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Cellular Computing


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

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

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

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

Martyn Amos, DNA Computation, Ph.D. thesis,
Department of Computer Science, University of
Warwick, 1997
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
  • 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

Leonard M. Adleman, Molecular Computation of
Solutions to Combinatorial Problems, Science 266,
pp. 1021-1024, 1994
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

Gerald Owenson, Martyn Amos, David Hodgson and
Alan Gibbons, DNA-based Logic, Soft Computing
52, pp. 102105, 2001
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
Genetics 101
  • Genes are basic building blocks of genetic
  • 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

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)

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.
Gene structure
  • Genes composed of number of regions
  • Promoter-gene-terminator
  • Transcription regulated by activators and

  • Set of functionally related genes with common
  • 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
  • lac operon controlled by two sugars (inputs)

Repression and inhibition
Just glucose
Glucose and lactose
Sugar logic
  • lac operon may be viewed as two input toggle
  • 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.

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

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

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

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
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
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)
Potential applications
  • Originally intended to demonstrate proof of
  • However, relatively recent paper suggests
    possibility of applying such implementations to
    gene therapy and biotechnology

Nature 403339-342, Jan. 20 2000
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
Courtesy of Michael L. Simpson
Courtesy of Michael L. Simpson
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

Example slime mould
Courtesy of T. Schmikl
Pattern generation
Pattern generation
Pattern generation
Nature 3766535, July 6 1995
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

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

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

Blatant plug
In press, OUP USA Series on Systems Biology
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