DNA Computing: Concept and Design

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DNA Computing Concept and Design

• Ruoya Wang
• April 21, 2008
• MATH 8803
• Final presentation

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The DNA molecule

• Serves as a long-term storage of genetic
  information.
• The information is stored as unique sequences
  composed of two base pairs A?T and G?C.
• The complexities of all living organisms are the
  result of simple manipulations of their encoded
  DNA sequences.

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DNA computing

• A relatively new form of computing that, instead
  of using silicon-based technology, utilizes the
  abilities of the DNA molecule and biochemistry.
• Pioneered and experimentally verified by computer
  scientist Leonard Adleman of USC. Molecular
  Computation of Solutions To Combinatorial
  Problems Science 266(11) 1021-1024.
• Although the field is still in its infancy, many
  significant advancements have been made since its
  inception.

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Adlemans DNA computer

• Proof-of-concept experiment.
• Solved a 7-nodal instance of a directed
  Hamiltonian path problem (i.e. the traveling
  salesman).
• The typically brute-force algorithm consists of
• Randomly produce all possible paths through the
  graph.
• Keep only the paths that begins and ends at the
  predefined vertices.
• Keep only the paths that entered the same number
  of vertices as the number of vertices of the
  graph.
• Keep only the paths entered each vertex exactly
  once.
• The remaining path is the answer.
• Adleman translated the algorithm to a form that
  could be implemented using DNA and biochemistry.

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Adlemans DNA computer

• Computer algorithm to molecular biochemistry
• Designated each vertex of the graph with a unique
  and random 20-mer DNA sequence and run all the
  sequences through a ligation reaction.
• Sequences starting and ending at the specified
  vertices were extracted using PCR with primers
  designed for those sequences.
• 140-mer sequences were extracted by running the
  remaining sequences from step 2 through gel
  electrophoresis. Result amplified by PCR.
• Using biotin-avidin magnetic bead system, only
  sequence with one of each vertex was extracted
• ssDNA ? Oi ? biotin-avidin ? magnetic beads
• Where i 1,2,3,4,5,6,7
• Product of step 4 is PCR amplified and run on a
  gel.

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Further developments

• Adleman successfully demonstrated the viability
  of DNA computing through his experiments.
• Further research by Adleman and other groups
  solved more complex combinatorial problems.
• A novel direction was taken when the concept of
  an autonomous DNA computer was introduced by
  Benenson et al.
• Exploited cleaving enzyme and the native
  potential free energy in DNA during spontaneous
  hydrolysis. Autonomous logic control was based on
  concept of finite state automata.
• Previous DNA computers relied on ATP and heat for
  ligation reactions and denaturing respectively.

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Finite state automata

• FSA or finite state machine describes the
  behavior of a system using a finite number of
  states or conditions.
• The states represent all possible behaviors of
  the system.
• Transitions between the states are dependent on
  an input alphabet and transition conditions.
• Formally a state machine M is defined by the
  following quintuple
• M S, S, f, si, F
• S finite, non-empty set of states
• S alphabet of finite, non-empty set of symbols
• f transition function defining the transition
  rules
• si initial state
• F final states

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A state machine

• Propose a state machine that determines if there
  is an even number of a particular symbol in a
  binary alphabet (i.e. abba).
• Assign conditions
• S S0, S1
• S a, b
• f S0?S1a, S1?S0a, S0?S0b, S1?S1b
• si S0
• F S0

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A state machine
ababbaa
f S0?S1a, S1?S0a, S0?S0b, S1?S1b
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Autonomous DNA computer

• Benenson et al. had to translate and incorporate
  FSA to DNA computing.
• Assigned a unique 5-base sequence to each symbol
  plus a terminator sequence.

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Fok I

• Autonomous computer must not only have its own
  logic system, but also its own fuel.
• Fok I is a type IIs restriction endonuclease.
• Recognition site
• 5-GGATG(N)9?-3
• 3-CCTAC(N)13?-5
• Cleaves if a noncovalent hybridization complex
  exists between the recognition and cleavage sites.

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Transition functions

• The transition function defined by
• f S0?S1a, S1?S0a, S0?S0b, S1?S1b
• was assigned to sequences of DNA.
• Blue nucleotides is the recognition site for Fok
  I, gray are nucleotide spacers, and the yellow
  are the detectors (unique for each transition
  rule)

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Computation cycle
Legend
Final state
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Clinical applications

• The terminator sequence is replaced by a ssDNA in
  a hairpin loop.
• This ssDNA can be programmed to target specific
  mRNA sequences that regulate biological
  processes.
• Designed a DNA computer that could analyze,
  in-vitro, the levels of mRNA genes associated
  with small-cell lung cancer (SCLC) and prostate
  cancer (PC).

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Diagnostic rules
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The future

• DNA computing research remains active and holds
  many promises in the future in fields such as
  biochemical sensing, genetic engineering, and
  medical diagnosis.
• There are however, many problems that still need
  to be addressed, mainly
• The imprecision of biochemistry.
• Mis-hybridization.
• Enzymatic digestions are not always complete.
• Amount of DNA required to scale up the process.
• Adverse effects in living organisms if it is to
  be used in-vivo.