Mandrita Mondal

Similarity-based fuzzy reasoning by DNA computing

In this paper, we propose a wet lab algorithm for applicable form of fuzzy reasoning by DNA computing with an aim to add a new dimension to the existing similarity-based fuzzy reasoning method by bringing it down to nanoscale computing. We replace the logical aspect of fuzzy reasoning by DNA chemistry. To achieve this goal, we first fuzzify the synthetic DNA sequence which is called fuzzy DNA and which handles the vague concept of human reasoning. We adopt the basic notion of DNA computing based on standard DNA operations. In the present model, we consider double stranded DNA sequences with a specific aim of measuring similarity between two DNA sequences. The present wet lab procedure can exploit massive parallelism of DNA computing. The end result of the wet lab algorithm produces multivalued status which can be linguistically interpreted to match the perception of human expert.

CLASSIFICATION OF SODAR DATA BY DNA COMPUTING

In this paper, we propose a wet lab algorithm for classification of SODAR data by DNA computing. The concept of DNA computing is essentially exploited to generate the classifier algorithm in the wet lab. The classifier is based on a new concept of similarity-based fuzzy reasoning suitable for wet lab implementation. This new concept of similarity-based fuzzy reasoning is different from conventional approach to fuzzy reasoning based on similarity measure and also replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. Thus, we add a new dimension to the existing forms of fuzzy reasoning by bringing it down to nanoscale. We exploit the concept of massive parallelism of DNA computing by designing this new classifier in the wet lab. This newly designed classifier is very much generalized in nature and apart from SODAR data, this methodology can be applied to other types of data also. To achieve our goal we first fuzzify the given SODAR data in a form of synthetic DNA sequence which is called fuzzy DNA and which handles the vague concept of human reasoning. In the present approach, we can avoid the tedious choice of a suitable implication operator (for a particular operation) necessary for the classical approach to fuzzy reasoning based on fuzzy logic. We adopt the basic notion of DNA computing based on standard DNA operations. We consider double stranded DNA sequences, whereas, most of the existing models of DNA computation are based on single stranded DNA sequences. In the present model, we consider double stranded DNA sequences with a specific aim of measuring similarity between two DNA sequences. Such similarity measure is essential for designing the classifier in the wet lab. Note that, we have developed a completely new measure of similarity based on base pair difference which is absolutely different from the existing measure of similarity and which is very much suitable for expert system approach to classifier design, using DNA computing. In the present model of DNA computing, the end result of the wet lab algorithm produces multi valued status which can be linguistically interpreted to match the perception of an expert.

Fuzzy molecular automaton using splicing theory

In this paper, we have developed a Turing machine or a finite automaton, which scans the input data tape in form of DNA sequences and inspires the basic design of DNA computer. This model based on splicing system can solve fuzzy reasoning autonomously by using DNA sequences and human assisted protocols. Its hardware consists of class IIS restriction enzyme and T4 DNA ligase while the software consists of double stranded DNA sequences and transition molecules which are capable of encoding fuzzy rules. Upon mixing solutions containing these components, the automaton undergoes a cascade of cleaving and splicing cycles to produce the computational result in form of double stranded DNA sequence representing automatons final state. In this work, we have fused the idea of splicing system with the automata theory to develop fuzzy molecular automaton in which 1018 processors can work in parallel, requires a trillion times less space for information storage, 105 times faster than existing super computer and 1019 power operations can be performed using 1 Joule of energy.

Splicing operation and fuzzy molecular automaton

Purpose – The purpose of this study is to develop a Turing machine or a finite automaton, which scans the input data tape in the form of DNA sequences and inspires the basic design of a DNA computer.Design/methodology/approach – This model based on a splicing system can solve fuzzy reasoning autonomously by using DNA sequences and human assisted protocols. Its hardware consists of class IIS restriction enzyme and T4 DNA ligase while the software consists of double stranded DNA sequences and transition molecules which are capable of encoding fuzzy rules. Upon mixing solutions containing these components, the automaton undergoes a cascade of cleaving and splicing cycles to produce the computational result in form of double stranded DNA sequence representing automatons final state.Findings – In this work, the authors have fused the idea of a splicing system with the automata theory to develop fuzzy molecular automaton in which 1,018 processors can work in parallel, requiring a trillion times less space for ...

Logical Inference by DNA Strand Algebra

Based on the concept of DNA strand displacement and DNA strand algebra we have developed a method for logical inference which is not based on silicon-based computing. Essentially, it is a paradigm shift from silicon to carbon. In this paper, we have considered the inference mechanism, viz. modus ponens, to draw conclusion from any observed fact. Thus, the present approach to logical inference based on DNA strand algebra is basically an attempt to develop expert system design in the domain of DNA computing. We have illustrated our methodology with respect to the worked out example. Our methodology is very flexible for implementation of different expert system applications.

Syllogistic reasoning by strand algebra

In this paper, we introduce DNA strand algebra, which can be defined as a branch of process algebra, for modelling dynamic DNA devices called DNA tweezers whose operation is based on the mechanism of DNA strand displacement. The main components of DNA strand algebra are DNA strands, DNA gates, and their interactions. In this paper we demonstrate a DNA fuelled molecular machine for reasoning with dispositions which is basically a challenging problem to handle commonsense reasoning. Finally, we have designed a successful model based on the syntax and semantics of DNA strand algebra to perform syllogistic reasoning with DNA tweezers.

Reasoning with disposition using DNA tweezers

In this paper, we consider dynamic DNA devices called DNA tweezers whose operation is based on the mechanism of DNA strand displacement. We show how the systematic use of this simple but robust mechanism makes it possible to produce a DNA-fuelled molecular machine for reasoning with dispositions which are essential ingredients of commonsense of an individual. A biochemical reaction on DNA strands is used to activate DNA tweezers for reasoning with dispositions taken from the commonsense-based knowledge base. The dispositions are basically propositions that are preponderantly but not necessarily always true. The concept of dispositionality is closely related to the notion of usuality which provides a computational framework for commonsense reasoning. As human perception is usually represented in a vague qualitative fashion, we consider fuzzy set as one tool of engineering and try to mimic human intelligence of reasoning based on perception. Since childhood, an individual perceives the world around him/her and accordingly makes several commonsense-based judgements which are essentially reasoning with dispositions.