Sequence Design for DNA Computing

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1 Sequence Design for DNA Computing Advanced AI Soo-Yong Shin and Byoung-Tak Zhang Biointelligence Laboratory

2 DNA Hydrogen bonds Hybridization Watson-Crick Complement A single-stranded DNA molecule is a sequence over four possible nucleotides

3 Glossary Terms (in these slides) Duplex Double stranded DNA strands Library A set of DNA strands for DNA computing

4 Role of DNA Sequences Short DNA strands are the units of information storage and manipulation in a computation process. Just like a computer memory Usually, in a DNA computing a long strand is the solution of the given problem, which is a typically concatenation of short DNA strands.

5 Role of DNA Sequences DNA bases DNA strands: represents each city. information Concatenation of DNA strands Computing process We have to design DNA strands very carefully.

6 Sequence Design Design the sequence set that correctly assembly them into the desired longer molecules To form stable duplexes with only their complements. Two distinct strands are non-interacting Between pairs of strands Between a strand and the Watson-Crick complement of another relatively unstable, compared with any perfectly matched duplex formed from a DNA strand and its complement

7 Sequence Design 5 -ATGCATGCAT-3 3 -TACGTACGTA-5 5 -AACCTTGGAC-3 Desired output 5 -ATGCATGCAT-3 3 -TACGTACGTA-5 Unexpected outputs 5 -ATGCATGCAT-3 3 -TAGGATCAGA-5 ΔG > ΔG 3 -TAGGATCAGA-5 5 -ATGCATGCAT-3 3 -TACGTACGTA-5

8 Sequence Design Example GACT TGCA ACGT Q P R CTCA S ACGT P GAGT CAAT CTGA S T Q GTTA T TGAC R 15mer for each variable CTCA S GTTA T GACT TGCA ACGT GAGT CAAT CTGA P R S T Q ACGT P TGAC R CTCA GTTA GACT TGCA ACGT GAGT CAAT CTGA ACGT TGAC S T Q P R P Q R, R? S T Q, S, T, P

9 Sequence Design Example Q P R : 5 3 Q T S : CGT ACG TAC GCT GAA CTG CCT TGC GTT GAC TGC GTT CAT TGT ATG TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT 3 5 S : 5 AAG CAG TAG CGA CCA 3 T : 3 ATT GAC GCA AAT TGA 5 P : 5 GTC AAC GCA AGG CAG 3 R : 5 TGC GTT CAT TGT ATG 3 R : 3 CAT ACA ATG AAC GCA 5

10 Sequence Design Example - Reaction in a Test Tube Q T S T P R P Q S R

11 Sequence Design Example - Hybridization and Ligation Q T S TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT R AGT TAA ACG CAG TTA T AGT R TAA ACG CAG TTA ACC AGC GAT GAC GAA T GTC AAC GCA AGG CAG CAT ACA ATG AAC GCAGTC AAC GCA AGG CAG TTC TTC AGC AGC GTA GTA CGT CGT ACG ACG TCA TCA ATT ATT TGC TGC GTC GTC AAT AAT TGG TGG TCG TCG CTA CTA CTG CTG CTT CTT GTA GTA GTA TGT TGT TGT TAC TAC TAC TTG TTG TTG CGT CGT CGT CAG CAG CAG TTG TTG TTG CGT CGT CGT TCC TCC TCC GTC GTC GTC AAG AAG AAG TCG TCG TCG CAT CAT CAT GCA GCA GCA TGC TGC TGCAGT TAA ACG CAG TTA ACC AGC GAT GAC GAA R R P P Q Q P CAT ACA ATG AAC GCA P GTC AAC GCA AGG CAG S Q T S R T CAT ACA ATG AAC GCA ACC AGC GAT GAC GAA S S

12 Research Directions Theoretical models, to study general properties of libraries Theoretical models, to estimate bounds on the size of a library Algorithms to design the libraries Software tools

13 Theoretical Models Related to the coding problem can be derived from classical theory of codes. Ex) error correcting codes Watson-Crick complementarity is a new feature to be considered. H-system or splicing system

14 Research Directions Theoretical models, to study general properties of libraries Theoretical models, to estimate bounds on the size of a library Algorithms to design the libraries Software tools

15 Strand Design Criteria Preventing undesired reactions Controlling the secondary structures Controlling the chemical characteristics of library Restricting DNA sequences

16 Preventing Undesired Reactions Forces the library to form the duplexes between a given DNA strand and its complement only. Hamming distance Reverse complement Hamming distance Similarity H-measure 3 -end H-measure

17 Preventing Undesired Reactions Similarity Simple Hamming distance with (or without) position shifts Compared the sequences with other sequences for the same direction 5 -ATGCATGC-3 5 -ACCAATCG-3 Similarity = 2 5 -ATGCATGC-3 5 -ACCAATCG-3 Similarity = 3

18 Preventing Undesired Reactions H-measure Simple Hamming distance with (or without) position shifts Compared the sequences with other sequences for the opposite direction To make duplex only at the planned positions 5 -ATGCATGC-3 3 -GCTAACCA-5 H-measure = 1

19 Controlling the Secondary Structures Secondary structures are usually formed by the interaction of single stranded DNA. Internal loop, hairpin loop, bulge loop, and so on. Prediction methods Thermodynamic approach Continuity It can be encouraged or prohibited by the target problem.

20 DNA Structures SantaLucia. Jr., Annu. Rev. Biophys. Biomol. Struct. 2004, 33: , Fig. 1

21 DNA Structures for DNA Computing Self-assembly computation Winfree et al., Nature, 394:

22 DNA Structures for DNA Computing Whiplash PCR

23 Controlling the Secondary Structures Thermodynamic approach Based on nearest neighbor parameters and dynamic programming Mfold Vienna RNA package

24 Controlling the Secondary Structures Continuity Reduce continuous occurrence of the same base more than threshold. If the same base appears continuously, a reaction is not well controllable since the structure of DNA will become unstable. 5 -ATGGGGGCATGC-3 Continuity = 5

25 Controlling the Chemical Characteristics of Library It is desirable to have similar chemical characteristics for the successful DNA operations. Free energy Melting temperature GC ratio

26 Controlling the Chemical Characteristics of Library Free energy The energy to make a duplex Actually, it is defined as the energy required to break a duplex The most reliable measure for the relative stability of a DNA duplex Easily calculated by the nearest neighbor model

27 Controlling the Chemical Characteristics of Library Nearest neighbor parameters SantaLucia. Jr., Annu. Rev. Biophys. Biomol. Struct. 2004, 33: , Table 1

28 Controlling the Chemical Characteristics of Library Melting temperature The temperature at which 50% of DNA strands and its perfect complement are in duplex. T m = S + H R ln( C T / 4) R : Boltzmann s constant (1.987 cal/(k mol)) [C ] ] : total molar strand concentration T : Kelvin H = H S = S ends ends + H + S init init + + k { stacks} k { stacks} H S k k ΔH : Enthalpy ΔS : Entropy

29 Controlling the Chemical Characteristics of Library GC ratio The percentage of G or C in a whole DNA sequence The most simple method, but unreliable 5 -ATGGTTGCATGC-3 GCratio = 50%

30 Restricting DNA Sequences Restriction of the composition (DNA base or subsequence) of a DNA sequence. One of four DNA bases is reserved for the special purposes. Special DNA sequences such as restriction enzyme site

31 Research Directions Theoretical models, to study general properties of libraries Theoretical models, to estimate bounds on the size of a library Algorithms to design the libraries Software tools

32 Sequence Design Algorithm Exhaustive search Random search Template-map strategy Graph method Stochastic methods Dynamic programming Evolutionary algorithms Biological-inspired methods

33 Sequence Design Algorithm Exhaustive search Hartemink et al. DNA4, pp , Random search Penchovsky and Ackermann, Journal of Comput. Biology, 10(2): , 2003.

34 Sequence Design Algorithm Template-map strategy Wenbin Liu et al. J. Chem. Inf. Comput. Sci. 2003, 43, Template Map Sequence T A G C A G C T

35 Sequence Design Algorithm Graph method Feldkamp et al. GPEM 4: , 2003.

36 Sequence Design Algorithm Stochastic methods Simulated annealing Tanaka et al., DNA7, pp , Dynamic Programming Marathe et al., DNA5, pp , 1999.

37 Sequence Design Algorithm Evolutionary algorithm Deaton et al., Physical Review Letters, 80(2): , Shin et al., IEEE Trans. Evolutionary Computation.

38 Sequence Design Algorithm Biological-inspired methods Deaton et al. DNA8, pp , 2002.

39 Sequence Design Algorithm Biological-inspired methods

40 Research Directions Theoretical models, to study general properties of libraries Theoretical models, to estimate bounds on the size of a library Algorithms to design the libraries Software tools

41 Requirements of DNA Sequence Design Systems Sequence reliability User friendliness Analysis capability Sequence reusability

42 NACST/Seq Sequence Generator Based on MOEA Using 6 objectives Each run of MOEA - GC Ratio, Tm, Continuity, Hairpin, H-measure, Similarity Selected Pareto optimal

43 Sequence Generation 1 Generation Options 2 Sequence Structure 3 Sequence Options 6 Genetic Algorithm Options 5 Fitness Parameter Setting 4 Fitness Options

44 NACST/Report 2 Compare two pools - Visualizes the superiority of each fitness comparing sequences of two pools. - User can select pools arbitrarily. 1 Show Fitness Result - Each objective values of generated pools. - User can get fitness information of each pool. 3 Analyze a pool - Shows each nucleotide. - Finds the given subsequence. - Finds the given complementary sequence. - Finds continuous occurrence of each nucleotide. - User can choose a pool arbitrarily.

45 NACST/Plot Comparison graph 1 Project Plot - Plot fitness results. - Plots comparison result of two pools. - User can browse plotting history. - Plotted graphs can be saved as postscript file. 2 Data Plot - Plots arbitrary data from a given file. Fitness results graph Data plot

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