DNA and Quantum Computers Trends in Computational Science DNA and Quantum Computers Lecture 2 (cont)

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.