Introduction to Biocomputing:
Structure
(DNA & RNA)
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•genome: biological information in an organism
•DNA: deoxyribonucleic acid, carries genome of cellular
lifeforms
•RNA: ribonucleic acid, carries genome of some viruses,
carries messages within the cell
•bases: the four bases found in DNA are
adenine (A), cytosine (C), guanine (G),
and Thymine (T); in a “double helix” of DNA,
bonds are always A--T or C--G; thus a single
strand of DNA carries the information about
the strand it would bond to
So DNA can be thought of as a “base 4” storage medium, a
“linear tape” containing information in a 4-character alphabet
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DNA—the “double helix”
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DNA—
”direction”
http://www.swbic.org/products/clipart/images/dna2.jpg
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RNA:
Thymine (T) replaced
by Uracil (U) and
deoxyribose
replaced by ribose
http://www.swbic.org/products/clipart/images/rna.jpg
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comparison
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Translation:
DNA  rRNA  mRNA  tRNA  protein
http://www.swbic.org/products/clipart/images/dogmag.jpg
http://www.swbic.org/products/clipart/images/translation.jpg
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DNA provides the basic “code”.
RNA copies this code from the DNA and
used this information to form a string of
amino acids—i.e., a protein.
Proteins “are the machines that make all
living things function”
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•Central Dogma:
Before the discovery of
retroviruses and prions, this was
believed to be the basic
mechanism of inheritance in all
living things
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Relative sizes:
10-18: electron
10-15: proton, neutron
“nanotechnology”:
10-14: atomic nucleus
10-10: water molecule (angstrom)
molecules, atoms
10-9: (nanometer, nm), one DNA “twist”
10-8: wavelength of UV light
10-7: thickness of cell membrane
0.18 or 0.13 mm, Pentium 4 wire width
10-6: diameter of typical bacterium (micron, mm)
10-5: diameter of typical cell
2-10 mm, typical MEMS feature size
10-4: width of human hair
10-3: diameter of sand grain (millimeter, mm)
10-2: diameter of nickel (centimeter, cm)
35 mm--one side of Pentium 4 chip
100: 1 meter
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Why is biomolecular computing attractive?
•Size:
--typical bacterium has diameter on ht order of 10-6 m. (1
micron);
--one twist of DNA double helix is on the order of 10-9 m.
(nanometer scale)
•Power requirements should be low
•Massive parallel computation is theoretically possible
•I/O can be two-dimensional
•Instabilities of quantum systems are much less of a problem
here
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What are the disadvantages?
•Speed--typical reaction can take hours or days
•Error rates--may be unacceptably high; may be introduced by
mechanical steps in proocessing data
•I/O--we do not yet have efficient mechanisms for doing
input/output with these systems
•“Herd” property--we can affect a mixture of data items; we
cannot in general pick out one specific item; biomolecular
computing is inherently parallel
•Exponential growth in size of computation--it may be that the
speed barrier in traditional computing is replaced by a size
barrier in biomolecular computing--we may need too much
biological material to solve a reasonable sized problem for the
“computation” to be feasible
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What interesting projects can build on our
knowledge of traditional computer
engineering?
• “structural” designs—DNA computing
• “chemical” designs—using proteins as signals
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Computing using DNA structures:
•polynucleotide: a single DNA strand
•oligonucleotide: short, single-stranded DNA molecule, usually
less than 50 nucleotides in length
In DNA computing, specific oligonucleotides are constructed to
represent data items.
•nucleotide: phosphate group + sugar + one of the 4 bases
(A,C,G,T): the phosphate end is labeled 5’, the base end, 3’
Example: in Adelman’s seminal 1994 paper, oligonucleotides of
length 20 were built to represent vertices and edges in a given
graph:
Vertex V1
A
Vertex V2
T
G
T
T
C
C
A
A
G
A
T
Edge V1-V2
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DNA computing (“structural”, “digital”)
Possible operations on DNA:
•building up custom oligonucleotide sequences to
represent parts of your data
•splitting--can be done by heating, e.g.
•recombining--can be done by cooling
•cutting strand at a particular site
•“sticking” two fragments together (at their ends)
•sorting by some string property (including length)
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So-----DNA computing:
•uses structure of the DNA
•relies on mechanical operations
•answers “self-assemble”
•basic steps:
•encode the problem
•make a “solution” of problem fragments
•cool the solution so fragments will form longer strands
•filter out the answers you want
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Example: solving graph problems
A
T
T
C
G
A
C
A
A
G
A
T
•Encode vertices and edges—use DNA properties to
encode graph “structure”
•Mix up a solution of your fragments
•Cool down, get resulting “paths”, “spanning trees”,
etc.
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“Standard cell architectures, FPGAs”
The BioBrick Project
Basic idea (after Prof. Tom Knght, MIT):
•“gates” are functional units
•Ends of gates are standard “join” DNA
sequences—reserved for this purpose
•So we can build computational chains easily
Web page:
http://parts.mit.edu/registry/index.php/Main_Page
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Other applications of DNA computing:
•general computing using “sticker” language
•study of relationship between traditional architectures
and DNA configurations:
---FSMs-linear DNA
---stack machines--branching DNA
---“Turing machines” (general purpose computers)-sheet DNA
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Other applications of DNA computing
(continued):
•3-D self-assembled structures:
•“walking and rolling DNA”:
•structures for nanotube assembly: (recently reported in
Science)
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