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Transcript
CSE 599 Lecture 4: DNA computing
 Cells process and store information
 DNA forms an instruction manual
for the chemical processes in a cell
 DNA stores hereditary information
passed from parents to offspring
 Information is encoded digitally as
nucleotide sequences in the DNA
Thanks to Chris Diorio and Doug Zonker
for some of the slides
Deoxyribonucleic acid (DNA) (a) inside,
and (b) outside, the cell nucleus
Used figures from “Understanding DNA”
by Calladine and Drew
R. Rao, Week 4: DNA computing
1
Digital Representation
 Rather than thinking “biology” or “molecule,” think in terms
of digital information storage using the alphabet A, T, G, and C
R. Rao, Week 4: DNA computing
2
Biomolecular computing: Basic Idea
 A DNA strand encodes a quaternary (2-bits/base) string
 Can use molecular techniques to manipulate strings
 Synthesize, cut, splice, copy, replicate and read DNA molecules
 Separate and classify strings according to their size or content
 These processes are slow but massively parallel
 DNA for general-purpose digital computation
 Encode: Map problem onto DNA strands
 Exhaustive Search:
 Generate all possible solutions by subjecting strands
simultaneously to biochemical reactions
 Use molecular techniques to eliminate invalid solutions
 The result: Turing Universal DNA computing
R. Rao, Week 4: DNA computing
3
DNA primer...
 DNA provides cells with long-term information storage
 Resides within cell nucleus
 Provides templates for protein manufacture
 mRNA is a temporary copy created from DNA
 Migrates out of nucleus into cytoplasm
 Ribosomes read mRNA to create proteins
 Assisted by tRNA
 Proteins perform cell functions
R. Rao, Week 4: DNA computing
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Components of DNA/RNA
 nitrogenous bases
 purines
 pyrimidines
 thymine in DNA
 uracil in RNA
 pentose sugar
 2-deoxyribose in DNA
 ribose in RNA
 phosphate group
R. Rao, Week 4: DNA computing
5
Nucleotides
 base + sugar = nucleoside
 nucleoside + phosphate = nucleotide
 bases are linked into a chain by
alternating sugars and phosphates
 direction is significant
 read from 5’ end to 3’ end
R. Rao, Week 4: DNA computing
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DNA base pairing (Watson-Crick
Complementarity)
 Antiparallel strands form
hydrogen bonds between bases
 Pairing of bases
 cytosine  guanine (C-G)
thymine  adenine (T-A)
R. Rao, Week 4: DNA computing
7
DNA double helix
 Cells are filled with water
 Sugar–phosphate group is hydrophilic
 Bases are hydrophobic
 DNA double-strand twists to shield bases from water
 10 – 12 bp/turn
 Forms a helix
 Human DNA strands can be 3cm long
 but only 20Å in diameter
R. Rao, Week 4: DNA computing
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DNA replication
“…it has not escaped our notice that the
specific pairing we have postulated
immediately suggests a possible copying
mechanism for the genetic material.”
- Watson & Crick
R. Rao, Week 4: DNA computing
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mRNA production from DNA
 Producing mRNA
1. Unwind section of DNA
2. Catalyze mRNA using RNA
polymerase
 Base pairing
 40 bases/sec at 37° C
3. DNA reforms into double helix
4. mRNA leaves nucleus
R. Rao, Week 4: DNA computing
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Amino acids and Proteins
 20 amino acids. Each is a carbon atom with:
 Amino (NH2) and carboxyl (COOH) groups
 A H+ atom (except proline) & something else
 Protein: A chain of amino acids
 Order of amino acids is primary structure
 Backbone folding gives secondary structure
R. Rao, Week 4: DNA computing
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mRNA allows protein synthesis
 Base triplets (codons) code for
amino acids
 Ribosomes serve as decoding
machines
 tRNA is the adapter molecule
 One end of tRNA carries an
amino acid
 Other end carries an anticodon
that matches codon on mRNA
strand
 When a ribosome finds a tRNA
with a matching anticodon,
amino acid is broken off and
attached to polypeptide chain
R. Rao, Week 4: DNA computing
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The genetic code
 Triplets code for amino
acids
 AUG signals start of
translation
 UAA, UAG, UGA signal
end of translation
 Redundancy: Many triplets
may code for 1 amino acid
R. Rao, Week 4: DNA computing
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A List of Molecular Techniques and Tools
 Separating and fusing DNA strands: Denaturation (melting)
and Hybridization (also called annealing or renaturation)
 Amplifying DNA: PCR (polymerase chain reaction)
 Shortening and Cutting DNA (based on exonucleases and
endonucleases)
 Determining the length of DNA (gel electrophoresis)
 Reading the contents of DNA (DNA sequencing)
R. Rao, Week 4: DNA computing
14
Denaturation and Hybridization
 Double helix can be denatured
by heating (85-95 degrees C)
 Denaturing is reversible by
cooling (renaturing)
 Called hybridization when DNA
is from different sources (e.g.
DNA and RNA)
 The ability of two nucleic acid
preparations to hybridize is a
precise test for Watson-Crick
complementarity of their base
sequences
R. Rao, Week 4: DNA computing
DNA melting from heating
15
PCR amplifies DNA
 PCR: Polymerase chain reaction
 Polymerase: Enzyme that adds
nucleotides to an existing DNA
strand in the 5’-3’ direction
 Amplifies short segments of DNA
 Doubling in ~5 minutes
 Segment must be bracketed by
known primer sequences
 ~20 bases
R. Rao, Week 4: DNA computing
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Enzymes that shorten or cut DNA
 Exonucleases shorten DNA
 Remove nucleotides one at a
time from the ends of DNA
molecules. E.g. ExonucleaseIII
removes nucleotides from the
two 3’ ends
 Endonucleases cut DNA
 E.g. Restriction enzymes such as
EcoRI recognize a short
sequence of DNA and cut the
molecule at that site
 Recognition site typically 4-6
bases (e.g. GAATTC)
 Sticky ends – overhanging ends
of DNA available for bonding
R. Rao, Week 4: DNA computing
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Gel electrophoresis determines length
 DNA molecules are negatively
charged
 Place DNA on gel in electric field
 DNA molecules drift through gel
toward positive electrode
 Small molecules move faster
through gel than large ones
 Deactivate field when first
molecules reach positive electrode
 Determine lengths by comparing
distance of a sample with distance
traveled by control fragments with
known lengths
R. Rao, Week 4: DNA computing
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Sequencing DNA
 Break DNA at some instance of
known site
 E.g. break DNA strands at G
 Determine length of broken strands
 Do this also for A, T, and C.
 Electrophorese the results on one gel
 Read out sequence from right to left
R. Rao, Week 4: DNA computing
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DNA computing
 Field started by Leonard M. Adleman (USC)
 Used DNA strands and molecular techniques to solve a simple
Hamiltonian path problem:
 Find a path that visits all vertices once and only once
 Nov. '94 issue of Science magazine
 Molecular Computations of Solutions to Combinatorial Problems
 Laboratory experiment
 Constructed DNA molecules representing the possible solutions
to a 7-city travelling salesperson problem
 Details: see copy of paper that was handed out in class
R. Rao, Week 4: DNA computing
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The computational premise
 Construct a DNA molecule for each potential solution
 Generate candidate solutions in parallel
 Use molecular operations to eliminate invalid solutions
 Five basic operations
 Extract: Separates 1 DNA tube into:
 One tube with all molecules containing a particular substring
 Another with the remaining molecules
 Merge: Mixes two tubes
 Detect: Checks if there are any DNA strands in a tube
 Copy: Amplifies the strands in a tube
 Append: Attaches a string to the end of every molecule in a tube
R. Rao, Week 4: DNA computing
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Adleman’s DNA-based encoding of graphs
Input graph:
Encoding:
R. Rao, Week 4: DNA computing
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Basic Steps in Adleman’s Algorithm
 Input: A directed graph G with n vertices, a start vertex vin and
a stop vertex vout
 Step 1: Generate paths in G randomly in large quantities
 Step 2: Reject all paths that do not begin with vin and end in
vout
 Step 3: Reject all paths that do not involve exactly n vertices
 Step 4: For each vertex v, reject all paths that do not involve v
 Output: “Yes” if any path remains, “No” otherwise
R. Rao, Week 4: DNA computing
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Adleman’s experiment
 Took 7 days to solve 7-city problem, mainly due to
laboratory-related set-up time; Robotic manipulators could
speed things up
 All steps are amenable to molecular implementation
 Related Problems:
 SAT: Solution proposed by Lipton
 Created a directed graph whose paths correspond to all possible
Boolean assignments of variables
 Search paths for a satisfiable assignment according to the
structure of input formula
 Cracking the DES (data encryption standard)
 Search for correct 56-bit key given (plaintext, cryptotext) pairs
 Not done yet: at 1 operation/hour, requires 9 months
R. Rao, Week 4: DNA computing
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Reasons to be optimistic...
 DNA computing is orders of magnitude more energy and
density efficient than digital computers
 Employs massive parallelism
 Field is only 7 years old, so many untried paths
 Example: Use the structure of DNA and proteins to compute?
 Living cells hold many secrets
 Copy their information-processing approaches
 Possibly use living cells in computing systems
 DNA can form self-assembling structures
 Analogous to cellular automata
R. Rao, Week 4: DNA computing
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Reasons to be pessimistic...
 Generate-and-test approach requires one strand of DNA for
each candidate solution
 270 DNA strands of length 1000 is 8 kilograms
 DNA processing is slow and error prone
 1 hour per reaction
 Approximate matches and mutations may give incorrect results
 Need to learn to build reliable computers from noisy components
 No communication between strands
 No easy way to determine if a tube contains two identical strands
 No killer app has been identified yet
R. Rao, Week 4: DNA computing
26
Homework Assignment (due in two weeks)
 Solve the SUBSET SUM problem using DNA computing
 SUBSET SUM: Given a set of N positive integers S0, S1, …
, SN and a positive integer T (the "target"), is there some
subset of these integers Si (with possible repetitions) that
sums exactly to T?
 Examples:
 Input: S = { 2, 4, 6, 8, 10 }, T = 12; Answer: Yes
 Input: S = { 2, 4, 6, 8, 10 }, T = 13; Answer: No
 Input: S = { 1, 5, 4, 2, 7, 2, 12, 19, 17}, T = 42; Answer: Yes
 (T = 1 + 5 + 5 + 2 + 12 + 17 or 1 + 1 + … + 1)
R. Rao, Week 4: DNA computing
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Homework Assignment (cont.)
 Three parts:
 Encode a given problem instance using DNA strands
 List the steps that will allow you to extract an answer
 Implement your idea using the Strand software package for high
level simulation of DNA computing
 Strand C++ Class Library:
 Simulates the creation of DNA strands
 Basic representation: short strand of DNA = an “Element”
 Does not use individual bases or base sequences
 High level simulation of typical operations performed in DNA
computing: melt, anneal, cut, detect, extract, remove, pour, append,
read, and length.
 Documentation: http://www.lut.fi/~kyrki/dna/doku.html
R. Rao, Week 4: DNA computing
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Next Week: Fundamentals of Neurobiology
 No homework due next week.
 Read the on-line articles for additional information on DNA
computing
 Download and test the simulator using sample programs for
the Hamiltonian path and SAT problems
 Contact TA or instructor if you have any questions or
problems regarding the DNA computing assignment
 Have a great weekend!
R. Rao, Week 4: DNA computing
29