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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/translation.jpg

http://www.swbic.org/products/clipart/images/dogmag.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

10-14: atomic nucleus

10-10: water molecule (angstrom)

10-9: (nanometer, nm), one DNA “twist”

10-8: wavelength of UV light

10-7: thickness of cell membrane

10-6: diameter of typical bacterium (micron, mm)

10-5: diameter of typical cell

10-4: width of human hair

10-3: diameter of sand grain (millimeter, mm)

10-2: diameter of nickel (centimeter, cm)

100: 1 meter

35 mm--one side of Pentium 4 chip

2-10 mm, typical MEMS feature size

0.18 or 0.13 mm, Pentium 4 wire width

“nanotechnology”:

molecules, atoms

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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

Edge V1-V2

Vertex V2

A T G T

C A A G

CT A T

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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)

DNA computing (“structural”, “digital”)

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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

C A A G

A T T G

C A 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)