how does nature compute? - western universitylila/dnacomputingadleman_2017.pdf · fast dna origami...

HOW DOES NATURE COMPUTE?

Lila KariDepartment of Computer Science

University of Western OntarioLondon, ON, Canada

http://www.csd.uwo.ca/~lila/[email protected]

Computing

• Computing ≠ Electronic computers

Lila Kari, University of Western Ontario

Computing

• Computing ≠ Electronic computers• Need for computing:

count months, seasons, trading, construction


Computing

• Computing ≠ Electronic computers• Need for computing:

count months, seasons, trading, construction• Means for computing:

Whatever was available !!!


First technology for computing



History of Hardware

• Abacus• Pascal• Jacquard• Babbage• Hollerith• ENIAC• Chip

Abacus


History of Hardware


Mechanical adding machine (1642)


History of Hardware


Jacquard’s punch card loom (1801)


History of Hardware

• Abacus• Pascal• Jacquard• Babbage • Hollerith• ENIAC• Chip

Babbage’s difference engine (1833)


History of Hardware

• Abacus• Pascal• Jacquard• Babbage• Hollerith • ENIAC• Chip

Hollerith punch card system (1890)


History of Hardware


ENIAC (1939-45) -167 sq.m.- 18,000 vacuum tubes- not programmable


History of Hardware


Modern computer chip

• Transistors

• Integrated circuit

Computing Technology• Manual• Mechanical• Electronic


Computing Technology• Manual• Mechanical• Electronic• Biological “The tools of molecular biology were used

to solve an instance of the Directed Hamiltonian Path Problem” (Adleman, Science,94)


What is DNA?

• DNA = Set of instructions telling each cell what job it will do

• It is like a computer program telling the computer what to do!

• It is written in a special alphabet that has only 4 letters !

Languages and alphabets

• Latin alphabet : A, B, C, D, E, F, G, ..., • Korean alphabet :

• Computer alphabet: 0, 1

• DNA alphabet: A, C, G, T

“You will have brown hair” in DNA language

• CTTACTTCGAAGGCTGTGCTCCGCTCACCATCCAGAGCGGAGGTGCGGACCTTAAACTCACTCCTGGAGA AAGATCTGCAAGTGCGCAGGTAAAGTGCACGTGCTCCGCGGTCGGGAGGAAGGAGGCGAGGAGCCAGACT AGCCTGGGAACAGGCAGGGAGGGTTTACACAGCCCCGGCTGAGTCGCGGCTTAGGAAGCAAGGCAAGTTC CCCTAAAGGTTAGTGTGCACAGACGGGTGCGACGGAGCCGACCTAGCGCGGCTGAGTCCGCCTGGGCCTG CAGCAGCTGCCCCCTGAGCACCCCCTCCGGCTCTCTGCCAGGCGACCCAGGAAAAAGTCGCCCCCTGGTG GGCCATGAGGTCATGGGTGGGGGGAGTTTGGAAAGGTTCAGACAGCAAATGTTCCACTTGAACTCCAGGG CAGCATCTGGCACTGCGGGGCCTCCTAGCCATGAGCCGTGGTCAGGCGTTTCCTTAGAATGGAATGCACT GGAGTGAAAACACTAAATCCCTCAAAGCTGCTTCTCTTTACTGTGGTCACACACAGTGAAATCAATGGGC ATTAGTGCAGCTAGCTCTTTTCAAGGACACAATGTTAAGCACAGGAAGCCTGGTATGTGGACGCTCTGGG TTTGCAGAACCAGGCAGGGGCCAGGGGCTCAGGACAAGTGCCCGGTGCTCCCTTCCCATTGGGCGAATCA GAGCCTGGGGCCCGGCGGTGAAGCTCCCCAGGTGACTCTAATGTGCAGTGCACTTTGAGGAGCACTACTT AGACCAATGTGACAGTCTACAATGTGTAGATTTAGGGTGAGTGATTCTGAGGAAAAGAAACCCGAGGCTG TTAGCAGTTGTGGGCAGCTGCTGTCTACCTAAACCAGCTGCGGTTTGCTTGCTTGGTGAGCCTGAGCTTC GCGGGGCGGGCATGGCATCTGCCATCCAGGACCCTGGGACGGGGCCTGCCAGGGCAAGGAGCTGAGCATT GAACGCATCTGGGGATAACTATCTCTCATAGAAGAACTAATGAGGATTGAACCTGAACACAGAGATGAAA GAGCTGAGTAGACTGCAAAGAATTGCACAAAAACTGCTTGTTCTGCAAATTTAGTTTACAACAATTTGAG TGCAGTTCACATTGGTGTAATGTGATTTAGGATCATAAGTCCCTAAAGTTTCATTACACTGATGAAAAGC AAATGCCTATACTGTTCTTGTTTTATGAGAAGAACGCAACCTCCAGCCCCTGGAGCAGCTGAGATCTGAG GATATCAGGAATACCAGGAATGGGGAGGTCCTGGCTGTCCCTGGAAACCCAGCGGCATGACACCTGTGTC ACCTGGGTCCCCGTTTCTATGCTGGAGTGGCAGGCAGCAAGGAAATATTTTGAGTTTGAGGCCATATGCA

DNA• Unlike a book, which is flat...• Unlike a computer screen, which is flat...• DNA is like a beautiful curved ladder

DNA helix

• The letters of the DNA make up the rungs• Other chemicals make up the rail (backbone)

• A pairs with T• G pairs with C

• Fit like jigsaw puzzle pieces

DNA activity

• Pick a DNA letter• Form DNA String 1 • Find a “DNA friend” to form DNA String 2

A is friend with T (both sharp)G is friend with C (both round)

AA A T T CCCGG ß String 1| | | | | | | | | | T T T A A GGGCC ß String 2

“1 + 1 = 2” in DNA language?• GTGATGGGTGATGGATGGTGGGATCAGTGGGCCCCTGGGAAAGGCCAGGTGTGGCTTCAGCACTCTGGGA

GTTTTGGGTACCTTTATGTCTAGGGACACAAAGAGGTAGATGGACACAGTGGTTTGAGCTGCAGAAGAGA GTTGGGCGGCCACCTGTCCACAGGTGAGGATGAGGTTTCTTGGGAGAGTAGAGAGAAAGGGGAGGCTTTT AACTGTTAGTAGTGGAAAGATGGCTTGCAATGGAGATAAAGGCTACCACAGAGGGGAGGAAAGCCCAGAG CCTGGAGGCCAGGGGGCAGGATCTGGGGCAGGGGCTGACCCAGTGTGGCATTCTCCATATGCTGGAAATG TGAGAAGAGACTCCACAAGCCGGAGCTCCCTCAGCTCCTTGGGGCCGCTGGCTCTACCAGCTCACTCGCT GGTTGTTGGGTTTCCGCAGTTTCCATTTGGGGTTCTGTCACTGACCTGCAGGCAGCCGGCTTGTCGCGGG CTGTGGCTCTTGCGCCGGCCACCCGCGGGACCCGCGCAGTTTCGGCGGAGCGCGGCGGGGTCCGTGTGGG TCCGACCCGTGGGGAGGTGTGGCTGGCGCGCCTGTGCTCTCGGCCCGGCCACCCACTGAGGCGCCAGGAG GCGCATTCCGCAGGGCGGCGGGGAGCAGGTCCGGTGTTTCGAGGAGCATGGGACACAGTTTCCAAGCTGG CCTGCAGGATGGGCGAAGGGACACCGAACTACAGCGGCACAGCGCCGGGCCAAACTGCCCGCAGCTGGGG TAGCAGAGGAGGCTTGCGGCAGCGGCGCCGCGGGGAGGGGTCCTGGCCCCGGGCTGGGAGCGGGGCTGGG CGGCCGCGTGCCCGGCTGCGCGTTTGCTGGCGCTGCTCGTTTCTCCCCGAGAGGTGCGCCTGGCCTGCCG CGGGGCCGCCAGTCCCGGGGGGCCCTGCGAACGCGGCCCTCGGCAGAGCCCACCCGCGGCCTCCCGAGGC CCTCTCCCGGGCCCCGCCCCTCCTCCCAGGCTGGAAGGAGGCGTACTCTTTCAAACAAGAACAGAAAACG GATGGAAAGCCACGATGTTTGGCTTAACGTTCTAGAAGCTTTTAACCAAGTTCACATGTTGAAAAATCCA CCTGAAGGGATATACGGTCACACTCGCGGTCTCGGTCCAGGCCCGAAGTGGGGTGGGGGCACCGCACCCT ACACTGCCCTCAGCGGGACACCCCTGTCTCAGCCCAGGACCGGCTGAGGAGGAGGCTGACCCCTGGACGG GCTTCTTCAGGCCTTCTTGGCCGCAGGGCTTCTCCTGCCCCCTCCCTGGGATCCGGGAACTAGAAGGAAT GCTTCAAATGCAACGGTCACAGCATCGCTCCTGCGGACACCGCCAGGCTCCCGAGACACGGCTACGCCTC CGGCTCACAGTCACGCCCGACACAGACAGACCACCTCCAACGAAAAGCCACGCCCCCGGAGACACGACCA CACCTCGGAGACAGGCCATGCCCCCGGAGACACTACCAAGCCCCAGAGACACGACCACGCCCCAGGAGAC ACAACCACGCCCCGGAGACAAGCCATGCCCCCAGAGACACGGCCACGCCTTGGAGACAAGCCACGCCCCC GAGACCAGCCACCTCCCGGAGACACGACCACGCCCCGGAGACAAGCTACGCCCCCGGAGACACATTGACG CCTTTGCAGACGCAGCCAGGCCCCCGGAGAGAAGGGCCGTGCTCCGAGAGACACAGCCATGCCCTGTGGA CCCAGCCAGGCCCCTGCAAGCATAGCCAGGGGCGGTTTCTCGCTCAGAGGGACGCCTTGGGGCTGAGCAG

(Any kind of) Computer

• Encode data

• Electronic computers

bits 0 and 1

• Perform operations

• Electronic computers

bit flip, test a bit, zero a bit


EbEEncode data PerformPerfPerform operations


DNA Computer

• Input / Output (DNA)§ Data encoded using the DNA

alphabet {A, C, G, T} and synthesized as DNA strands

• Bio-operations§ Cut§ Paste§ Copy§ Anneal§ Recombination

Single-stranded anddouble-stranded DNA


Encoding information

as DNA


Bio-operation example


Adlemans’s Result:The Hamiltonian Path Problem


Hamiltonian Path Problem

• Hamiltonian Path:* Starts at “Start”* End at “End”* Enters every node exactly once

Hamiltonian Path Problem (HPP): “Does a given graph have a Hamiltonian Path?”


Algorithm for solving HPP• Generate random paths in the graph• Keep only those paths that begin with start

and end with end• If the graph has N nodes, keep only those

paths that enter exactly N nodes• Keep only those paths that enter each node

at least once• If any paths remain, say YES, otherwise NO


Example

1) Generate random paths2) Eliminate paths that do not start with 0 or

do not end with 6, like3-4-5-2 and 2-3-4-5-6

3) Keep only paths that enter exactly 7 nodesthat is, eliminate too short or to long paths like0-6 and 0-3-4-5-6 and 0-3-4-5-2-3-4-5-6


Example continued

4) Keep only paths that enter every node at least once, that is, eliminate paths like

0-3-2-3-4-5-65) If there is any path left, say YES, else NO

The only path left is 0-1-2-3-4-5-6The answer is YES.


Encoding the input as DNA: Nodes

• For each node, a 20-letter DNA strand was generated, for example

O2 = TATCGGATCG GTATATCCGA

O3 = GTTATTCGAG CTTAAAGCTAO4 = GGCTAGGTACCAGCATGCTT


Encoding the input as DNA: Arrows

• For each arrow, a DNA sequence was created consisting of

* the second half of the source node * the first half of the target node

O2-3 = GTATATCCGA GCTATTCGAG

o3-4 = CTTAAAGCTA GGCTAGGTAC


Step 1: Generating paths

• By using complements of nodes as splints, compatible edges were ligated

O2-3 O3-4GTATATCCGAGCTATTCGAGCTTAAAGCTAGGCTAGGTAC

| | | | | | | | | | | | | | | | | | | |

CGATAAGCTCGAAT TTCGAT

O3


DNA Paths

• The DNA nodes and DNA arrows fit together like two layers of bricks


Step 2• Keep only those paths with correct start and

end• To implement Step 2, the product of Step 1

was amplified by Polymerase Chain Reaction (PCR) with primers O0 and O6

• Thus only molecules that begin with node 0and end with node 6 were amplified


PCR


Step 3

• Keep only paths of correct length• To implement Step 3, the product of Step 2

was run on an agarose gel• The band corresponding to the path entering

exactly 7 nodes was excised and soaked in water to extract DNA


Step 4• Keep only those paths that pass through

each node at least once• The product of Step 3 was affinity-purified

with a biotin-avidin magnetic beads system• Generate single-stranded DNA from the

double-stranded DNA product of Step 3• Incubate the single stranded DNA with O1

conjugated to magnetic beads• Repeat the process successively: O2... O5


Step 5

• Is there anything left in the test tube?• The product of Step 4 was amplified by

PCR followed by determining the DNA sequence of the amplified process.

• The result was sequenced and the Hamiltonian Path was read out


Note!

• Adleman’s DNA algorithm not only gives a solution to a mathematical problem

• The solved problem is a

Hard Computational Problem!!!



Biomolecular (DNA) Computing• Hamiltonian Path Problem [Adleman, Science, 1994]• DNA-based addition [Guarnieri et al, Science, 1996]• Maximal Clique Problem [Ouyang et al, Science, 1997]• DNA computing by self-assembly [Winfree et al, Nature 1998]• Computations by circular insertions, deletions [Daley et al,1999]• DNA computing on surfaces [Liu et al, Nature, 2000]• Molecular computation by DNA hairpin formation[Sakamoto, Science, 2000]• Programmable and autonomous computing machines made of biomolecules

[Benenson et al, Nature, 2001]• 20-variable Satisfiability [Braich et al., Science 2002]• An autonomous molecular computer for logical control of gene expression

[Benenson et al, Nature, 2004]• 12-tooth DNA gear [Dietz, Douglas, Shih, Science, 2009]• DNA nanotechnology: origami circuit boards [Rothemund, Nature

Nanotechnology, 2009]• DNA nanobots carrying gold particles [Chao et al., Nature 2010]


How Does Nature Compute?

• Technical difficulties encountered in experimental DNA computations (error-detection, error-correction) are routinely solved by biological systems in nature

• Idea: study and utilize the information-storing and computational abilities of unicellular organisms

DNA-encoded information

“ORGANIC DATA MEMORY Using the DNA Approach” – [Wong et al, CACM ’03]•For very long-term storage and retrieval, encode information as artificial DNA strands and insert into living hosts.• Bacteria, even some bugs and weeds,might be good for hundreds of millions of years.

DNA Encodings

• The DNA-encoded English text was inserted into bacteria: Deinococcus Radiodurans –which survive extreme conditions, desiccation, partial vacuum, high doses of radiation

Computer files stored accurately on DNA


Scientists have recorded data such as Shakespearean sonnets and an MP3 file on strands of DNA, in a breakthrough which could see millions of records stored on a handful of molecules rather than computer drives. (N.Goldman, Nature 2013)


Cellular computing

Photo courtesy of L.F. Landweber


• Guided Recombination System = A formal computational model based on contextual circular insertions and deletions

• Such systems have the computational power of Turing Machines (Landweber, Kari, ’99)

• The model is consistent with the limited knowledge of this biological process

Ciliate Computing

Multicellular computing (Tamsir, Tabor, Voigt, Nature 2010)


NOR gates implemented by bacteria and chemical wires


DNA Nanotechnology(Chen, Seeman - Nature, 2001)


Computation by DNA Self-Assembly(Mao, LaBean, Reif, , Seeman - Nature, 2000)


DNA Clonable Octahedron(Shih, Joyce - Nature 2004)


Nanoscale DNA Tetrahedra(Goodman, Turberfield - Science, 2005)


DNA Origami(Rothemund - Nature, 2006)

Fast DNA origami opens way for nanomachines


DNA strands can be coaxed to fold up into shapes in a matter of minutes.The finding could radically speed up progress in the field of DNA origami and nanomachines. (H.Dietz, Science,2012)

Sierpinsky Triangles(Fujibayashi, Hariadi, Park, Winfree, Murata ’07)

• Cellular automaton pattern self-assembled starting from a seed built by DNA origami



Twisted and Curved Shapes(Dietz, Douglas, Shih-Science 2009)


DNA Circuit Boards(Rothemund-Nature Nanotech.2009)

DNA Nanobots Carry Gold (Gu, Chao, Xiao, Seeman, Nature 2010)(G, Chao,

Xiao, Seeman, Nature 2010)



Potential Advantages of DNA Computing

• Information density1 gram of DNA (1 cm3 when dry) = 1 trillion CDs1 lb DNA – more memory then all computers together.

• SpeedThousand to million times faster than an electronic

computer due to massive parallelism• Energy consumption

thousand times more energy efficient

DNA is tiny• The human body has 100 trillion cells• One human cell has 2 meters of DNA• A human body has enough DNA to reach

from Earth to Sun and back, 300 times


IMPACT OF DNA COMPUTING

• Sheds new light into the nature of computation• Opens prospects of radically different

computers• Could lend new insights into the information

processing abilities of cells

Tiny, wet, DNA computers...

how does nature compute? - western universitylila/dnacomputingadleman_2017.pdf · fast dna origami...

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