7. COMPARISON OF DNA AND CONVENTIONAL ELECTRONIC COMPUTERS

7. COMPARISON OF DNA AND CONVENTIONAL ELECTRONIC COMPUTERS
As we have discussed the concepts and characteristics of DNA Computer, we can now compare the DNA Computers with Conventional Electronic Computers.
7.1) Similarities
1) Transformation of Data:
Both DNA computers and electronic computers use Boolean logic (AND, OR, NAND, NOR) to transform data. The logical command "AND" is performed by separating DNA strands according to their sequences, and the command "OR" is done by pouring together DNA solutions containing specific sequences. For example, the logical statement "X or Y" is true if X is true or if Y is true. To simulate that, the scientists would pour the DNA strands corresponding to "X" together with those corresponding to "Y."[2][3]. Following is an example of how a Bio Chemical Inverter works.
Bio-chemical Inverter: The characteristics of natural gene regulation systems can be exploited to design in vivo logic circuits (Weiss et al., 1999).
How a biochemical inverter achieves the two states in digital inversion using genetic regulatory elements? Here, the concentration of a particular messenger RNA (mRNA) molecule represents a logic signal. In the first case, the input mRNA is absent and the cell transcribes the gene for the output mRNA using RNA polymerase (RNAp) molecules. In the second case, the input mRNA is present and the cell translates the input mRNA into the input protein using ribosomes.
A digital inverter that consists of a gene encoding the instructions for protein B and containing a region (P) to which protein A binds. When A is absent (left)—a situation representing the input bit 0—the gene is active and B is formed—corresponding to an output bit 1. When A is produced (right)—making the input bit 1—it binds to P and blocks the action of the gene—preventing B from being formed and making the output bit 0.
The input protein then binds specifically to the gene at the promoter site (labeled \P") and prevents the cell from synthesizing the output mRNA.
Now more complete picture that explains the role of transcription and translation cellular processes in inversion is explained here.
Biochemical inversion uses the transcription and translation cellular processes. Ribosomal RNA translates the input mRNA into an amino acid chain, which then folds into a three-dimensional protein structure. When the protein binds an operator of the gene's promoter, it prevents transcription of the gene by RNA polymerase (RNAp). In the absence of the repressor protein, RNAp transcribes the gene into the output mRNA.
It depicts a functional model of the inverter derived from its biochemical reaction phases. The first phase in inversion is the translation stage, denoted as L. The input signal to this stage, and thus the inverter, corresponds to the concentration level of the input mRNA, φA. Ribosomal RNA (rRNA) translates the input mRNA into the input repressor protein, ψA , where L represents the steady state mapping between the mRNA and protein concentrations. The relationship between the input mRNA and repressor protein is initially linear, with increases in φA corresponding to increases in ψA, until an asymptotic boundary is reached. The properties of this boundary are determined by characteristics of the cell such as amino acid synthesis capabilities, the efficiency of the ribosome-binding site, and mRNA stability. Since cells degrade mRNA as well as protein molecules, constant synthesis of the input mRNA is needed to maintain a steady level of the input repressor protein. In the second phase, input protein monomers combine to form polymers that bind the operator, and subsequently repress the transcription of the output gene.
The Functional composition of the inversion stages: the translation stage maps input mRNA levels (ψA) to input protein levels (φA), the cooperative binding stage maps input protein levels to bound operator levels (ρA), and the transcription stage maps bound operator levels to output mRNA levels (ψZ). The degradation of the mRNA and protein molecules is represented with the electrical ground symbol. The degradation of mRNA is part of the translation stage, while the degradation of proteins is part of the cooperative binding stage. The graphs illustrate the steady-state relationships for each of these stages and the overall inversion function that results from combining these stages.
This cooperative binding, which ensures that only dimerized proteins can bind the DNA, decreases the digital noise. Let us define the concentration of operator that is bound to the repressor, or the strength of the repression, as ρA. In addition, denote the cooperative binding stage that occurs between ψA and ρA as C. In steady state, the relationship between ψA and ρA is sigmoidal. At low levels of ψA, the strength of repression does not increase significantly for increases in ρA because these concentrations are too low for appreciable dimerization. At higher concentrations of ψA, however, considerable dimerization occurs, resulting in a nonlinear increase in repression activity. For values of ψA approaching saturation, the operator is mostly bound, and repressor activity is close to maximal. At this point, increasing the concentration of ψA does not increase repression, and instead causes the ψA/ρA curve to move toward an asymptotic boundary. In this way, the cooperative binding stage performs signal restoration in which the analog output signal better represents the appropriate digital meaning than the corresponding analog input signal. Because each stage of the computation reduces the noise in the system through signal restoration, multiple inverters can be combined into more complex circuits, while still maintaining or even increasing the overall reliability of the system.
In the final stage of the inverter, the transcription stage, RNA polymerase (RNAp) transcribes the regulated gene and the input signal is inverted. Let us define Z to be the output signal of the inverter and ψZ to be its corresponding mRNA concentration. The transcription stage, with input ρA and output φZ, has a steady state relationship in which increases in ρA correspond to monotonic decreases in φZ. During periods of minimal repression, transcription progresses at rapid rates resulting in maximal concentrations of φZ. However, for high levels of repression, the transcriptional activity declines and the level of φZ drops.
Overall, the three stages combine to form a system that behaves as an inverter, negating the input mRNA signal, φA, to yield the output mRNA signal, φZ. Furthermore, with efficient signal restoration during the cooperative binding stage of inversion, complex but reliable digital logic circuits are attainable.

2) Manipulation of Data:-
Electronic computers and DNA computers both store information in strings, which are manipulated to do processes. Vast quantities of information can be stored in a test tube. The information could be encoded into DNA sequences and the DNA could be stored. To retrieve data, it would only be necessary to search for a small part of it - a key word, for example – by adding a DNA strand designed so that its sequence sticks to the key word wherever it appears on the DNA [3].

3) Computation Ability:-
All computers manipulate data by addition and subtraction. A DNA computer should be able to solve a satisfiability problem with 70 variables and 1,000 AND-OR connections. To solve it, assign various DNA sequences to represent 0’s and 1’s at the various positions of a 70 digit binary number. Vast numbers of these sequences would be mixed together, generating longer molecules corresponding to every possible 70-digit sequence [2][3].

7.2) Differences

1) Size:-
Conventional computers are about 1 square foot for the desktop and another square foot for the monitor. One new proposal is for a memory bank containing more than a pound of DNA molecules suspended in about 1,000 quarts of fluid, in a bank about a yard square. Such a bank would be more capacious than all the memories of all the computers ever made.
The first ever-electronic computer (Eniac) took up a large room whereas the first DNA computer (Adleman) was 100 micro liters. Adleman dubbed his DNA computer the
TT-100, for test tube filled with 100 micro liters, or about one-fiftieth of a teaspoon of fluid, which is all it took for the reactions to occur.

2) Representation of Data:-
DNA computers use Base4 to represent data, whereas electronic computers use Base2 in the form of 1’s and 0’s. The nitrogen bases of DNA are part of the basic building blocks of life. Using this four letter alphabet, DNA stores information that is manipulated by living organisms in almost exactly the same way computers work their way through strings of 1’s and 0’s.

3) Parallelism:-
Electronic 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 [4], 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. The great Richard Feynman [5], in his Lectures on Computation, summed up von Neumann computers by saying, "the inside of a computer is as dumb as hell, but it goes like mad!" DNA computers, however, are non-von Neuman, stochastic machines that approach computation in a different way from ordinary computers for the purpose of solving a different class of problems. 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 1Mbit/sec; after 30 iterations it increases to 1000 Gbits/sec. This is beyond the sustained data rates of the fastest hard drives.

4) Material:-
Obviously, the material used in DNA Computers is different than in Conventional Electronic Computers. Generally, people take a variety of enzymes such as restriction nuclease and ligase as the hardware of DNA Computers, encoded double-stranded or single-stranded DNA molecules as software and data are stored in the sequences of base pairs. As for conventional electronic computers, electronic devices compose hardware. Software and data are stored in the organized structure of electronic devices represented by the electrical signals.
The other difference between DNA Computers and conventional electronic computers in material is the reusability. The materials used in DNA Computer are not reusable. Whereas an electronic computer can operate indefinitely with electricity as its only input, a DNA computer would require periodic refueling and cleaning. On the other side, until now, the molecular components used are still generally specialized. In the current research of DNA Computing, very different sets of oligonucleotides are used to solve different problems.

5) Methods of Calculation:- By synthesizing particular sequences of DNA, DNA computers carry out calculations. Conventional computers represent information physically expressed in terms of the flow of electrons through logical circuits. Builders of DNA computers represent information in terms of the chemical units of DNA. Calculating with an ordinary computer is done with a program that instructs electrons to travel on particular paths; with a DNA computer, calculation requires synthesizing particular sequences of DNA and letting them react in a test tube [3]. As it is, the basic manipulations used for DNA Computation include Anneal, Melt, Ligate, Polymerase Extension, Cut, Destroy, Merge, Separate by Length which can also be combined to high level manipulations such as Amplify, Separate by Subsequence, Append, Mark, Unmark. And the most famous example of a higher-level manipulation is the polymerase chain reaction (PCR).