In-vitro Molecular Computing Based on DNA Strands An unconventional - - PowerPoint PPT Presentation

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In-vitro Molecular Computing Based on DNA Strands An unconventional - - PowerPoint PPT Presentation

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives In-vitro Molecular Computing Based on DNA Strands An unconventional computing concept inspired by nature PD Dr. Thomas Hinze Brandenburg University of Technology Cottbus

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SLIDE 1

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

In-vitro Molecular Computing Based on DNA Strands

An unconventional computing concept inspired by nature PD Dr. Thomas Hinze

Brandenburg University of Technology Cottbus – Senftenberg Institute of Computer Science and Information and Media Technology

thomas.hinze@b-tu.de

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 2

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

high storage density up to

1 bit / nm3

L.M. Adleman. Molecular Computation of Solutions to Combinatorial Problems. Science 266:1021-1024, 1994 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 3

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

A C G T

high storage density up to

1

base pair in strand of deoxyribonucleic acid (DNA)

00 01 10 11

2 bit per nucleotide or per

bit / nm3

L.M. Adleman. Molecular Computation of Solutions to Combinatorial Problems. Science 266:1021-1024, 1994 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 4

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

A A A A C A C C T G T T T G T G

A C G T

high storage density up to

1

base pair in strand of deoxyribonucleic acid (DNA)

00 01 10 11

2 bit per nucleotide or per 5’ 5’ 3’ 3’ 2 nm3 / base pair

bit / nm3

L.M. Adleman. Molecular Computation of Solutions to Combinatorial Problems. Science 266:1021-1024, 1994 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 5

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

A A A A C A C C T G T T T G T G

A C G T

high storage density up to

1

base pair in strand of deoxyribonucleic acid (DNA)

00 01 10 11

2 bit per nucleotide or per electronic flash memory card

3

0.001 bit / nm corresponds to 128 Gbit / 146 qmm 5’ 5’ 3’ 3’ 2 nm3 / base pair

bit / nm3

L.M. Adleman. Molecular Computation of Solutions to Combinatorial Problems. Science 266:1021-1024, 1994 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 6

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

high storage persistence up to several thousand years

  • W. Miller et al. Sequencing the nuclear genome of the extinct woolly mammoth. Nature 456:387-391, 2008

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 7

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

under optimal environmental conditions

high storage persistence up to several thousand years

  • W. Miller et al. Sequencing the nuclear genome of the extinct woolly mammoth. Nature 456:387-391, 2008

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 8

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

www.zmescience.com

from >20,000 years old mammoth

  • approx. 80% of genome reconstructed

under optimal environmental conditions

high storage persistence up to several thousand years

  • W. Miller et al. Sequencing the nuclear genome of the extinct woolly mammoth. Nature 456:387-391, 2008

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 9

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

www.zmescience.com

from >20,000 years old mammoth

  • approx. 80% of genome reconstructed

high storage persistence up to several thousand years

floppy disk: hard disk: DVD (expected): 5 ... 10 years 10 ... 15 years 30 ... 50 years under optimal environmental conditions

  • W. Miller et al. Sequencing the nuclear genome of the extinct woolly mammoth. Nature 456:387-391, 2008

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 10

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

highly efficient chemical processing by low energy consumption

  • L. Kari. Arrival of biological mathematics. The Mathematical Intelligencer 19(2):9-22, 1997

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 11

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

: hydrogen bond : covalent bond Guanine Cytosine 3’ 3’ 5’ Thymine 5’ Adenine

A T G C A T G C A G T C

(break or form internucleotide chemical bond) per Joule elementary operations

up to 1018 highly efficient chemical processing by low energy consumption

O N C C C N N N CH O C H H N H N C C N N N CH CH H H N C O N H C N C C C H N CH

3

H N C O N C C C H O CH

2

CH CH CH

2

CH

2’ 3’ 1’ 4’ 5’

O CH

2

CH CH CH

2

CH

2’ 3’ 1’ 4’ 5’

OH O P O O O OH OH O CH

2

CH CH CH

2

CH

4’ 5’ 1’ 3’ 2’

CH

2

CH CH CH

2

CH

4’ 5’ 1’ 3’ 2’

OH P O O O O O

  • L. Kari. Arrival of biological mathematics. The Mathematical Intelligencer 19(2):9-22, 1997

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

slide-12
SLIDE 12

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

: hydrogen bond : covalent bond Guanine Cytosine 3’ 3’ 5’ Thymine 5’ Adenine

A T G C A T G C A G T C

(break or form internucleotide chemical bond) per Joule elementary operations

up to 1018 1016

1

bit set

  • r reset

electronically

highly efficient chemical processing by low energy consumption

O N C C C N N N CH O C H H N H N C C N N N CH CH H H N C O N H C N C C C H N CH

3

H N C O N C C C H O CH

2

CH CH CH

2

CH

2’ 3’ 1’ 4’ 5’

O CH

2

CH CH CH

2

CH

2’ 3’ 1’ 4’ 5’

OH O P O O O OH OH O CH

2

CH CH CH

2

CH

4’ 5’ 1’ 3’ 2’

CH

2

CH CH CH

2

CH

4’ 5’ 1’ 3’ 2’

OH P O O O O O

  • L. Kari. Arrival of biological mathematics. The Mathematical Intelligencer 19(2):9-22, 1997

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 13

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

massive data parallelism

L.M. Adleman. Molecular Computation of Solutions to Combinatorial Problems. Science 266:1021-1024, 1994 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 14

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

DNA as an Excellent Data Storage Medium

up to 1015

molecules DNA and other

massive data parallelism

DNA strands per test tube (2ml) simultaneous and autonomous molecular interactions without central control

L.M. Adleman. Molecular Computation of Solutions to Combinatorial Problems. Science 266:1021-1024, 1994 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 15

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

  • n DNA

Operations 2.

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 16

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Operations on DNA (Selection)

Gaining DNA strands

  • Synthesis (oligos)

5’−ACGGAAC−3’ A C G G A A C

  • Isolation (like plasmids or genomic DNA from organisms)

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 17

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Operations on DNA (Selection)

Gaining DNA strands

  • Synthesis (oligos)

5’−ACGGAAC−3’ A C G G A A C

  • Isolation (like plasmids or genomic DNA from organisms)

Handling DNA solutions

  • Union (merge)
  • Split (aliquot)

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 18

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Operations on DNA (Selection)

Gaining DNA strands

  • Synthesis (oligos)

5’−ACGGAAC−3’ A C G G A A C

  • Isolation (like plasmids or genomic DNA from organisms)

Handling DNA solutions

  • Union (merge)
  • Split (aliquot)

Forming and breaking hydrogen bonds

  • Annealing (hybridisation)

T T G C C T T A C G G A A C T A T C G G C G C A T A T C

  • Melting (denaturation)

T A T C G G C G C A T A T C T T G C C T T A C G G A A C

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 19

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Operations on DNA (Selection)

Enzymatically catalysed reactions

  • Ligation (concatenation)................

T A C G G C A

P

T A T T A

P

T A C G G C T A A T T A

  • Digestion (cleavage).....................

A T G C C G G C C G A T HinP1 I A T G C G C

P

C G C G A T

P

G C C G G C C G 5’ 3’ 3’ 5’
  • Labelling (strand end modification)

A T G C C G G C C G A T A T G C C G G C C G A T

P P

+ 5’−Phosphat

  • Polymerisation (completion)..........

A G C G G C C A A T G C C G G C

  • PCR (polymerase chain reaction)...

........ duplicate strands

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 20

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Operations on DNA (Selection)

Enzymatically catalysed reactions

  • Ligation (concatenation)................

T A C G G C A

P

T A T T A

P

T A C G G C T A A T T A

  • Digestion (cleavage).....................

A T G C C G G C C G A T HinP1 I A T G C G C

P

C G C G A T

P

G C C G G C C G 5’ 3’ 3’ 5’
  • Labelling (strand end modification)

A T G C C G G C C G A T A T G C C G G C C G A T

P P

+ 5’−Phosphat

  • Polymerisation (completion)..........

A G C G G C C A A T G C C G G C

  • PCR (polymerase chain reaction)...

........ duplicate strands Separation and analysis of DNA strands

  • Affinity purification (sep. by biotin)..

A C G C G T A A T G C T C

B T

A A T T A + 5’−Biotin T C

B T

A A T T A

  • Gel electrophoresis (sep. by length)

sort and detect strands

  • Sequencing (readout).......................

A C G G A A C 5’−ACGGAAC−3’

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

NP−hard Knapsack Problem Algorithm for Solution to the 3.

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Knapsack Problem

NP-hard decision problem, exponential need of resources Definition Given n natural numbers a1, ..., an and reference number b ∈ N Is there a subset I ⊆ {1, ..., n} with

i∈I

ai = b ? Explanation a1, ..., an: weights of objects 1, ..., n. Is there a possibility to pack a selection of these objects into the knapsack which exactly meets the reference weight b? Example

.

9 9 5 7 7

2 2

a = 7 a = 2 b = 9

1 2 3

a = 5

?

  • bject 2
  • bject 1
  • bject 3

"yes" solution

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Idea of Wetware Solution to Knapsack Problem

Brute force approach

  • Encode a1, . . . , an into DNA double strands by length (c · ai)
  • Generate all solution candidates by a controlled

split-and-combine strategy

  • Separate final DNA pool by length
  • Detect DNA at Starter length + c · b and answer yes

by length separation

10 20 30 5 15 25 35

a1 Starter Starter a1 Starter Starter Starter Starter Starter a1 a1 a2 a2 a2 Starter Starter Starter Starter Starter Starter Starter Starter a1 a2 a1 a2 a1 a2 a1 a2 DNA operations DNA operations DNA operations a3 a3 a3 a3 a3

  • M. Sturm, T. Hinze. Verfahren zur Ausführung von mathematischen Operationen mittels eines DNA-Computers und

DNA-Computer hierzu. Deutsches Patent DE 101 59 886 B4, IPC G06N 3/00, erteilt 2010 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Split-and-combine Strategy

doubles number of combinations by addition of one object

Union Ligation Split Union DNA pool composed of combinations from k objects start with k = 0 (starters only) DNA pool composed of combinations from k+1 objects Labelling 5’+P add object a_(k+1)

Starter Starter Starter Starter a1 a1 a2 a2 a2 Starter a1 Starter a2 a2 a1 Starter Starter In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Simple Implementation of a Problem Instance

  • n = 3 objects taken from plasmid (pQE30 cleaved with PvuII)
  • a1 = 719, a2 = 393, a3 = 270, b = 1112, c = 1
  • Exponential need of resources moved from time to space
  • Final sequencing of DNA band corresponding to b reveals “yes”
  • Limited scalability of the algorithm due to side effects and

amount of DNA

100 1000 1200 1 719 bp 393 bp 270 bp 750 bp 500 bp 250 bp 150 bp bp 270

270

393

393

663

393 270

719

719 719 270 719 393 719 393 270

989 1112 1382 bp 2 1 2 3

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Side Effects and Perturbations

prevent DNA operations from running in an ideal manner

  • Loss of DNA
  • Incomplete reactions
  • Non-specificities
  • Malformed DNA (artefacts)
  • DNA damage
  • Contaminations or impurities of DNA solutions
  • . . . (many others)

Coping with side effects is a hard challenge to overcome in practical in-vitro DNA computation. Assuming an error rate of 5% per operation and having a sequence of 10 operations, the

  • verall success rate is merely 0.9510 · 100 ≈ 60%.

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Trends, and Perspectives Further Applications, 4.

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 28

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Milestones of DNA-based Computing

  • Pioneering era after Adleman’s experiment
  • Refinement and improvement of techniques and encoding

schemes complemented by much theoretical work

Adleman’s DNA−based solution to Hamiltonian path problem (n = 7 nodes)

www.usc.edu

1994 DNA−based solution to satisfiability problem by brute force approach (n = 20 variables)

www.sciencemag.org

2002 In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 29

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Milestones of DNA-based Computing

  • Pioneering era after Adleman’s experiment
  • Refinement and improvement of techniques and encoding

schemes complemented by much theoretical work

  • Addressable DNA-based memory able to store data from files

Adleman’s DNA−based solution to Hamiltonian path problem (n = 7 nodes)

www.usc.edu

1994 DNA−based solution to satisfiability problem by brute force approach (n = 20 variables)

www.sciencemag.org

2002 DNA chips for massive parallel computing by string matching

  • approx. 40,000 spots

2010

www.newscientist.com

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 30

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Milestones of DNA-based Computing

  • Pioneering era after Adleman’s experiment
  • Refinement and improvement of techniques and encoding

schemes complemented by much theoretical work

  • Addressable DNA-based memory able to store data from files
  • Computing by DNA self-assembly promising clue towards

freely programmable nanomachines

Adleman’s DNA−based solution to Hamiltonian path problem (n = 7 nodes)

www.usc.edu

1994 DNA−based solution to satisfiability problem by brute force approach (n = 20 variables)

www.sciencemag.org

2002 DNA chips for massive parallel computing by string matching

  • approx. 40,000 spots

2010

www.newscientist.com

algorithmic self−assembly for cellular automaton and universal DNA computer 2014

PLoS Biology

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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Motivation Operations on DNA Solution to Knapsack Problem Perspectives

International Molecular Computing Community

≈ 500 researchers worldwide, conference series like CMC, DNA, UC, . . .

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 32

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Take Home Message Living organisms comprise almost perfect DNA-based computers. We are going to learn and to adapt the underlying principles for utilisation in vitro. There are first successes but there is still a lot of work to do.

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze

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SLIDE 33

Motivation Operations on DNA Solution to Knapsack Problem Perspectives

Take Home Message Living organisms comprise almost perfect DNA-based computers. We are going to learn and to adapt the underlying principles for utilisation in vitro. There are first successes but there is still a lot of work to do.

Further and more detailed information

  • T. Hinze, M. Sturm. Rechnen mit DNA -

Eine Einführung in Theorie und Praxis. De Gruyter, eBook, 2014

  • T. Hinze. Computer der Natur.

bookboon.com, eBook (for free), 2013

In-vitro Molecular Computing Based on DNA Strands Thomas Hinze