relative Determinism......Page 31
4. Nested Hierarchy of Biocomputing Dynamics......Page 34
5. Membrane as a Mesoscopic Substrate......Page 37
5.1. Localized and delocalized potentials in biomembranes......Page 38
5.2. Role of membrane fluidity in the mesoscopic dynamics......Page 45
5.3. Electrostatic interactions as a molecular switching mechanism......Page 50
5.4. Lateral mobility of protons on membrane surfaces: the “Pacific Ocean” effect......Page 55
5.5. Role and specificity of phospholipid polar head-groups......Page 57
5.6. Effect of transmembrane diffusion potentials and compartmentalization......Page 60
5.7. Vesicular transport, exocytosis and synaptic transmission......Page 61
6. Shape-Based Molecular Recognition......Page 63
6.1. Role of short-range non-covalent bond interactions in molecular recognition......Page 64
6.2. Molecular recognition between ferredoxin and FNR......Page 70
6.3. Comparison of plastocyanin and cytochrome c6......Page 72
6.4. Molecular recognition of transducin and arrestin......Page 75
6.5. Electronic-conformational interactions......Page 81
7. Intracellular and Intramolecular Dynamics......Page 82
7.1. Electrostatic interactions between a small molecule and a macromolecule......Page 83
7.2. Effect of phosphorylation......Page 85
7.3. Concept of intelligent materials......Page 88
7.4. Concept of calcium-concentration microdomain......Page 96
7.5. Errors, gradualism and evolution......Page 98
7.6. Protein folding......Page 101
8. Stochastic Nature of Neural Events: Controlled Randomness of Macroscopic Dynamics......Page 109
9. Long-Term Potentiation and Synaptic Plasticity......Page 121
10. Role of Dendrites in Information Processing......Page 124
11. Efficiency of Biocomputing......Page 126
12. General Discussion and Conclusion......Page 131
References......Page 136
CHAPTER 2 A MULTI-DISCIPLINARY SURVEY OF BIOCOMPUTING: 2. SYSTEMS AND EVOLUTIONARY LEVELS, AND TECHNOLOGICAL APPLICATIONS*......Page 162
1. Introduction......Page 163
2.1. Key conclusions of Part 1......Page 168
2.2. Element of non-equilibrium thermodynamics......Page 169
2.3. Element of cellular automata......Page 170
2.4. Element of nonlinear dynamic analysis......Page 172
3.1. Is evolution deterministic?......Page 174
3.2. Explanatory power of evolution......Page 176
3.3. Evolution as problem solving......Page 177
3.4. Random search, exhaustive search and heuristic search......Page 178
3.5. Enigma of homochirality of biomolecules......Page 179
3.6. Damage control and opportunistic invention......Page 181
3.7. Analogues and homologues......Page 184
3.8. Co-evolution and perpetual novelty......Page 185
3.9. Punctuated equilibrium and Cambrian explosion......Page 186
4.1. Models of creative problem solving......Page 187
4.2. Parallel processing versus sequential processing in pattern recognition......Page 191
4.3. Random search versus heuristic search......Page 196
4.4. Dogmatism and self-imposed constraint......Page 198
4.5. Retention phase: the need of sequential verification......Page 200
4.6. Picture-based reasoning versus rule-based reasoning in pattern recognition......Page 202
4.7. Advantages and disadvantages of rule-based reasoning......Page 204
4.8. Contemporary interpretation of Freud’s concept of the unconscious and Poincaré’s introspective account......Page 212
4.9. Interpretation of hypnagogia and serendipity......Page 226
4.10. Gray scale of understanding and interpretation of intuition and “aha” experience......Page 236
4.11. Pseudo-parallel processing......Page 247
4.12. Need of conceptualization and structured knowledge......Page 250
4.13. Koestler’s bisociation versus Medawar’s hypothetico-deduction scheme......Page 252
4.14. Behaviorism versus cognitivism......Page 255
4.15. Cerebral lateralization......Page 257
4.16. Innovation versus imitation: gray scale of creativity......Page 262
4.17. Elements of anticipation and notion of planning ahead......Page 265
4.18. Intelligence of nonhuman animals: planning ahead, versatility and language capability......Page 269
4.19. Multiple intelligences: role of working memory......Page 277
4.20. Creativity in music, art and literary works......Page 282
4.21. Complex and interacting factors in the creative process: role of motivation, hard work and intelligence......Page 295
4.22. Education and training: present educational problem......Page 305
4.23. Substituted targets and goals in social engineering......Page 318
4.24. Cognitive development: nature versus nurture......Page 321
4.25. Is the crisis in the U.S. science education false?......Page 327
4.26. Simulations of Gestalt phenomena in creativity......Page 332
5. Consciousness and Free Will......Page 346
5.1. Consciousness......Page 347
5.2. Controversy of the free will problem......Page 349
5.3. Conflict between free will and classical determinism......Page 351
5.4. One-to-one versus one-to-many temporal mapping......Page 353
5.5. Compatibilists versus incompatibilists......Page 356
5.6. Randomness and determinism in microscopic dynamics......Page 359
5.7. Randomness and determinism in mesoscopic and macroscopic dynamics......Page 362
5.8. Endogenous noise......Page 364
5.9. “Controlled” randomness in a hierarchical biocomputing system......Page 375
5.10. Impossibility of proving or disproving the existence of free will......Page 376
5.11. Quantum indeterminacy at the biological level......Page 377
5.12. Microscopic reversibility and physical determinism......Page 379
5.13. Incompatibility of microscopic reversibility and macroscopic irreversibility......Page 384
5.14. Origin of macroscopic irreversibility......Page 397
5.15. Enigmas of alternativism, intelligibility and origination......Page 407
5.16. Laplace’s “hidden cause” argument......Page 415
5.17. Physical determinism and cosmology......Page 418
5.18. Free will and simulations of consciousness......Page 420
5.19. Critique of the new-mysterian view......Page 424
5.20. Readiness potential and subjective feeling of volition......Page 434
6.1. Falsifiability and non-uniqueness of scientific theories......Page 440
6.2. Rise of postmodernism......Page 444
6.3. Gauch’s analysis......Page 445
6.4. Fallibility of falsification......Page 446
6.5. Science of conjecture......Page 448
6.6. Role of subjectivity in creative problem solving and value judgment......Page 456
6.7. Critiques of science fundamentalism and postmodernism......Page 463
6.8. Level of confidence in scientific knowledge......Page 467
6.9. Sociological aspects of science......Page 468
6.10. Logical inconsistencies of antirealism......Page 469
6.12. Method of implicit falsification: Is psychoanalysis unscientific?......Page 470
6.13. Life itself: epistemological considerations......Page 472
6.14. Unity of knowledge or great divide: the case of Harris versus Edwards......Page 496
7.1. Expert systems in artificial intelligence......Page 502
7.2. Neural network computing......Page 504
7.3. Animat path to artificial intelligence......Page 509
7.4. Agent technology......Page 510
7.5. Neuromolecular brain model: multi-level neural network......Page 511
7.6. Embryonics: evolvable hardware......Page 514
7.7. A successful example of molecular computing: solving the direct Hamiltonian path problem......Page 515
7.8. Prospects of molecular electronics in biocomputing......Page 516
8. General Discussion and Conclusion......Page 520
Acknowledgments......Page 552
References......Page 553
1. Introduction......Page 596
2.1. The Basal Transcription Machinery......Page 597
2.2. Chromatin Structure in Regulatory Regions......Page 600
2.3. Specific Gene Regulation: Sequence Elements and Transcription Factors......Page 602
3.1. Ab initio Prediction......Page 606
3.2. Alignment Approaches......Page 613
4. Prediction of Regulatory Regions by Cross-species Conservation......Page 614
5. Searching for Motif Clusters......Page 617
6. Perspective......Page 620
References......Page 623
1. Introduction......Page 632
2. Algorithm......Page 633
3. Experiments......Page 634
References......Page 635
1. Introduction......Page 636
1.2. Some Examples......Page 637
1.3. Chapter Overview......Page 638
2.2. Diversifying Selection - The Biological Arms Races......Page 639
3.1. Cost of Natural Selection......Page 640
3.2. Recent Tests of the Neutral Theory......Page 641
3.3. Detecting Departures from Neutrality......Page 642
4. Selective Sweeps and Genetic Hitchhiking......Page 643
4.1. Detecting Selective Sweeps......Page 644
4.2. Correlation Between Local Recombination Rates and Diversity......Page 645
4.3. Distinguishing Complex Demographic Histories or Background Selection from Positive Selection......Page 646
5. Codon-based Methods to Detect Positive Selection......Page 647
5.1. Counting Methods......Page 649
5.2. Probabilistic Methods......Page 651
5.3. Comparison of Counting and Probabilistic Approaches to Comparative Methods......Page 656
5.4. Codon Volatility......Page 657
5.5. Codon-based Methods that use Polymorphism Data......Page 658
6. Discussion and Future Prospects......Page 659
References......Page 660
1. What is Phylogenetics?......Page 666
3. Identifying Duplicate Genes......Page 667
3.3. Reconstructing Phylogenetic Trees......Page 668
6. Concluding Remarks......Page 670
References......Page 671
1. Introduction......Page 674
2. Bioinformatic Approaches......Page 675
2.1. Homology......Page 676
2.2. Fusion events......Page 677
2.3. Co-localization......Page 678
2.4. Co-evolution......Page 680
2.5. Literature mining......Page 681
3. From Interactions to Networks......Page 685
3.1. False negatives......Page 686
3.2. False positives......Page 687
4. Conclusion......Page 689
References......Page 690
1. Introduction......Page 694
2. A Novel Approach......Page 695
3.1. Discrete-Time Approximation of First-Order Differential Equations......Page 697
3.2. State Space Representation......Page 698
3.4. Using GA for The Selection of Gene Subset for a GRN......Page 699
3.6. Procedure of the GA-Based Method for Gene Subset Selection......Page 700
4. Experiments and Results......Page 702
4.1. Building a Global GRN of the Whole Gene Set Out of the GRNs of Smaller Number of Genes (Putting the Pieces of the Puzzle Together)......Page 704
5. Conclusions......Page 705
References......Page 706
1. Introduction......Page 708
3. Scope and Nature of Text-Mining in Life-Sciences Domain......Page 710
3.2. Systems Aimed at Life-sciences Applications......Page 711
References......Page 713
CHAPTER 10 INTEGRATED PROGNOSTIC PROFILES: COMBINING CLINICAL AND GENE EXPRESSION INFORMATION THROUGH EVOLVING CONNECTIONIST APPROACH......Page 716
1. Introduction......Page 717
2. Methods......Page 718
2.3. Common Feature Set Selection......Page 719
2.4. Algorithm of Integrated Feature Selection......Page 720
3. Results......Page 722
3.1. Classification Accuracy Test and Profile Verification......Page 723
4.1. Discovering Genotype Phenotype Relationships Through Integrated Profiles......Page 725
6. Acknowledgement......Page 726
References......Page 727
1. Introduction......Page 730
2. Brief Overview of Common Databases Presenting General Information on Genes and Proteins......Page 732
3. Specialized Databases on Transcription Regulation......Page 733
3.1. TRANSFAC®......Page 734
3.3. SMARt DB - A Database on Scaffold / Matrix Attached Regions......Page 736
4.1. Regulatory Networks: General Properties and Peculiarities......Page 737
4.2. Variety of Databases on Protein Interactions and Signaling Networks......Page 738
4.3. TRANSPATH® - A Database on Signal Transduction Pathways......Page 740
5.1. Analysis of Promoters......Page 742
5.2. Identification of Key Nodes in Signaling Networks......Page 744
References......Page 745
1. Introduction......Page 750
3. Preprocessing......Page 751
4. Normalization......Page 753
5. Identification of Differentially Expressed Genes......Page 754
6. Validation Strategies......Page 755
7.2. Quantitative RT-PCR......Page 757
7.3. Mutant versus Wild Type......Page 758
7.4. Gene Spike-in Experiments......Page 759
8. Summary......Page 760
References......Page 761
1. Introduction......Page 762
2. Information Extraction from Dynamic Web Sources......Page 764
3.1. Wrapper Verification Methods......Page 766
3.2. Wrapper Reinduction Methods......Page 767
4. Conclusion......Page 769
References......Page 770
2. Signaling Pathways: A Prickly Proposition......Page 772
3. Challenges of Signaling Modeling......Page 775
Time cost of simulation.......Page 776
4. The Goals and Features of Cellware......Page 777
5. Concluding Remarks......Page 778
References......Page 780
1. Introduction......Page 782
2.2. Coding Genes......Page 783
2.4. Structure of Coding Genes......Page 785
2.6. Non-Coding Genes......Page 786
3. Genomes......Page 787
3.1. Computational Analysis of the Genome: Coding Gene Prediction......Page 788
3.2. Computational Analysis of the Genome: Non-coding Gene Prediction......Page 789
References......Page 790
1. Introduction......Page 792
2. Growing Need for Quality Control......Page 793
3.1. Content......Page 794
3.2. Availability......Page 795
3.3. Combining Different Metrics......Page 796
References......Page 797