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The Many Facets of Natural Computing

The Many Facets of Natural Computing . Lila Kari Dept. of Computer Science University of Western Ontario London, ON, Canada http://www.csd.uwo.ca/~lila/ lila@csd.uwo.ca. Natural Computing. Investigates models and computational techniques inspired by nature

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The Many Facets of Natural Computing

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  1. The Many Facets of Natural Computing Lila Kari Dept. of Computer Science University of Western Ontario London, ON, Canada http://www.csd.uwo.ca/~lila/ lila@csd.uwo.ca

  2. Natural Computing • Investigates models and computational techniques inspired by nature • Attempts to understand the world around us in terms of information processing • Interdisciplinary field that connects computer sciences with natural sciences Lila Kari, University of Western Ontario

  3. Natural Computing • (i) Nature asInspiration • (ii) Nature asImplementation Substrate • (iii) Nature as Computation Lila Kari, University of Western Ontario

  4. (i) Nature as Inspiration • Cellular Automata – self-reproduction • Neural Computation – the brain • Evolutionary Computation – evolution • Swarm Intelligence – group behaviour • Immunocomputing– immune system • Artificial Life – properties oflife • Membrane Computing – cells and membranes • Amorphous Computing - morphogenesis Lila Kari, University of Western Ontario

  5. 1.Cellular Automata • Cellular automaton = dynamical system consisting of a regular grid of cells • Space and time and discrete • Each cell can be in a finite number of states • Each cell changes its state according to a list of transition rules, based on its current state and the states of its neighbours • The grid updates its configuration synchronously Lila Kari, University of Western Ontario

  6. CA Example: Rule 30 111 110 101 100 011 010 001 000 0 0 0 1 1 1 1 0 Lila Kari, University of Western Ontario

  7. Conus Textile pattern Lila Kari, University of Western Ontario

  8. 2.Neural Computation • Artificial Neural Network: a network of interconnected artificial neurons • NeuronA : * n real- valued inputs x1,…, xn * weights w1,…,wn * computes fA(w1x1 + w2x2 + …+ wnxn) • Network Function = vectorial function that, forninput values, associates the outputs of the m pre-selected output neurons Lila Kari, University of Western Ontario

  9. Applications to Human Cognition[T.Schultz,www.psych.mcgill.ca/labs/lnsc] Lila Kari, University of Western Ontario

  10. 3.Evolutionary Computation • Constant or variable-sized population • A fitness criterion according to which individuals are evaluated • Genetically inspired operators (mutation or recombination of parents) that produce the next generation from the current one Lila Kari, University of Western Ontario

  11. Genetic Algorithms • Individuals = fixed-length bit strings • Mutation = cut-and-paste of a prefix of a parent with a suffix of another • Fitness function is problem-dependent • If initial population encodes possible solutions to a given problem, then the system evolves to produce a near-optimal solution to the problem • Applications: real-valued parameter optimization Lila Kari, University of Western Ontario

  12. Using Genetic Algorithms to Create Evolutionary Art [M.Gold] Lila Kari, University of Western Ontario

  13. 4.Swarm Intelligence • Swarm: group of mobile biological organisms (bacteria, ants, bees, fish, birds) • Each individual communicates with others either directly or indirectly by acting on its environment • These interactions contribute to collective problem solving = collective intelligence Lila Kari, University of Western Ontario

  14. Particle Swarm Optimization • Inspired by flocking behaviour of birds • Start with a swarm of particles (each representing a potential solution) • Particles move through a multidimensional space and positions are updated based on * previous own velocity * tendency towards personal best * tendency toward neighbourhood best Lila Kari, University of Western Ontario

  15. Ant Algorithms • Model the foraging behaviour of ants • In finding the best path between nest and a source of food, ants rely on indirect communication by laying a pheromone trail on the way back (if food is found) and by following concentration of pheromones (if food is sought) Lila Kari, University of Western Ontario

  16. Lila Kari, University of Western Ontario

  17. 5.Immunocomputing • Immune system’s function = protect our bodies against external pathogens • Role of immune system: recognize cells and categorize them as self or non-self • Innate (non-specific) immune system • Adaptive (acquired) immune system Lila Kari, University of Western Ontario

  18. Artificial Immune Systems • Computational aspects of the immune system: distinguishing self from non-self, feature extraction, learning, immunological memory, self-regulation, fault-tolerance • Applications: computer virus detection, anomaly detection in a time-series of data, fault diagnosis, pattern recognition Lila Kari, University of Western Ontario

  19. 6.Artificial Life • ALife attempts to understand the very essence of what it means to be alive • Builds ab initio, within in silico computers, artificial systems that exhibit properties normally associated only with living organisms Lila Kari, University of Western Ontario

  20. Lindenmayer Systems • Parallel rewriting systems • Start with an initial word • Apply the rewriting rules in parallel to all letters of the word • Used, e.g., for modelling of plant growth and morphogenesis Lila Kari, University of Western Ontario

  21. L-Systems Applications • Plant growth [Fuhrer, Wann Jensen, Prusinkiewicz 2004-05] • Architecture and design [J.Bailey, Archimorph] Lila Kari, University of Western Ontario

  22. Mechanical Artificial Life • Evolving populations of artificial creatures in simulated environments • Combining the computational and experimental approaches and using rapid manufacturing technology to fabricate physical evolved robots that were selected for certain abilities (to walk or get a cube) Lila Kari, University of Western Ontario

  23. How to insert pdf file Lila Kari, University of Western Ontario

  24. 7.Membrane Computing • Inspired by the compartmentalized internal structure of cells • Membrane System = a nested hierarchical structure of regions delimited by “membranes” • Each region contains objects and transformation rules+transfer rules Lila Kari, University of Western Ontario

  25. 8.Amorphous Computing • Inspired by developmental biology • Consist of a multitude of irregularly placed, asynchronous, locally interacting computing elements • The identically programmed “computational particles” communicate only with others situated within a small radius • Goal: engineer specified coherent computational behaviour from the interaction of large quantities of such unreliable computational particles. Lila Kari, University of Western Ontario

  26. Amorphous Computing[Generating patterns: Abelson, Sussman, Knight, Ragpal] Lila Kari, University of Western Ontario

  27. (ii) Nature as Implementation Substrate • Molecular Computing (DNA Computing) Uses biomolecules, e.g., DNA, RNA • Quantum Computing Uses, e.g., ion traps, superconductors, nuclear magnetic resonance Lila Kari, University of Western Ontario

  28. (ii-1) Molecular Computing • Data can be encoded as biomolecules (DNA, RNA) • Arithmetic/logic operations are performed bymolecular biology tools • The proof-of-principle experiment was Adleman’s bio-algorithm solving a Hamiltonian Path Problem (1994) Lila Kari, University of Western Ontario

  29. Molecular (DNA) Computing • Single-stranded DNA is a string over the four-letter alphabet, {A, C, G, T} Lila Kari, University of Western Ontario

  30. Power of DNA Computing Data: DNA single and double strands • Watson–Crick Complementarity: W(C) = G, W(A) = T • Bio-operations: cut-and-paste by enzymes, extraction by pattern, copy, read-out • R.Freund, L.Kari, G.Paun. DNA computing based on splicing: the existence of universal computers.Theory of Computing Systems, 32 (1999). Lila Kari, University of Western Ontario

  31. DNA-Encoded Information • DNA strands interact with each other in programmed but also undesirable ways • The information has no fixed location • The results of a biocomputation are not deterministic, as they depend e.g. on concentration of populations of DNA strands, diffusion reactions, statistical laws Lila Kari, University of Western Ontario

  32. DNA-Motivated Concepts • θ-periodicity w = u1u2…unwhere ui is in {u, θ(u)} and θ is an antimorphic involution • Generalize Lyndon-Schutzenberger u^n v^m = w^m • θ-prefix, θ-infix, θ-compliant codes Lila Kari, University of Western Ontario

  33. Our DNA Information Research • L. Kari, S. Seki, On pseudoknot-bordered words and their properties, Journal of Computer and System Sciences, (2008) • L.Kari, K.Mahalingam, Watson-Crick Conjugate and Commutative Words, Proc. DNA Computing 13, LNCS 4848 (2008) • L. Kari, K. Mahalingam, S. Seki, Twin-roots of words and their properties, Theoretical Computer Science (2008) • E.Czeizler, L.Kari, S.Seki. On a Special Class of Primitive Words. MFCS (2008) • M. Ito, L. Kari, Z. Kincaid, S. Seki, Duplication in DNA sequences. Proc. of Developments in Language Theory (2008) Lila Kari, University of Western Ontario

  34. Computing by Self-Assembly • Self-Assembly = The process by which objects autonomously come together to form complex structures • Examples • Atoms bind by chemical bonds to form molecules • Molecules may form crystals or macromolecules • Cells interact to form organisms Lila Kari, University of Western Ontario

  35. Motivation for Self-Assembly Nanotechnology:miniaturization in medicine, electronics, engineering, material science, manufacturing • Top-Down techniques: lithography (inefficient in creating structures with size of molecules or atoms) • Bottom-Up techniques: self-assembly Lila Kari, University of Western Ontario

  36. Computing by Self-Assemblyof Tiles • Tile = square with the edges labelled from a finite alphabet of glues • Tiles cannot be rotated • Two adjacent tiles on the plane stick if they have the same glue at the touching edges Lila Kari, University of Western Ontario

  37. Computation by DNA Self-Assembly[Mao, LaBean, Reif, , Seeman, Nature, 2000] Lila Kari, University of Western Ontario

  38. Our Self-Assembly Research • L.Adleman, J.Kari, L.Kari, D.Reishus, P.Sosik. The Undecidability of the Infinite Ribbon Problem: Implications for Computing by Self-Assembly (SIAM Journal of Computing, to appear, 2009) • This solves an open problem formerly known as the “unlimited infinite snake problem” • Undecidability of existence of arbitrarily large supertiles that can self-assemble from a given tile set (starting from an arbitrary “seed”) • E.Czeizler, L.Kari, Geometrical tile design for complex neighbourhoods (2008, submitted) • L.Kari, B.Masson, Simulating arbitrary neighbourhoods by polyominoes (2008, in preparation) Lila Kari, University of Western Ontario

  39. DNA Clonable Octahedron[Shih, Joyce, Nature, 2004] Lila Kari, University of Western Ontario

  40. Nanoscale DNA Tetrahedra[Goodman, Turberfield, Science, 2005] Lila Kari, University of Western Ontario

  41. DNA Origami[Rothemund, Nature, 2006] Lila Kari, University of Western Ontario

  42. (ii-2) Quantum Computing • A qubit can hold a “0”, a “1” or a quantum superposition of these • Quantum mechanical phenomena such as superposition and entanglement are used to perform operations on qubits • Shor’s quantum algorithm for factoring integers (1994) Lila Kari, University of Western Ontario

  43. Quantum Crytography • “Unbreakable encryption unveiled” (BBC News, Oct 2008) • “Perfect secrecy has come a step closer with the launch of the world's first computer network protected by unbreakable quantum encryption.” • The network connects six locations across Vienna and in the nearby town of St Poelten, using 200 km of standard commercial fibre optic cables. Lila Kari, University of Western Ontario

  44. (iii) Nature as Computation Understand nature by viewing natural processes as information processing • Systems Biology • Synthetic Biology • Cellular Computing Lila Kari, University of Western Ontario

  45. (iii-1) Systems Biology • Attempt to understand complex interactions in biological systems by taking a systemic approach and focusing on the interaction networks themselves and on the properties that arise because of these interactions *gene regulatory networks * protein-protein interaction networks * transport networks Lila Kari, University of Western Ontario

  46. The Genomic Computer[Istrail, De Leon, Davidson, 2007] • Molecular transport replaces wires • Causal coordination replaces imposed temporal synchrony • Changeable architecture replaces rigid structure • Communication channels are formed on an as-needed basis • Very large scale • Robustness is achieved by rigorous selection Lila Kari, University of Western Ontario

  47. (iii-2) Synthetic Biology • TIMES best inventions 2008 : #21 The Synthetic Organism[C.Venter et al.] • Generate a synthetic genome (5,386bp) of a virus by self-assembly of chemically synthesized short DNA strands Lila Kari, University of Western Ontario

  48. (iii-3) Cellular Computing Computation in living cells: ciliated protozoa Lila Kari, University of Western Ontario

  49. Ciliates: Gene Rearrangement Lila Kari, University of Western Ontario Photo courtesy of L.F. Landweber

  50. Our Cellular Computing Research • L.Landweber, L.Kari. The evolution of cellular computing: nature's solution to a computational problem.Biosystems 52(1999) • L.Kari, L.F.Landweber. Computational power of gene rearrangement.Proc. DNA Computing 5, DIMACS Series, 54(2000) • L.Kari, J.Kari, L.Landweber. Reversible molecular computation in ciliates. In Jewels are Forever, Springer-Verlag (1999) Lila Kari, University of Western Ontario

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