DNA Computing

1. Introduction

            Despite their respective complexities, the biological operations in our body and mathematical operations in a computer have a basic similarity:

• In a living body the very complex structure is the result of applying simple operations to initial information encoded in a DNA sequence called genes.
• In a computer the complex math problem is result of lots of simple operations like addition and subtraction. ^[1]

Because of this reason the first idea of DNA computer was developed with a concept of “DNA strings can also be used to encode information for mathematical systems.” ^[2]

In 1994 Leonard Adleman a computer scientist at the University of Southern California (USC) introduced the idea of using DNA to solve complex mathematical problems. In his paper he came to the conclusion that the DNA had computational potential after reading the book “Molecular Biology of the Gene” by James Watson, who co-discovered he structure of DNA in 1953 ^[5]. Adleman is often called the inventor of DNA computer. His article in a 1994 issue of the journal Science outlined how to use DNA to solve a well-known mathematical problem called the “Hamilton Path Problem”. We will see this problem in next section of this paper.

All computers in existence today make use of binary code 1’s and 0’s, or on’s and off’s on the circuits of a computer chip, forming the basis for every calculation a computer performs, from simple addition to the solution of the most complex differential equations. Adleman saw the possibility for using DNA as molecular computer. However, rather than relying on the position of electronic switches on a microchip, Adleman relied on the much faster reaction of DNA nucleotides binding with their complements, a brute force method that would indeed work.

2. The Hamilton Path Problem

The “Hamilton Path Problem” basically involves finding all the possible paths between a certain numbers of vertices. We can view each vertex as a city, with the problem to find all possible routes for a salesman passing through each of these cities. This problem is also named as “Traveling Salesman Problem”. Adleman used DNA to solve a system of seven vertices, which is not difficult for modern computers, but as the number of cities grows so does the number of paths between them making a 1000 city path impossible to solve for even the best supercomputers.

2.1 The problem

            Figure 1: The Hamilton Path problem. The goal is to find a path from the start city to the end city going through every city only once.

Figure 1 shows a diagram of the Hamilton Path Problem. The objective is to find a path from start to end going through all the points only once. This problem is difficult for conventional serial logic computers because they must try each path one at a time. It is like having a whole bunch of keys and trying to see which fits a lock. Conventional computers are very good at math, but poor at “key into lock” problems. DNA based computers can try all the keys at the same time means parallel and thus are very good at key-into-lock problems. The Hamilton Path Problem was chosen because “every key-into-lock problem can be solved as a Hamilton Path Problem” ^[1].

2.2 Solving the Problem

The following algorithm solves the Hamilton Path problem, regardless of the type of computer

used:

             I.      Generate all possible routes.

          II.      Select itineraries that start with the proper city and end with the final city.

       III.      Select itineraries with the correct number of cities.

       IV.      Select itineraries that contain each city only once^[5].

2.3 Programming with DNA

The key to solving the problem was using DNA to perform the four steps in the above algorithm. We will see how Adleman implemented step-by-step algorithm in his DNA computer. For Example in this case we use a problem of five cities:

 

 

 

 

 

 

 

 

 Figure 2

Suppose that at present we are in Ahmedabad and need to visit four cities: Mumbai, Delhi, Bangalore, and colcuta being our final destination. The airline we are taking has a specific set of connecting flights that restrict which routes we can take. What should our itinerary be if we want to visit each city only once?

            It should take you only a moment to see that there is only one route. Starting from Ahmedabad we need to fly to Delhi, Mumbai, Bangalore, and then to Colcuta. Any other choice of cities will force we to miss a destination, visit a city twice, or not make it to Colcuta. But let us see how DNA will solve the Problem.

Step 1:  Generate all possible routes

Strategy: Encode city names in short DNA sequences. Encode itineraries by connecting the city sequences for which routes exist.

DNA can simply be treated as a string of data. For example, each city can be represented by a "word" of six bases:

Ahmedabad	GCTACG
Delhi	CTAGTA
Mumbai	TCGTAC
Bangalore	CTACGG
Colcuta	ATGCCG

The entire itinerary can be encoded by simply stringing together these DNA sequences that represent specific cities. For example the route from Ahmedabad à Delhi à Mumbai à Bengalore à Colcuta would simply be GCTACGCTAGTATCGTACCTACGGATGCCG, or equivalently it could be represented in double stranded form with its complement sequence.

So how do we generate this? Synthesizing short single stranded DNA is now a routine process, so encoding the city names is straightforward. The molecules can be made by a machine called a DNA synthesizer or even custom ordered from a third party. Itineraries can then be produced from the city encoding by linking them together in proper order. To accomplish this you can take advantage of the fact that DNA hybridizes with its complimentary sequence^[7]. For example, you can encode the routes between cities by encoding the compliment of the second half (last three letters) of the departure city and the first half (first three letters) of the arrival city. For example the route between Bangalore (CTACGG) and Colcuta (ATGCCG) can be made by taking the second half of the coding for Bangalore(CGG) and the first of the coding for Colcuta(ATG). This gives CGGATG. By taking the complement of this you get, GCCTAC, which not only uniquely represents the route from Bangalore and Colcuta but will connect the DNA representing Bangalore and Colcuta. By hybridizing itself to the second half of the code representing Bangalore (...CGG) and the first half of the code representing Colcuta (ATG...). For example:

 

 

 

 

 

 

 

 Figure 3

Random itineraries can be made by mixing city encodings with the route encodings. Finally, the DNA strands can be connected together by an enzyme called ligase^[7]. What we are left with are strands of DNA representing itineraries with a random number of cities and random set of routes. For example:

Figure 4

We can be confident that we have all possible combinations including the correct one by using an excess of DNA encoding.

Step 2:  Select itineraries that start and end with the correct cities.

Strategy: Selectively copy and amplify only the section of the DNA that starts with Ahmedabad and ends with Colcuta by using the Polymerase Chain Reaction.

After Step 1, we now have a test tube full of various lengths of DNA that encode possible routes between cities. What we want are routes that start with Ahmedabad and end with Colcuta. To accomplish this we can use a technique called Polymerase Chain Reaction (PCR), which allows you to produce many copies of a specific sequence of DNA^[5]. PCR is an iterative process that cycles through a series of copying events using an enzyme called polymerase. Polymerase will copy a section of single stranded DNA starting at the position of a primer, a short piece of DNA complimentary to one end of a section of the DNA that you're interested in. By selecting primers that flank the section of DNA you want to amplify, the polymerase preferentially amplifies the DNA between these primers, doubling the amount of DNA containing this sequence. After many iterations of PCR, the DNA you're working on is amplified exponentially. So to selectively amplify the itineraries that start and stop with our cities of interest, we use primers that are complimentary to Ahmedabad and Colcuta What we end up with after PCR is a test tube full of double stranded DNA of various lengths, encoding itineraries that start with Ahmedabad and end with Colcuta.

 

Step 3:  Select itineraries that contain the correct number of cities.

Strategy: Sort the DNA by length and select the DNA whose length corresponds to 5 cities.

Our test tube is now filled with DNA encoded itineraries that start with Ahmedabad and end with Colcuta, where the number of cities in between Ahmedabad and Colcuta varies. We now want to select those itineraries that are five cities long. To accomplish this we can use a technique called Gel Electrophoresis, which is a common procedure used to resolve the size of DNA^[5]. The basic principle behind Gel Electrophoresis is to force DNA through a gel matrix by using an electric field. DNA is a negatively charged molecule under most conditions, so if placed in an electric field it will be attracted to the positive potential. However since the charge density of DNA is constant (charge per length) long pieces of DNA move as fast as short pieces when suspended in a fluid. This is why you use a gel matrix. The gel is made up of a polymer that forms a meshwork of linked strands. The DNA now is forced to thread its way through the tiny spaces between these strands, which slows down the DNA at different rates depending on its length. What we typically end up with after running a gel is a series of DNA bands, with each band corresponding to a certain length. We can then simply cut out the band of interest to isolate DNA of a specific length. Since we known that each city is encoded with 6 base pairs of DNA, knowing the length of the itinerary gives us the number of cities. In this case we would isolate the DNA that was 30 base pairs long (5 cities times 6 base pairs)^[7].

Figure 5^[7]

Step 4:  Select itineraries that have a complete set of cities.

Strategy: Successively filter the DNA molecules by city, one city at a time. Since the DNA we start with contains five cities, we will be left with strands that encode each city once.

DNA containing a specific sequence can be purified from a sample of mixed DNA by a technique called affinity purification. This is accomplished by attaching the compliment of the sequence in question to a substrate like a magnetic bead ^[5]. The beads are then mixed with the DNA. DNA, which contains the sequence you're after then hybridizes with the complement sequence on the beads. These beads can then be retrieved and the DNA isolated.

Figure 6

So we now affinity purifies fives times, using a different city complement for each run^[7]. For example, for the first run we use Ahmedabad-bead to fish out DNA sequences which contain the encoding for Ahmedabad the next run we use Mumbai –beads and then Delhi-beads, Bangalore-beads, and finally Colcuta-beads. The order isn’t important. If an itinerary is missing a city, then it will not be "fished out" during one of the runs and will be removed from the candidate pool. What we are left with are the itineraries that start in Ahmedabad, visit each city once, and end in Colcuta. This is exactly what we are looking for. If the answer exists we would retrieve it at this step

 

2.4 Reading out the Answer

One possible way to find the result would be to simply sequence the DNA strands. However, since we already have the sequence of the city encoding we can use an alternate method called graduated PCR ^[7]. Here we do a series of PCR amplifications using the primer corresponding to L.A., with a different primer for each city in succession. By measuring the various lengths of DNA for each PCR product we can piece together the final sequence of cities in our itinerary. For example, we know that the DNA itinerary starts with Ahmedabad and is 30 base pairs long, so if the PCR product for the Ahmedabad and Mumbai primers was 24 base pairs long, you know Mubai is the fourth city in the itinerary (24 divided by 6). Finally, if we were careful in our DNA manipulations the only DNA left in our test tube should be DNA itinerary encoding Ahmedabad, Delhi, Bangalore, Mumbai, and Colcuta. So if the succession of primers used is Ahmedabad & Delhi, Ahmedabad & Bangalore, Ahmedabad & Mumbai, Ahmedabad & Colcuta, then we would get PCR products with lengths 12, 18, 24, and 30 base pairs.

3. Silicon Vs DNA

DNA, with its unique data structure and ability to perform many parallel operations, allows you to look at a computational problem from a different point of view. Transistor-based computers typically handle operations in a sequential manner. Of course there are multi-processor computers, and modern CPUs incorporate some parallel processing, but in general, in the basic von Neumann architecture computer, instructions are handled sequentially. A von Neumann machine, which is what all modern CPUs are, basically repeats the same "fetch and execute cycle" over and over again; it fetches an instruction and the appropriate data from main memory, and it executes the instruction. It does this many, many times in a row, really, really fast.

Typically, increasing performance of silicon computing means faster clock cycles (and larger data paths), where the emphasis is on the speed of the CPU and not on the size of the memory. For example, will doubling the clock speed or doubling your RAM give you better performance? For DNA computing, though, the power comes from the memory capacity and parallel processing. If forced to behave sequentially, DNA loses its appeal. For example, let's look at the read and write rate of DNA. In bacteria, DNA can be replicated at a rate of about 500 base pairs a second. Biologically this is quite fast (10 times faster than human cells) and considering the low error rates, an impressive achievement. But this is only 1000 bits/sec, which is a snail's pace when compared to the data throughput of an average hard drive. But look what happens if you allow many copies of the replication enzymes to work on DNA in parallel. First of all, the replication enzymes can start on the second replicated strand of DNA even before they're finished copying the first one. So already the data rate jumps to 2000 bits/sec. But look what happens after each replication is finished - the number of DNA strands increases exponentially (2ⁿ after n iterations). With each additional strand, the data rate increases by 1000 bits/sec. So after 10 iterations, the DNA is being replicated at a rate of about 1MB/sec; after 30 iterations it increases to 1000 GB/sec. This is beyond the sustained data rates of the fastest hard drives ^[7].

Now let's consider how you would solve a nontrivial example of the traveling salesman problem (number of cities > 10) with silicon vs. DNA. With a von Neumann computer, one immature method would be to set up a search tree, measure each complete branch sequentially, and keep the shortest one. Improvements could be made with better search algorithms, such as pruning the search tree when one of the branches you are measuring is already longer than the best candidate. A method you certainly would not use would be to first generate all possible paths and then search the entire list. Why? Well, consider that the entire list of routes for a 20 city problem could theoretically take 45 million GB of memory (18! routes with 7 byte words)! Also for a 100 MIPS computer, it would take two years just to generate all paths (assuming one instruction cycle to generate each city in every path). However, using DNA computing, this method becomes feasible! 10¹⁵ is just a nanomole of material, a relatively small number for biochemistry. Also, routes no longer have to be searched through sequentially. Operations can be done all in parallel. ^[7]

 

4. DNA: The unique material

            In this section we will see why the DNA looks as the best material to manufacture next generation microprocessor. Liters of water could contain DNA with more memory than all the computers ever made, and a pound of DNA would have more computing power than all the computers ever made ^[2].

4.1 DNA: A unique Data Structure

The amount of information gathered on the molecular biology of DNA over the last 40 years is almost overwhelming in scope. So instead of getting bogged down in biochemical and biological details of DNA, we'll concentrate on only the information relevant to DNA computing.

The data density of DNA is impressive. Just like a string of binary data is encoded with ones and zeros, we know that a strand of DNA is encoded with four bases, represented by the letters A, T, C, and G. The bases (also known as nucleotides) are spaced every 0.35 nanometers along the DNA molecule, giving DNA an remarkable data density of nearly 18 MB per inch. In two dimensions, if you assume one base per square nanometer, the data density is over one million GB per square inch ^[7]. Compare this to the data density of a typical medium performance hard drive, which is about 7 GB per square inch -- a factor of over 100,000 smaller.

Another important property of DNA is its double stranded nature. The bases A and T, and C and G, can bind together, forming base pairs. Therefore every DNA sequence has a natural complement. For example if sequence S is ATTACGTCG, its complement, S', is TAATGCAGC. Both S and S' will come together (or hybridize) to form double stranded DNA. This complementarities makes DNA a unique data structure for computation and can be exploited in many ways. Error correction is one example. Errors in DNA happen due to many factors. Occasionally, DNA enzymes simply make mistakes, cutting where they shouldn't, or inserting a T for a G. DNA can also be damaged by thermal energy and UV energy from the sun. If the error occurs in one of the strands of double stranded DNA, repair enzymes can restore the proper DNA sequence by using the complement strand as a reference. In this sense, double stranded DNA is similar to a RAID 1 array, where data is mirrored on two drives, allowing data to be recovered from the second drive if errors occur on the first. In biological systems, this facility for error correction means that the error rate can be quite low. For example, in DNA replication, there is one error for every 10⁹ copied bases or in other words an error rate of 10^-9. (In comparison, hard drives have read error rates of only 10^-13 for Reed-Solomon correction)^[7].

 

4.2 The Parallel Operations

In the cell, DNA is modified biochemically by a variety of enzymes, which are tiny protein machines that read and process DNA according to nature's design. There is a wide variety and number of these "operational" proteins, which manipulate DNA on the molecular level. For example, there are enzymes that cut DNA and enzymes that paste it back together. Other enzymes function as copiers, and others as repair units. Molecular biology, Biochemistry, and Biotechnology have developed techniques that allow us to perform many of these cellular functions in the test tube. It's this cellular machinery, along with some synthetic chemistry, that makes up the palette of operations available for computation. Just like a CPU has a basic suite of operations like addition, bit-shifting, logical operators (AND, OR, NOT NOR), etc. that allow it to perform even the most complex calculations, DNA has cutting, copying, pasting, repairing, and many others. And note that in the test tube, enzymes do not function sequentially, working on one DNA at a time. Rather, many copies of the enzyme can work on many DNA molecules simultaneously. This is the power of DNA computing, that it can work in a massively parallel fashion.

4.3 DNA: The Genetic Gate

Three years after Adleman's experiment, researchers at the University of Rochester developed Logic Gates made of DNA^[3]. Logic gates are a vital part of how your computer carries out functions that you command it to do. These gates convert binary code moving through the computer into a series of signals that the computer use to perform operations. Currently, logic gates interpret input signals from Silicon Transistors, and convert those signals into an output signal that allows the computer to perform complex functions.

The Rochester team's DNA logic gates are the first step toward creating a computer that has a structure similar to that of an electronic PC. Instead of using electrical signals to perform logical operations, these DNA logic gates rely on DNA code. They detect fragments of genetic material as input, splice together these fragments and form a single output. For instance, a genetic gate called the "And gate" links two DNA inputs by chemically binding them so they're locked in an end-to-end structure, similar to the way two Legos might be fastened by a third Lego between them. The researchers believe that these logic gates might be combined with DNA microchips to create a breakthrough in DNA computing.

4.4 A Successor to silicon

Silicon microprocessors have been the heart of the computing world for more than 40 years. In that time, manufacturers have crammed more and more electronic devices onto their microprocessors. In accordance with Moore's Law, the number of electronic devices put on a microprocessor has doubled every 18 months. Moore's Law is named after Intel founder Gordon Moore, who predicted in 1965 that microprocessors would double in complexity every two years. Many have predicted that Moore's Law will soon reach its end, because of the physical speed and miniaturization limitations of silicon microprocessors. DNA computers have the potential to take computing to new levels, picking up where Moore's Law leaves off.

As long as there are cellular organisms, there will always be a supply of DNA. The large supply of DNA makes it a cheap resource. Unlike the toxic materials used to make traditional microprocessors, DNA biochips can be made cleanly. DNA computers are many times smaller than today's computers.

DNA's key advantage is that it will make computers smaller than any computer that has come before them, while at the same time holding more data ^[3]. One pound of DNA has the capacity to store more information than all the electronic computers ever built; and the computing power of a teardrop-sized DNA computer, using the DNA logic gates, will be more powerful than the world's most powerful supercomputer. More than 10 trillion DNA molecules can fit into an area no larger than 1 cubic centimeter (0.06 cubic inches). With this small amount of DNA, a computer would be able to hold 10 terabytes of data, and perform 10 trillion calculations at a time. By adding more DNA, more calculations could be performed.

5. The Progress

Since DNA computing was first demonstrated in 1994, fewer than a dozen U.S. research teams have launched molecular computing projects. Together, they have a combined budget of only about $1 million. But now this field is in progress and institutions allocates very high budgets to scientists.

5.1 The first experiment

On November 11 1994 Dr. Adleman who represented his first report on his experiment explained above in section Hamilton Path Problem, performs calculations on 7 cities and his DNA computer takes about a week to complete the problem. With a paper and pen we can solve this problem in a while than how we can make trust on speed of DNA based computers? From First computer Dr. Adleman just want to prove that we can use the DNA for computation. “It is too early for either great optimism of great pessimism. Early computers such as ENIAC filled entire rooms and had to be programmed by punch cards. Since the time. Computers have since become much smaller and easier to use. The same way DNA computer will also used for complex problems and for commercial use.” Said Dr. Adleman. ^[5]

5.2 DES: The Target

After about a year from solving his “toy problem”, Dr. Adleman teamed up with molecular biologist Myron Goodman, a professor in the college of Letters Arts and sciences. Goodman specializes in the natural systems that protect the genetic messages written in DNA form errors in transcription or copying.

The Foundation has committed a $230,000 start-up grant to the new laboratory ^[8]. After that continuously new grants was given to Adleman and luxury of laboratory is also with him. His initial experiment was done in borrowed quarters.

In the new laboratory, Adleman and Goodman are going after a real problem- they ultimately hope to use DNA to decode a scrambled message using the national Data Encryption Standard (DES). The DES was designed to be un-crackable by even the largest and fastest existing supercomputers. Adleman and Goodman hope to solve the problem in a matter of months.

"It's clearly theoretically possible," said Goodman. "The question is whether the chemical operations can actually be done with a low-enough error frequency. In my opinion, this can be achieved." Adleman's method works by taking advantage of the fact that information can be written onto individual DNA molecules, using the alphabet of four bases that all living things use to record genetic information. According to Goodman, the fastest supercomputers now available can perform about 10⁹ operations per second. By using DNA molecules, he and Adleman hope to achieve effective speeds of as much as 10¹⁷ operations per second - enough to crack the DES problem ^[8].

5.3 After Three Years

Since Dr. Adleman's success three years ago, other researchers have flocked to the field, enticed in part by grants from the National Science Foundation and the Pentagon's Defense Advanced Research Projects Agency. Much of the military's interest arises from the increasing sophistication of encryption techniques that other countries can use to encode their data. As a result, Washington needs ever-more-powerful computers for code breaking.

Three researchers -- Richard J. Lipton, a computer-science professor at Princeton University, Daniel Boneh, an assistant professor of computer science at Stanford University, and Christopher T. Dunworth, a computer-science doctoral student at Princeton -- have outlined a way for a DNA computer to crack messages coded with the U.S. government's own Data Encryption Standard.

"It's a different way of thinking about computing. It's a different way of thinking about chemistry," says Dr. Corn, a chemistry professor at the University of Wisconsin at Madison who is collaborating on a DNA-computer project with three other Madison faculty members: Lloyd M. Smith, a chemistry professor; Max G. Lagally, a materials-science professor; and Anne E. Condon, a computer-science professor

The gold-coated square of glass in Robert M. Corn's hand doesn't look like a memory chip-or like any other computer component, for that matter. But the glass, less than an inch square, is one key to building a radically different kind of computer.^[9]

5.4 Now Complex Problems

Scientists have created a "DNA computer" from strands of synthetic DNA they coaxed into solving relatively complex calculations, according to a report in 13^th January 2000’s issue of the journal Nature. The short-lived chemical computer has no immediate practical applications, but it nudges the fledgling technology of DNA computing further out of world of science fiction and into the realm of the possible, the University of Wisconsin-Madison researchers said.

Laura F. Landweber, an assistant professor of biology at Princeton University, is leading a team working to exploit RNA's computing potential. Her team recently fashioned RNA strands that processed complex problems similar to those that chess players encounter.

While Lloyd Smith's team produced a chemical computer that tackled a problem with 16 possible solutions, the Princeton RNA computer searched through 512 possible answers, she said. The research was published in year 2000 in the journal Proceedings of the National Academy of Sciences.

5.5 Olympus Develops DNA computer

In Starting of year 2002 the Olympus Optical Co. Ltd. Developed what the company claimed the

commercially practical DNA computer that specializes in gene analysis. The computer was developed in conjunction with Akira Toyama, an assistant professor at Tokyo University.

Gene analysis has been usually done manually, by arranging DNA fragments and observing the chemical reactions. But that was time-consuming, said Satoshi Ikuta, a spokesman of Olympus Optical. When DNA computing is applied to gene analysis what used to take three days can now be done in six hours, he said. DNA computing also allows scientists to observe chemical reaction that occur simultaneously, lowering the research costs.

The bottleneck was that engineers were required to have expert knowledge in two specific fields, in order to develop a gene analysis DNA computer.

             I.      Information Processing Engineering

          II.      Molecular Biology

This is called genome informatics as a whole.

To achieve this, the company formed a joint venture NovousGene Inc. Which specializes in genome informatics, in February 2001. The principles for a DNA computer that works for gene analysis were provided by Tokyo University’s Toyama.

The computer Olympus Optical has developed is divided into two sections, a molecular calculation component and an electronic calculation component. He former calculates DNA combinations of molecules, implements chemical reactions, searches and pulls out the right DNA the latter executes processing programs and analyzes these results.

The company was started gene analysis using the DNA computer on a trial basis for a year, and form this year hopes to offer the service on a commercial basis for researchers.

7. Conclusion

On the side of the "hardware", improvements in biotechnology are happening at a rate similar to the advances made in the semiconductor industry. With the amount of government funded research dollars flowing into genetic-related R&D and with the large potential payoffs from the lucrative pharmaceutical and medical-related markets, this isn't surprising. Just look at the number of advances in DNA-related technology that happened in the last five years. Today we have not one but several companies making "DNA chips," where DNA strands are attached to a silicon substrate in large arrays (for example Affymetrix’s genechip). Production technology of MEMS is advancing rapidly, allowing for novel integrated small scale DNA processing devices. The Human Genome Project is producing rapid innovations in sequencing technology. The future of DNA manipulation is speed, automation, and miniaturization.

Considering all the attention that DNA has garnered, it isn’t too hard to imagine that one day we might have the tools and talent to produce a small integrated desktop machine that uses DNA, or a DNA-like biopolymer, as a computing substrate along with set of designer enzymes. Perhaps it won’t be used to play Quake IV or surf the web -- things that traditional computers are good at -- but it certainly might be used in the study of logic, encryption, genetic programming and algorithms, automata, language systems, and lots of other interesting things that haven't even been invented yet.

So will DNA ever be used to solve a traveling salesman problem with a higher number of cities than can be done with traditional computers? Will Ruy asked this question in his paper in 1997. But now this is no a question. The new question is when DNA will overtake conventional computation and set a new record? Can ever common men uses DNA computers in place of current silicon based PCs? By seeing progress of last 9 years we are very hopeful about it.