The Many Facets of Natural Computing

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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_at_csd.uwo.ca

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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

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Natural Computing

• (i) Nature as Inspiration
• (ii) Nature as Implementation Substrate
• (iii) Nature as Computation

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(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 of life
• Membrane Computing cells and membranes
• Amorphous Computing - morphogenesis

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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

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CA Example Rule 30

• 111 110 101 100 011 010 001 000
• 0 0 0 1 1 1 1
  0

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Conus Textile pattern
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2.Neural Computation

• Artificial Neural Network a network of
  interconnected artificial neurons
• Neuron A n real- valued inputs x1,,
  xn
• weights w1,,wn
• computes
• fA(w1x1 w2x2
  wnxn)
• Network Function vectorial function that,
• for n input values, associates the outputs of
  the m pre-selected output neurons

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Applications to Human Cognition T.Schultz,
www.psych.mcgill.ca/labs/lnsc
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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

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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

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Using Genetic Algorithms to Create Evolutionary
Art M.Gold
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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

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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

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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)

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5.Immunocomputing

• Immune systems 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

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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

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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

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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

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L-Systems Applications

• Plant growth Fuhrer, Wann Jensen, Prusinkiewicz
  2004-05
• Architecture and design J.Bailey, Archimorph

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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)

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• How to insert pdf file

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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

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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.

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Amorphous ComputingGenerating patterns
Abelson, Sussman, Knight, Ragpal
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(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

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(ii-1) Molecular Computing

• Data can be encoded as biomolecules (DNA, RNA)
• Arithmetic/logic operations are performed by
  molecular biology tools
• The proof-of-principle experiment was Adlemans
  bio-algorithm solving a Hamiltonian Path Problem
  (1994)

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Molecular (DNA) Computing

• Single-stranded DNA is a string over the
  four-letter alphabet, A, C, G, T

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Power of DNA Computing

• Data DNA single and double strands
  
• WatsonCrick 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).

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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

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DNA-Motivated Concepts

• ?-periodicity
• w u1u2un where ui is in u, ?(u)
• and ? is an antimorphic involution
• Generalize Lyndon-Schutzenberger
• un vm wm
• ?-prefix, ?-infix, ?-compliant codes

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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)

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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

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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

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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

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Computation by DNA Self-Assembly Mao, LaBean,
Reif, , Seeman, Nature, 2000
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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)

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DNA Clonable Octahedron Shih, Joyce, Nature,
2004
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Nanoscale DNA TetrahedraGoodman, Turberfield,
Science, 2005
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DNA OrigamiRothemund, Nature, 2006
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(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
• Shors quantum algorithm for factoring integers
  (1994)

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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.

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(iii) Nature as Computation

• Understand nature by viewing
• natural processes as information processing
• Systems Biology
• Synthetic Biology
• Cellular Computing

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(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

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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

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(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

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(iii-3) Cellular Computing

• Computation in living cells ciliated protozoa

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Ciliates Gene Rearrangement
Photo courtesy of L.F. Landweber
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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)

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Natural Computing

• Nature as inspiration cellular automata, neural
  networks, evolutionary computation, swarm
  intelligence, immunocomputing, ALife, membrane
  computing, amorphous computing
• Nature as implementation substrate molecular
  (DNA) computing, quantum computing
• Nature as computation systems biology, synthetic
  biology, cellular computing
• Research interests of
  the UWO Biocomputing Lab

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Biocomputing at Western

• UWO Biocomputing Lab
• http//www.csd.uwo.ca/lila/biocomplab.html
• DNA COMPUTING, CS 9562B/4462B
• http//www.csd.uwo.ca/lila/cs662.html
• UWO Biocomputing Student Award
• http//www.csd.uwo.ca/lila/award.html

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Natural Sciences, Ours to Discover

• Biology and computer science
• life and computation are related.
• I am confident that at their interface great
  discoveries await those who seek them
• Leonard Adleman, Scientific American, August
  1998