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Cellular Computing

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Illustrations. Courtesy of Michael L. Simpson. Cellular communication ... millions of cells acting in a self-organized, distributed, coordinated fashion ... – PowerPoint PPT presentation

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Title: Cellular Computing


1
Cellular Computing
  • Martyn Amos
  • Department of Computer Science
  • University of Exeter
  • http//www.dcs.ex.ac.uk/mramos

2
Lifes logic
  • The idea that living systems may be viewed in
    terms of logic and electronics is now well
    established

3
Reversing the metaphor
  • Can the metaphor be reversed can we use natural
    systems (as opposed to simply modelling them) to
    build computing devices?
  • We propose using DNA and cells as computational
    substrates

Martyn Amos, DNA Computation, Ph.D. thesis,
Department of Computer Science, University of
Warwick, 1997
4
Plenty of room at the bottom
  • Richard Feynman
  • Theres plenty of room at the bottom (1961)
  • Essentially founded field of nanotechnology
  • Key idea - molecules as machine components

Richard P. Feynman, Theres Plenty of Room at the
Bottom, in Miniaturisation, D. Gilbert (Ed.), pp.
282-296, 1961
5
Realisation
  • As is often the case, Feynman was way ahead of
    his time in suggesting possibility of
    molecular-level computing
  • Technology has lagged behind his vision
  • Only realised in 1994, when Len Adleman
    demonstrated feasibility of computing with DNA
    molecules

Leonard M. Adleman, Molecular Computation of
Solutions to Combinatorial Problems, Science 266,
pp. 1021-1024, 1994
6
Cellular hardware
  • Previous proposals have used DNA simply as a
    inert storage medium, which is then acted upon by
    laboratory operations
  • However, within its natural environment (the
    cell), DNA is much more powerful
  • It carries biological meaning, as it is
    interpreted by the hardware of the cellular
    components

Gerald Owenson, Martyn Amos, David Hodgson and
Alan Gibbons, DNA-based Logic, Soft Computing
52, pp. 102105, 2001
7
Cellular Computing
The punched tape running along the inner seam of
the double helix is much more than a repository
of enzyme stencils. It packs itself with
regulators, suppressors, promoters,
case-statements, if-thens.
Richard Powers, The Gold Bug Variations,
p. 365, HarperPerennial
8
Genetics 101
  • Genes are basic building blocks of genetic
    information
  • Each gene codes for a protein (or proteins)
  • May be turned on (expressed) or off (repressed)
  • Gene read (transcribed) and then converted
    (translated) into a protein

9
Gene expression
  • DNA contains the potential coding information for
    a vast range of possible proteins
  • Gene expression is not a linear process
  • Genes may require the product(s) of other gene(s)
    to in order to be expressed
  • The product of one gene may turn off the
    expression of another gene
  • The product of a gene can even effect its own
    expression (feedback)

10
Example
Gene 2 codes for a protein that activates the
transcription of gene 1, while gene 1 and gene 3
code for proteins that form a complex inhibiting
the transcription of gene 2. Activation and
inhibition of gene expression are indicated by
and -, respectively.
11
Gene structure
  • Genes composed of number of regions
  • Promoter-gene-terminator
  • Transcription regulated by activators and
    repressors

12
Operons
  • Set of functionally related genes with common
    promoter
  • lac operon contains three structural genes that
    allow E. coli to utilise lactose
  • When bacteria grown in glucose, product of lacI
    gene represses transcription of lac
  • When grown in glucose and lactose, lactose
    by-product inhibits repressor, and the genes are
    expressed
  • lac operon controlled by two sugars (inputs)

13
Repression and inhibition
Just glucose
Glucose and lactose
14
Sugar logic
  • lac operon may be viewed as two input toggle
    switch
  • Grow in glucose, and externally toggle with
    presence/absence of lactose
  • Can visualise OR by selecting operons that
    require one or more activators for transcription
  • AND with two activators required, etc.

15
Cellular benefits
  • Cells are miniature, energy efficient,
    self-reproducing systems that can manufacture
    biochemical products
  • They can make logical decisions based on both
    their internal state and environmental factors,
    and then act upon these
  • It is now possible to re-program the genetic
    circuitry underlying some of these
    decision-making processes

16
Genetic process engineering - dry
  • Methodology for modifying the DNA encoding of
    existing genetic elements to achieve desired
    input/output behaviour for constructing reliable
    circuits of significant complexity
  • Construct library of well-characterised
    (understood) genes, with their inputs and outputs
    defined circuit components
  • Take a circuit for a given task (eg. Simple
    if-then-else clause) and map it onto the
    component library

17
Genetic process engineering - wet
  • Then clone the required genes into your target
    organism
  • May take many months, but then have limitless
    supply
  • Choose one or more output genes to yield
    detectable signal
  • Cell development and metabolism simulates the
    circuit

18
Laboratory implementations
  • Elowitz and Leibler describe the construction of
    an oscillator network that causes colony of E.
    coli to periodically flash oscillation cycle
    slower than reproduction cycle, showing that
    oscillation state was transmitted from one
    generation to the next

M.B. Elowitz and S. Leibler, A synthetic
oscillatory network of transcriptional
regulators, Nature 403335338, 2000
19
Laboratory implementations
  • In the same issue, Gardner et al. describe the
    construction of a genetic toggle switch that is
    flipped from one state to another by either
    chemical or heat induction molecular memory

T.S. Gardner, C.R. Cantor and J.J. Collins,
Construction of a genetic toggle switch in
Escherichia coli, Nature 403339342, 2000
20
Laboratory implementations
  • Weiss et al. (MIT AI Lab, now at Princeton) have
    demonstratedc onstruction and testing of
    engineered genetic circuits which exhibit the
    ability to send a controlled signal from one
    cell, diffuse that signal, receive that signal in
    a second cell and activate a remote
    transcriptional response

Ron Weiss, et al, Cellular Computation and
Communications Using Engineered Genetic
Regulatory Networks, in Martyn Amos (Ed.),
Cellular Computing, Series in Systems Biology,
Oxford University Press USA, 2004 (in press)
21
Potential applications
  • Originally intended to demonstrate proof of
    principle
  • However, relatively recent paper suggests
    possibility of applying such implementations to
    gene therapy and biotechnology

Nature 403339-342, Jan. 20 2000
22
Cells and nanotubes
  • Cells have recently been integrated into hybrid
    micro- and nano-scale systems
  • Cells grown on a bed of carbon nano-fibres, which
    opens up the possibility of individually
    addressing cells in a colony
  • Allows targeted delivery of plasmids (ie. novel
    genetic material) or other macro-molecules
  • Nano-fibres allow individual electrical addressing

Michael L. Simpson et al, Integration of Cells
into Microscale and Nanoscale Systems, in
Cellular Computing
23
Illustrations
Courtesy of Michael L. Simpson
24
Illustrations
Courtesy of Michael L. Simpson
25
Cellular communication
  • Cells can also communicate via chemicals secreted
    into their environment which are detected and
    acted upon by other cells
  • Allows formation of super-organisms, made up of
    millions of cells acting in a self-organized,
    distributed, coordinated fashion

26
Example slime mould
Courtesy of T. Schmikl
27
Pattern generation
28
Pattern generation
29
Pattern generation
Nature 3766535, July 6 1995
30
Controlled self-assembly
  • If we could in some way control or program the
    self-assembly of bacterial colonies, then we
    could potentially provide substrates for further
    micro or nano-scale engineering
  • For example, pattern formation followed by
    chemical deposition
  • Ongoing work in our group

31
Long term prospects
  • Local, decentralised, fault-tolerant architecture
  • Biosensors (pollution, chemical agents, etc)
  • Programmable delivery systems
  • "Construction workers" for assembly of
    nano-structures
  • Flagellar motors - microbots"

32
Acknowledgements
  • Joint work with Alan Gibbons (Kings College) and
    Dave Hodgson (Warwick)
  • EC MolCoNet IST Network
  • EPSRC Novel Computation cluster on Cellular
    Computing
  • http//www.dcs.ex.ac.uk/research/cellcom

33
Blatant plug
In press, OUP USA Series on Systems Biology
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