Importance of Sequence Design Methods Considering Hybridization

Kinetics for in vivo DNA Computers

Toshihiro Kojima and Akira Suyama

Life Sciences and Institute of Physics, University of Tokyo, 3-8-1, 153-8902, Komaba, Meguro-Ku, Tokyo, Japan

Keywords: Sequence Design, DNA Computer, Hybridization Kinetics.

Abstract: A DNA computer is a DNA-based synthetic system inspired by biology. One of the goals of DNA computer

research is to develop an in vivo DNA computer, which can function within living cells through non-

destructively processing intracellular signals under isothermal conditions. DNA computers working in

isothermal conditions need a set of nucleotide sequences satisfying a kinetic condition in addition to the

thermodynamic conditions considered previously, because the progress of computation under isothermal

conditions is often dominated by the rate of nucleic acid hybridization reactions. We thus developed a

method to predict the hybridization reaction rate from nucleotide sequences and have demonstrated

experimentally the importance of hybridization reaction rates and the usefulness of our method. The present

method is general and can be used to develop any hybridization-based DNA/RNA system such as DNA

computers, DNA sensors, DNA nanostructures, and nucleic acid drugs, working in isothermal conditions.

1 DEOXYRIBONUCLEIC ACID

(DNA) BASED SYNTHETIC

SYSTEMS

Recent advances in DNA nanotechnology have

allowed the development of several types of DNA-

based synthetic systems (Stulz et al., 2011). A DNA

computer (Adleman, 1994), for instance, is one such

DNA-based synthetic system that can perform

computations. DNA computers employ nucleic acid

reactions, such as hybridization, strand exchange,

and enzymatic strand synthesis and cleavage

reactions to process input information and produces

output results. Hybridization reactions play an

important role in DNA computers and DNA-based

synthetic systems as they enable the systems to

possess highly programmable attributes.

2 in vivo DNA COMPUTERS

One of the goals of DNA computer research is to

develop an in vivo DNA computer, which can

function within living cells (Benenson, 2012;

Hemphill and Deiters, 2013). In vivo DNA

computers are expected to be widely used in fields

ranging from basic to applied biology and medicine.

For instance, an in vivo DNA computer can be used

as a non-destructive measuring instrument. It can

collect information on cell conditions from mRNAs

transcribed in a cell, and can report the information

outside the cell by enclosing it in vesicles such as

exosomes. An in vivo DNA computer can also be

used to develop intelligent drugs. This computer can

use the information it has collected to produce RNA

and protein molecules as drugs to treat unfavorable

cell conditions or induce differentiation of cells to

form organs.

Fuzzy logic (Kosko and Isaka, 1993) may

provide a theoretical basis for the operation of in

vivo DNA computers. Values of data in vivo DNA

computers can collect in living cells are in fact

fuzzy. In addition, molecular reaction circuits of in

vivo DNA computers cannot perform sufficiently

precise operations, unlike electronic circuits. Thus,

implementation of fuzzy logic may be important for

the operation of in vivo DNA computers.

Certain evolutionary systems may be

successfully combined with in vivo DNA computers

to more closely mimic the process of natural

selection. For instance, an in vivo DNA computer

could automate iterative cycles of mutagenesis,

selection, and amplification processes employed in

the directed evolution (or evolutionary molecular

248

Kojima T. and Suyama A.

Importance of Sequence Design Methods Considering Hybridization Kinetics for in vivo DNA Computers.

DOI: 10.5220/0006246902480252

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 248-252

ISBN: 978-989-758-212-7

engineering) method (Packer and Liu, 2015) to

simulate the evolution of proteins or nucleic acids

toward a user-defined goal. Automation allows the

user to optimize the method.

Figure 1: Central dogma of molecular biology.

3 REVERSE-TRANSCRIPTION-

AND-TRANSCRIPTION-BASED

AUTONOMOUS COMPUTING

SYSTEM (RTRACS)

RTRACS is a modular DNA computer that works

under isothermal conditions using DNA as a

program and RNA as a variable (Nitta and Suyama,

2004; Takinoue et al., 2008). RTRACS provides a

promising framework for in vivo DNA computers,

because the idea of RTRACS was inspired by the

central dogma of molecular biology. The dogma

defines the flow of genetic information within a

biological system (Fig. 1). Genomic DNA storing

genetic information in the form of a DNA sequence

is transcribed onto a messenger RNA (mRNA),

which is then translated into a protein to assemble

replicated systems. Considering information stored

in genomic DNA as a source program and small

pieces of the information transferred into mRNA as

variables, the information flow within a biological

system and that within a computer appear to be

homologous. This implies that a biological system

is itself a DNA computer that uses DNA as a

program and RNA as a variable. RTRACS uses

DNA and RNA in a manner similar to a biological

system. Therefore, RTRACS is highly compatible

with a biological system, and can provide a suitable

framework for in vivo DNA computers.

RTRACS performs computations using the

network or circuit of modularized computational

elements called function modules. In the network,

function modules are connected together by RNA

molecules (Fig. 2). An RTRACS program is written

using the network structure of function modules. A

function module accepts input RNA molecules and

returns output RNA molecules produced through

molecular reactions that include DNA-DNA

hybridizations, DNA-RNA hybridizations, and

enzymatic reactions with reverse transcriptase,

RNase H, and RNA polymerase. The input and

output RNA sequences contain not only the values

for input and output data but also information

regarding the connection of function modules. The

RNA sequences consist of code word sequences

(orthonormal sequences) with high sequence

specificity.

Figure 2: Network of function modules used in RTRACS

to perform computation.

4 RTRACS-BASED in vivo DNA

COMPUTER

We describe here, an example of a synthetic genetic

circuit constructed using RTRACS function

modules. The synthetic circuit is compatible with

endogenous genetic circuits, namely, genetic circuits

within a cell. Thus, the synthetic gene circuit can

work as an in vivo DNA computer, which measures

cellular conditions and applies appropriate action to

the cell in response to these conditions.

The synthetic genetic circuit has two input

mRNAs and one output mRNA translated into a

target protein (Fig. 3a) and works as follows. First,

DNA

RNA

protein

replicati on

transcription

translation

reverse

transcription

Importance of Sequence Design Methods Considering Hybridization Kinetics for in vivo DNA Computers

249

two input mRNAs, X and Y, are encoded as RNA

sequences used in RTRACS. Second, the coded

RNAs are processed by the function module that

performs the AND operation, and an output RNA is

produced according to the value of the operation

result. Third, the produced RNA is decoded into

mRNA of protein Z. Finally, the mRNA is translated

into protein Z. Unlike the natural genetic circuit

inherently present in a cell (Fig. 3b), the synthetic

genetic circuit (Fig. 3a) has an advantage that the

input mRNA molecules are not necessarily the

sequences of transcription factors. In addition, any

protein can be produced as the output. Preliminary

experiments using green fluorescence protein as the

output protein demonstrated that the synthetic

genetic circuit functions within a cell-free protein

synthesis environment.

Figure 3: Regulation of gene expression with transcription

factors. (a) A synthetic mechanism constructed using

RTRACS. (b) A natural mechanism widely employed for

genetic circuits in living cells.

5 SEQUENCE DESIGN FOR in

vivo DNA COMPUTERS

DNA computers need a set of nucleotide sequences

of high hybridization specificity. A reliable design

method for such a sequence set usually adopts the

following three thermodynamic conditions: presence

of normality, orthogonality, and prevention of stable

secondary self-folding. Normality implies that all

sequences within the set have similar melting

temperatures, while orthogonality indicates that any

two sequences in the set, except sequences

complementary to each other, do not hybridize into a

stable double strand with comparable melting

temperature to a double strand formed between

complementary strands. The prevention of stable

secondary self-folding helps sequences take double-

stranded forms with complementary strands rather

than maintain their single-stranded form.

DNA computers working in isothermal

conditions, such as RTRACS and in vivo DNA

computers, need a set of nucleotide sequences

satisfying a kinetic condition in addition to the three

thermodynamic conditions. Namely, all

hybridization reaction rates are required to be

adequate for reliable computation. The progress of

computation under isothermal conditions is often

dominated by the rate of nucleic acid hybridization

reactions. Therefore, various undesirable problems

would occur in computation if the hybridization

reaction rates are not appropriate. For example, a

computation is not completed within the expected

timeline if the computation includes a very slow

hybridization step. Timing problems tend to occur in

parallel computing if the speed of computation

differs largely between processes.

To ensure that the design of nucleotide sequence

sets satisfies the kinetic condition, we developed a

method to predict the rate of hybridization reactions

involved in DNA computation from nucleotide

sequences. The method predicts the rate based on the

nucleation-zippering model of complementary

strand hybridization and can be applied not only to

DNA-DNA hybridization reactions but also to

DNA-RNA and RNA-RNA hybridization reactions.

Figure 4: Schematic diagram of computational processes

in logic gate modules.

mRNA X

mRNA Y

protein Z

ENC

AND

DEC

mRNA X

mRNA Y

transcription

factor X

transcription

factor Y

gene

protein Z

(b)

(a)

Start

XorX'?

YorY'?

output Z output Z'

BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing

250

Figure 5: Output production rates of LGM1 and LGM2. (a) Z and (b) Z´ output of LGM1. (c) Z and (d) Z´ output of LGM2.

The rate is defined as the speed at which a change in the intensity of fluorescence emitted from output RNA occurs. (e) The

input-output relation (truth table) for LGM and line types of the input combinations. The results were obtained as follows.

The reaction mixtures contained 100 mM primer DNA cx that specifically binds to input X, 10 mM primer DNA cx´ that

specifically binds to input X´, 2 mM each of converter DNAs transcribing output Z and Z´, 100 mM each of input RNAs

corresponding to the specified input combination, 250 mM each of 2´-O-methyl molecular beacons with FAM and Cy5

dyes specifically binding to output Z and Z´, respectively, 0.3 unit/μL AMV Reverse Transcriptase (Promega), 1 unit/μL

Hybridase Thermostable RNase H (Epicentre), Thermo T7 RNA polymerase (Toyobo), 5 mM DTT, 0.15 mM of each

dNTP, and 1 mM of each NTP in a 20 μL reaction buffer (40 mM Tris-HCl [pH 8.0], 50 mM NaCl, 9.6 mM MgCl

). The

mixtures were prepared on ice and immediately incubated at 50°C to measure the fluorescence intensity of FAM (Z output)

and that of Cy5 (Z´ output) once every 2 minutes using a real time polymerase chain reaction detection system, CFX96

(Bio-Rad).

The method was applied to design nucleotide

sequence sets for logic gate modules (LGMs) of

RTRACS. LGM can perform various logical

operations including AND, OR, NAND, NOR, INH,

and NINH (Kan et al., 2014). A schematic diagram

of the computational processes forming LGM is

shown in Fig. 5. LGM accepts four combinations of

input RNA molecules: (X, Y), (X, Y´), (X´, Y), and

(X´, Y´). The output RNA molecule Z is produced

only if the input combination is (X, Y). The output

RNA molecule Z´ is produced if the combination is

one of the other three. There are DNA-RNA

hybridization reactions in the decision process of X

or X´, and Y or Y´. We designed two sets of

nucleotide sequences, one for LGM1 and the other

for LGM2. The sequence set for LGM1 was

designed using only the thermodynamic conditions.

In contrast, the set for LGM2 was designed using

both, kinetic and thermodynamic conditions. After

applying the kinetic condition to nucleotide

sequences in the set for LGM1, those sequences that

did not satisfy the kinetic condition were replaced

with those satisfying both, kinetic and

thermodynamic conditions to generate the sequence

set for LGM2.

Experimental results of the operation of LGM1

and LGM2 agreed with the predicted kinetic

properties of their sequences. LGM1 did not work

correctly and produced no output RNA Z for the

input combination (X, Y), while LGM2 correctly

(a) (b)

(e)

100

150

0204060

ProductionRate(a.u./min)

Time(min)

100

150

0204060

ProductionRate(a.u./min)

Time(min)

100

150

0204060

ProductionRate(a.u./min)

Time(min)

100

150

0204060

ProductionRate(a.u./min)

Time(min)

In Out

XY Z

XY′ Z′

X′ YZ′

X′ Y′ Z′

‐‐ ‐

Importance of Sequence Design Methods Considering Hybridization Kinetics for in vivo DNA Computers

251

produced output RNA Z according to the truth table

of LGM (Fig. 6a, c). As for output RNA Z´, LGM1

and LGM2 both, correctly produced the output (Fig.

6b, d). These results were consistent with the

hybridization rate prediction, which stated that the

hybridization reaction rate related to the decision

process of Y in LGM1 only was too slow to produce

output RNA Z promptly. These results demonstrate

the importance of predicting hybridization reaction

rates for the development of LGMs and usefulness

of the present kinetic method.

Our kinetic method to predict the hybridization

reaction rate from nucleotide sequences is general

and its application is not limited to the development

of LGMs of RTRACS. The method can be used to

develop any hybridization-based DNA/RNA system

such as DNA computers, DNA sensors, DNA

nanostructures, and nucleic acid drugs, especially

working in isothermal conditions. The method is

also useful in other areas of biological research, such

as identification of non-coding RNA functions and

understanding their mechanism.

ACKNOWLEDGEMENTS

This work was supported by Grant-in-Aid for

Scientific Research on Innovative Areas [23119007]

to A.S. from the Ministry of Education, Culture,

Sports, Science, and Technology, Japan. H.K.

acknowledges support from the Japan Society for the

Promotion of Science through Program for Leading

Graduate Schools (ALPS).

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