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Trends in Computational ScienceDNA and Quantum ComputersDNA and Quantum Computers
Lecture 2 (cont)Lecture 2 (cont)
Sources•1. Nicholas Carter•2. Andrea Mantler, University of North Carolina•3. Michael M. Crow, Executive Vice Provost , Columbia University.•4. Russell Deaton, Computer Science and Engineering, University of Arkansas• 5. Julian Miller• 6. Petra Farm, John Hayes, Steven Levitan, Anas Al-Rabadi, Marek Perkowski, Mikael Kerttu, Andrei Khlopotine, Misha Pivtoraiko, Svetlana Yanushkevich, C. N. Sze, Pawel Kerntopf, Elena Dubrova
Dominating role of biology and system science
Outline
• Trends in Science
• Programmability/Evolvability Trade-Off
• DNA Computing
• Quantum Computing
• DNA and Quantum Computers
Introduction
• What’s beyond today’s computers based on solid state electronics?
• Biomolecular Computers (DNA, RNA, Proteins)
• Quantum Computers
• Might DNA and Quantum computers be combined? Role of evolutionary methods.
Scientific questions are growing more complex and interconnected.
We know that the greatest excitement in research often occurs at the borders of disciplines, where
they interface with each other.
Computers and Information Computers and Information TechnologyTechnology
No field of research will be left untouched by the current explosion of information--and of information technologies. Science used to be composed of two endeavors--theory and experiment. Now it has a third component: computer simulation, which links the other two.
- Rita Colwell
Government Initiatives on Information Technology
• Interdisciplinary teams to exploit advances in computing– Involves computer science, mathematics, physics, psychology, social
sciences, education
• Focus on:– Role of entirely new concepts, mostly from biology
– New technologies are for linking computing with real world - nano-robots, robots, intelligent homes, communication.
– Developing architecture to scale up information infrastructure
– Incorporating different representations of information (visual, audio, text)
– Research on social, economic and cultural factors affected by and affecting IT usage
– Ethical issues.
The Price of Programmability
• Michael Conrad
• Programmability: Instructions can be exactly and effectively communicated
• Efficiency: Interactions in system that contribute to computation
• Adaptability: Ability to function in changing and unknown environments
• What is nanotechnology?– Any technology below nano-meter scale
• Carbon nano-tubes
• Molecular computing
• Quantum computing
• Are we going there?– Yes, a technology compatible with existing silicon process
would be the best candidate.
• Is it too early for architecture and CAD?– No
CAD problems in nanotechnology
NanotechnologyNanotechnology
Atoms<1 nm
DNA~2.5 nm
Cellsthousands of nm
We are at the point of connecting machines to individual cells
Federal Initiative: Federal Initiative: NanotechnologyNanotechnology
• Interdisciplinary ability to systematically control and manipulate matter at very small scales– Involves biology, math, physics, chemistry,
materials, engineering, information technology
• Focus on: – Biosystems, structures of quantum control,
device and system architecture, environmental processes, modeling and simulation
Biocomplexity
Planet Biome
Ecosystem Community HabitatPopulation
Organism Organ
Tissue Cell Organelle MolecularAtomic
REDUCTIONIS
M
INTEG
RATION
Multidisciplinary
Human Genome Sequence
• Next race: annotation– Pinpoint genes
– Translate genes into proteins
– Assign functions to proteins
• Genomic tool example: DNA DNA chipchip– Array of genetic building blocks
• acts as “bait” to find matching DNA sequences from human samples
Entire yeast genome on a chip
DNA ComputersDNA Computers• Massive ParallelismMassive Parallelism through
simultaneous biochemical reactions
• Huge information storage density
• In Vitro Selection and Evolution
• SatisfiabilitySatisfiability and Hamiltonian Path
DNA Computing (Adleman, 1994)
DNA is the hereditary molecule in every biological cell.
Its shape is like a twisted rubber ladder (i.e. a double helix).
The rungs of the ladder consist of two bonded molecules called
bases, of 4 possible types, labeled G, C, A, T.G, C, A, T.
G can only bond (pair off) with C, and A with T.
What is DNA? Basic CodingWhat is DNA? Basic Coding
A = adenine
T = thymine
C = cytosine
G = guanine
Double HelixDouble Helix
Base PairingBase Pairing
If a single strand (string)strand (string) of DNA is placed in a solution with isolated bases of A, G, C, T, then those bases will pair off with the bases in the string, and form a complementary string, e.g.
G A T T C A G A G A T T A TC T A A G T C T C T A
Strands and Pairing OffStrands and Pairing Off
DNA CodeDNA Code
Sticky EndsSticky Ends
DNA operations 1DNA operations 1
• Separating DNA strands (denaturation)
• Binding together DNA strands– (renaturation or annealing)
• Completing sticky ends
• Synthesizing DNA molecules
DNA operations 2DNA operations 2
• ShorteningShortening DNA molecules• CuttingCutting DNA molecules
DNA operations 2DNA operations 2
• LinkingLinking DNA molecules• InsertingInserting or deletingdeleting short subsequences• MultiplyingMultiplying DNA molecules
DNA operations 3DNA operations 3
• Filtering
• Separation by length
• Reading
The Hamiltonian path problemThe Hamiltonian path problem
The Hamiltonian path problem: In a directed graph,
find a path from one node that visits (following
allowed routes) each node exactly once.
This complementary bonding can be used to perform computation,e.g. a version of the traveling salesperson problem (TSP), called theHamiltonian Path problem
Hamiltonian path as an example of graph Hamiltonian path as an example of graph theory problemtheory problem
• This kind of problems are abstracted as graphs. Graphs has nodes and edges. Graphs are oriented (like the above) and non-oriented.
Start with a directed graph G (i.e. the edges between nodes are arrows) at node A, and end at node B.
The graph G has a Hamiltonian Path from A to B if one enters every other node exactly once.
E.G. for the directed graph G1 shown,
a
b c
A B
d e
A solution, (G1’s Hamiltonian Path) is
A => c => d => b => a => e => B
G1
Example of a solution to HP problemExample of a solution to HP problem :- :-
Adelman’s DNA algorithm for Hamiltonian PathAdelman’s DNA algorithm for Hamiltonian Path
• Input:
– A directed graph with nn nodes including a start node start node AA and an end node B.end node B.
• Step 1. Generate graphs of the above form, randomly, and in large quantities (Generate random paths through the graph).
• Step 2. Remove all paths that do not begin with start node A and end with end node B.
• Step 3. If the graph has n nodes, then keep only those paths that
• enter exactly n nodes.
• Step 4. Remove any paths that repeat nodes
• Step 5. If any path remains then answer “yes”otherwise answer “no”.
This is a nondeterministic algorithm.
DNA can implement this algorithm! (Uses 1015 DNA strings)
Step 1 : To each node “i” of the graph is associated a randomrandom 20 base string 20 base string (of the 4 bases A,G,C,T), e.g. TATCGGATCGGTATATCCGA Call this string “S-i”.(It is used to “glue” 2 other strings, like LEGO bricks).
1glue2
DNA Computer for this problemDNA Computer for this problem
Representation of nodes
For each directed (arrowed) edge (node “i” to node “j”) of the graph, associate a 20 base DNA string, called “S-i-j” whose -
a) left half is the DNA complement (i.e. c) of the right half of S-i,b) right half is the DNA complement of the left half of S-j.
Step 2 : The product of Step 1 was amplified by “Polymerase Chain Reaction” (PCRPCR) using primers O-A and (complement) cO-B. Thus, only those molecules encoding paths that began with node A and ended with node B were amplified.
S-i-j
Glue S-jGlue S-i
20 bases
10 bases10 bases
Representation of edges
LitigationLitigation
Implementing the Implementing the algorithm with DNAalgorithm with DNA
• Create a unique sequence of 20 nucleotides to represent a node. Similarly create 20 nucleotide sequence to represent the links between nodes in the following way:
Step 1. Generate random pathsStep 1. Generate random paths
• Step 2a. Denature and add node 0 Step 2a. Denature and add node 0 primer and node 6 anti-primerprimer and node 6 anti-primer
Recall: denature = separate strands
Step2b: PCR amplifies 0-6 strandsStep2b: PCR amplifies 0-6 strands
Step3: Find paths with 7 nodesStep3: Find paths with 7 nodes
• The product of step 2 was separated according to length by electrophoresis.
• The DNA with 140 base pairs was extracted,
PCR amplified,
subjected to electrophoresis a few times to purify sample
PCR is a technique in molecular biology that makes zillions of copies of a given DNA (starter) string.
Step 3 : The product of Step 2 was run on an gel, and the 140-base pair (bp) band (corresponding to double-stranded DNA encoding paths entering exactly seven nodes) was extracted.
Step4: extract paths that containStep4: extract paths that containall nodesall nodes
• The product of step 3 was denatured.• Magnetic beads with complementary node sequences
(nodes 1 to 5) were obtained.• The product was successively filtered by annealing
with solutions containing single complement node beads
Step 4 : Generate single-stranded DNA from the double-stranded DNA product of Step 3 and incubate the single-stranded DNA with cO-i stuck to magnetic beads.
Only those single-stranded DNA molecules that contained the sequence cO-a (and hence encoded paths that entered node a at least once) annealed to the bound cO-a and were retained
Step5: PCR amplify remaining productStep5: PCR amplify remaining product
• See if any product left.
• Actually the exact node sequence in the path can be obtained by a process called graduated PCR
This process was repeated successively with cO-b, cO-c, cO-d, and cO-e.
Step 5 : The product of Step 4 was amplified by PCR and run on a gel (to see if there was a solution found at all).
Problems with Adelman’s Appraoch to DNA Problems with Adelman’s Appraoch to DNA computingcomputing
• Solving a Hamiltonian graph problem with 200 nodes would require a weight of DNA larger than the earth!
• What algorithms can be profitably implemented using DNA?
• What are the practical algorithms?
• Can errors be controlled adequately?
.
DNA Computers
This work took Adleman (the inventor of DNA computing, 1994)a week.
See November 11, 1994 Science, (Vol. 266, page 1021)
As the number of nodes increases, the quantity of DNA neededrises exponentially, so the DNA approach does not scale well.The problem is NP-complete.
But for N nodes, where N is not too large, the 1015 DNAmolecules offer the advantages of massive parallelism.
Summary on Adleman
DNA Computers
Problems with DNA ComputersProblems with DNA Computers
Problems with DNA ComputersProblems with DNA Computers
• Can be adaptable through enzymatic action
• Hard to program because of hybridization errors
• Not very efficient because of space complexity
DNA Self-AssemblyDNA Self-Assembly
Molecular ComputingMolecular Computing• Building electronic circuits from the bottom up, beginning
at the molecular level
• Molecular computers will be the size of a tear drop with the power of today's fastest supercomputers
Single monolayer of organically functionalized silver quantum dots Journal of Physical Chemistry,May 6, 1999
Molecular Computing as an Emerging Field
• Interdisciplinary field of quantum information science addresses atomic system (vs. classical system) efficiency and ability to handle complexity – Involves physics, chemistry, mathematics, computer science
and engineering
• Quantum information can be exploited to perform tasks that would be nearly impossible in a classical world
Observing quantum interference
Quantum ComputersQuantum Computers• Different operating principles than either
DNA or conventional computers
• Coherent superposition of states produces massive parallelism
• Explores all possible solutions simultaneously
• Prime Factorization, Searching Unsorted List
Quantum Computers
• Qubits: |Q> = A |0> + B |1>
• |A|2 + |B|2 = 1
• P(0) = |A|2, P(1) = |B|2
1 0
Quantum Computers: CNOT Gate
|a>
|b>
|a>
|a + b>
|a> |b> |a'> |a+b>
0 0 0 0
0 1 0 1
1 0 1 1
1 1 1 0
Quantum Computers
• Very efficient because of superposition of exponential number of states
• Can be programmed
• Not adaptable
• Must be isolated from the environment
DNA Assembly of Quantum Circuits
CN
CN
CN
CNCN
CN
Quantum Gate
Modification withDNA
Self-AssembledQuantum Circuit
DNA NMR Computers
http://rabi.cchem.berkeley.edu/~kubinec/slideshow1/slideshow/sld013.htm
DNA Qubit
Light
Photoactive Baseor Intercalator
Trap Trap
Enzyme
MeasurementInduced Particle
Research IssuesResearch Issues• What is the state-of-art?
– A lot of funding available!– Lots of experimental research on device level
• Molecular RAMs• Carbon-film memories
– Limited activity of higher levels of design– Lack of communication between
physicists/chemists and architecture/CAD engineers
What could the CAD community contribute at this stage?
• Identifying which properties we need to build circuits– Composability/cascadability: (x')' = x
– Gain for signal restoration
• Restoring logic (molecular amplifiers)
• Error-correcting techniques in quantum computing
• Techniques for building reliable circuits from unreliable components– What logic abstractions do we need for that?
– How much can be borrowed from the existing fault-tolerant design techniques?
ConclusionsConclusions• DNA, Quantum Computers and other nano-
technologies have great potential.• Serious technical barriers need to be overcome.• These technologies have complementary properties.• Work together to have programmability, efficiency,
and adaptability• It is not too early to think about CAD, architectures,
and algorithms.• Past (thousands), present (50 years) and future (few
years) technologies of computing.• Be braveBe brave, have a perspective.
Reading AssignmentReading Assignment• 1. Read slides to lectures 1, 2 and 3 from
my WebPage.• 2. Read Chapter three (Introduction to
Computer Science) from Nielsen and Chuang.
• 3. Read Chapter 1. Sections 1.1, 1.2, 1.3, 1.4.1, and 1.4.2.
• You may expect a very short quizz next week.
This is the end of Lecture 2.
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