Handbook of Intelligent Computing and Optimization for Sustainable Development. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Техническая литература
Год издания: 0
isbn: 9781119792628
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solution representing the corresponding kth weight molecular library tube, denoted by tk, so that the hybridization with the weight coefficient can occur. Prolongation reaction is performed with the solution of tube tk by using initial input sample as the primer sequence. Then, the solution is treated with excision enzyme to remove the single strands. The new tube is generated with the resultant strands.

       • Generation of DNA strands representing weight set: Again, is treated with restriction enzyme and gel electrophoresis is performed to remove shorter DNA strands. The filtered solution containing the longer strands is treated with ligase to perform coupled reaction. This reaction generates DNA strands representing the weight sum.(2.14)(2.15)

       • Gel Electrophoresis: Using the strand as probe, the output strands denoting “0” is extracted into the tube . Again, using the strand as probe, the output strands denoting “1” is extracted into the tube , and extract the DNA strands which output value is “1” into the tube . Gel electrophoresis is done with both of the tubes.

      For the tube image, DNA strands with length less than a specific threshold ∮ are retained and for the tube image, the DNA strands with length greater than ∮ are retained. From this step, a series DNA strands presenting w1, w2, .…, wp are generated.

       • Performing Intersection to generate w: If p is even number, then the set w1, w2, .…, wp is divided into p/2 groups, intersection of each group is solved. If p is odd, then the set divided into (p/2 + 1) groups, and again the interaction for each group is solved. The remainder tube with no match tube directly takes part in the next cyclic grouping, till the last cycle there is one tube remain. If any DNA strand exists, then the intersection of w1, w2, .…, wp can be deduced.

       • The sequence of the strand can be read by performing sequencing.

       • Classification of the unknown input vector: Using the probe 5′ − wij − 3′ the DNA strands are extracted from the weight. The extracted strands are put into a new tube and it is mixed with the solution representing the unknown input vector. The first, second, and third steps are again performed using the solution. Using the strand as probe, the output strands denoting “0” is extracted. Again, using the strand as probe, the output strands denoting “1” is extracted.

      Following these steps, the unknown input vector can be classified.

      So far, we have developed neural model using short DNA sequences and replaced the mathematical aspect of ANN by the elementary operations of the DNA chemistry. In next section, we illustrate the DNA logic gates which are the basic of Boolean algebra. It is essential for the hands-on development of DNA computer.

      The activity of the brain resembles the computer as it functions as an input-output device. The basic design of digital computer follows Boolean algebra. McCulloch and Pitts [1] presented their view on the possibility of the brain to use Boolean algebra. As the input and output of the neural model are binary numbers, thus the researcher do proposed that multilayer neural network can implement the basic logic gates, i.e., AND, OR, and NOT. This can be achieved by appropriately choosing the weights. Complex Boolean circuits can be constructed by designing properly connected architecture by neurons.

       • AND Gate: The truth table for AND gate is represented by Table 2.1.Table 2.1 Truth table for AND gate.Input (ix)Input (iy)Output (y)000100010111The function for implementation of AND gate is represented by Equation (2.16).(2.16)where b ≡ bias value and 1 < b < 2;g(.) ≡ step function;ix, iy ≡ input values and ix, iy ∈ {0, 1};y ≡ output value and y ∈ {0, 1}.

       • OR Gate: The truth table for OR gate is represented by Table 2.2.Table 2.2 Truth table for OR gate.Input (ix)Input (iy)Output (y)000011101111The function for implementation of OR gate is represented by Equation (2.17).(2.17)where b ≡ bias value and 0 < b < 1;g(.) ≡ step function;ix, iy ≡ input values and ix, iy ∈ {0, 1};y ≡ output value and y ∈ {0, 1}.

       • NOT Gate: The truth table for NOT gate is represented by Table 2.3.Table 2.3 Truth table for NOT gate.Input (ix)Output (y)0110The function for implementation of NOT gate is represented by Equation (2.18).(2.18)where b ≡ bias value and 0 < b < 1;g(.) ≡ step function;ix ≡ input value and ix ∈ {0, 1};y ≡ output value and y ∈ {0, 1}.

      In this section, we focus on the design strategy of logic gates which has been developed using the secondary structures of DNA molecules. These DNA logic gates are the pillars of logic circuits which are needed to design a competent DNA computer in near future.

      2.5.1 Logic Gates Using Deoxyribozymes

       2.5.1.1 Catalytic Activity of Deoxyribozyme

      The mechanism of cleaving nucleic acid strand is shown in Figure 2.12 which is used to perform simple computations and logic operations. In the figure, the substrate strand, i.e., the strand which has to be cleaved is tagged by fluorophore at one end and quencher molecule at the other end. Quenching process decreases the fluorescence