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We improve and extend a recent result showing that spiking neural P systems with the same rules in all neurons of the system (homogenous) and working in the max sequential manner are universal. The previous work in this area reported by the group led by Dr. Linqiang Pan did not put any bound on the number of neurons used. We believe this is an important question for any future practical implementation of such systems that deserves investigation, and we provide some results in this direction. Extending the aforementioned construction with the work of Korec on small register machines one could estimate the size of the previous construction at 105 neurons. We are able to improve this result and to show that an SNP system with 83 neurons having homogenous rules and working in the max sequential manner is universal. Several related results with respect to max-pseudo sequentiality mode are also obtained: 83 neurons are necessary for this case, too. When considering the case of systems without weighted synapses, we show that one needs at most 244 homogenous neurons for reaching universality in the max-pseudo sequentiality case.

The paper presents a DNA-based computing model which is enzyme-free and autonomous, not requiring a human intervention during the computation. The model is able to perform iterated resolution steps with logical formulae in conjunctive normal form. The implementation is based on the technique of DNA strand displacement, with each clause encoded in a separate DNA molecule. Propositions are encoded assigning a strand to each proposition p, and its complementary strand to the proposition ¬p; clauses are encoded comprising different propositions in the same strand. The model allows to run logic programs composed of Horn clauses by cascading resolution steps. The potential of the model is demonstrated also by its theoretical capability of solving SAT. The resulting SAT algorithm has a linear time complexity in the number of resolution steps, whereas its spatial complexity is exponential in the number of variables of the formula.

The paper aims at demonstrating and confirming that breadth first search or pruning techniques can substantially improve the effectiveness of biomolecular algorithms. A breadth first search-based DNA algorithm solving the maximum clique problem for a graph is presented, and its complexity and scalability parameters are studied. The analysis shows that parameters like the number of steps, the length and volume of DNA strands, the number of enzymes and the concentration of the molecules encoding solutions are dramatically improved in comparison with previous approaches to the same problem and, theoretically, they would allow to process graphs with thousands of vertices. These parameters are also compared with several related results focusing on the scalability of DNA computing methods. Finally, an analysis of error-resistance of the algorithm is given.

We study the computational power of pure insertion grammars. We show that pure insertion grammars of weight 3 can characterize all recursively enumerable languages. This is achieved by either applying an inverse morphism and a weak coding, or a left (right) quotient with a regular language. We also study an application in DNA computing and improve some known results concerning the power of insertion–deletion DNA systems.

We present a P system with replicated rewriting to solve the Maximum Clique Problem for a graph. Strings representing cliques are built gradually. This involves the use of inhibitors that control the space of all generated solutions to the problem. Calculating the maximum clique for a graph is a highly relevant issue not only on purely computational grounds, but also because of its relationship to fundamental problems in genomics. We propose to implement the designed P system by means of a DNA algorithm. This algorithm is then compared with two standard papers that addressed the same problem and its DNA implementation in the past. This comparison is carried out on the basis of a series of computational and physical parameters. Our solution features a significantly lower cost in terms of time, the number and size of strands, as well as the simplicity of the biological implementation.

We study binary word operations of the insertion, deletion and substitution type. Many of these operations can be generalized into a unified framework by introducing so-called trajectory condition. This generalization has been previously made for insertion and deletion operations. In this paper we naturally extend this approach also to substitution operations. We study closure properties and decision problems of substitutions on trajectories. The obtained results are then applied to model complex noisy channels and a cryptanalysis problem. Another application concerns the design of sets of DNA strands without undesired bonds.

The input data for DNA computing must be encoded into the form of single or double DNA strands. As complementary parts of single strands can bind together forming a double-stranded DNA sequence, one has to impose restrictions on these sets of DNA words (languages) to prevent them from interacting in undesirable ways. We recall a list of known properties of DNA languages which are free of certain types of undesirable bonds. Then we introduce a general framework in which we can characterize each of these properties by a solution of a uniform formal language inequation. This characterization allows us among others to construct (i) a uniform algorithm deciding in polynomial time whether a given DNA language possesses any of the studied properties, and (ii) in many cases also an algorithm deciding whether a given DNA language is maximal with respect to the desired property.

We present a DNA-based implementation of reaction system with molecules encoding elements of the propositional logic, that is, propositions and formulas. The protocol can perform inference steps using, for example,modus ponensandmodus tollensrules and de Morgan’s laws. The set of the implemented operations allows for inference of formulas using the laws of natural deduction. The system can also detect whether a certain propositionacan be deduced from the basic facts and given rules. The whole protocol is fully autonomous; that is, after introducing the initial set of molecules, no human assistance is needed. Only one restriction enzyme is used throughout the inference process. Unlike some other similar implementations, our improved design allows representing simultaneously a factaand its negation ~a, including special reactions to detect the inconsistency, that is, a simultaneous occurrence of a fact and its negation. An analysis of correctness, completeness, and complexity is included.

This chapter looks at the question of managing errors that arise in DNA-based computation. Due to the inaccuracy of biochemical reactions, the experimental implementation of a DNA computation may lead to incorrectly calculated results. This chapter explores different methods that can assist in the reduction of such occurrences. The solutions to the problem of erroneous biocomputations are presented from the perspective of computer science techniques. Three main aspects of dealing with errors are covered: software simulations, algorithmic approaches, and theoretical methods. The objective of this survey is to explain how these tools can reduce errors associated with DNA computing.

An incremental approach to construction of biomolecular algorithms solving intractable problems is presented. The core idea is to build gradually the space of candidate solutions and remove invalid solutions as soon as possible. We demonstrate two examples of this strategy: a P system with replication and inhibitors for solving the Maximum Clique Problem for a graph, and an incremental DNA algorithm for the same problem inspired by the membrane solution. The DNA implementation is based on the parallel filtering DNA model featuring error-resistance of the employed operations. The algorithm is compared with two standard papers that addressed the same problem and its DNA implementation in the past. The comparison is carried out on the basis of a series of computational and physical parameters. The incremental algorithm features a dramatically lower cost in terms of time, the number and size of DNA strands, together with a high error-resistance. A probabilistic analysis shows that physical parameters (volume of the DNA pool, concentration of the solution-encoding strands) and error-resistance of the algorithm should allow to process in vitro instances of graphs with hundreds to thousands of vertices.

A P system is a natural computing model inspired by information processes in cells and a control role of cellular membranes. We show that uniform families of P systems with active membranes are able to solve, in polynomial time, exactly the class of decisional problems PSPACE. Similar results were achieved also with other models of bio-inspired computers, such as DNA computing. Together they suggest that PSPACE naturally characterizes the computational potential of biological information processing.

The input data for DNA computing must be encoded into the form of single or double DNA strands. As complementary parts of single strands can bind together forming a double-stranded DNA sequence, one has to impose restrictions on these sets of DNA words (=languages) to prevent them from interacting in undesirable ways. We recall a list of known properties of DNA languages which are free of certain types of undesirable bonds. Then we introduce a general framework in which we can characterize each of these properties by a solution of a uniform formal language inequation. This characterization allows us among others to construct (i) a uniform algorithm deciding in polynomial time whether a given DNA language possesses any of the studied properties, and (ii) in many cases also an algorithm deciding whether a given DNA language is maximal with respect to the desired property.

The paper examines the concept of hairpin-free words motivated from the biocomputing and bioinformatics fields. Hairpin (-free) DNA structures have numerous applications to DNA computing and molecular genetics in general. A word is called hairpin-free if it cannot be written in the form xvyθ (v)z, with certain additional conditions, for an involution θ (a function θ with the property that θ2 equals the identity function). We consider three involutions relevant to DNA computing: a) the mirror image function, b) the DNA complementarity function over the DNA alphabet {A,C,G,T} which associates A with T and C with G, and c) the Watson-Crick involution which is the composition of the previous two. We study elementary properties and finiteness of hairpin (-free) languages w.r.t. the involutions a) and c). Maximal length of hairpin-free words is also examined. Finally, descriptional complexity of maximal hairpin-free languages is determined.

The word substitutions are binary word operations which can be basically interpreted as a deletion followed by insertion, with some restrictions applied. Besides being itself an interesting topic in formal language theory, they have been naturally applied to modelling noisy channels. We introduce the concept of substitution on trajectories which generalizes a class of substitution operations. Within this framework, we study their closure properties and decision questions related to language equations. We also discuss applications of substitution on trajectories in modelling complex channels and a cryptanalysis problem.

D0L systems consist of iterated letter-to-string morphism over a finite alphabet. Their descriptional power is rather limited and their length sequences can be expressed as sum of products of polynomial and exponential functions. There were several attempts to enrich their structure by various types of regulation, see e.g. [1], leading to more powerful mechanisms. Due to increasing interest in biocomputing models, V. Mihalache and A. Salomaa suggested in 1997 Watson-Crick D0L systems with so called Watson-Crick morphism. This letter-to-letter morphism maps a letter to the complementary letter, similarly as a nucleotide is joined with the complementary one during transfer of genetic information. Moreover, this new morphism is triggered by a simple condition (called trigger), e.g. with majority of pyrimidines over purines in a string. This paper deals with the expressive power of standard Watson-Crick D0L systems. A rather unexpected result is obtained: any Turing computable function can be computed by a Watson-Crick D0L system.

The properties of Watson–Crick D0L system, a language–theoretical formalism inspired by natural DNA processing, are studied. The model incorporates the iterated D0L-like morphism and the DNA complementarity principle represented by a letter-to-letter morphism. These two morphisms are connected by a natural condition called the trigger.We show first that this very simple model has rather unexpected power; it can closely and simply simulate any Minsky register machine. As a consequence, any recursively enumerable language can be obtained as a projection of the language of some standard Watson–Crick D0L system. Finally, we show that the graph reachability problem, equivalence problems and some other problems of standard Watson–Crick D0L systems are undecidable.

Watson-Crick D0L systems, introduced in 1997 by Mihalache and Salomaa, arise from two major principles: the Lindenmayer rewriting and the Watson-Crick complementarity principle. Complementarity can be viewed as a purely language-theoretic operation. Majority of a certain type of symbols in a string (purines vs. pyrimidines) triggers a transition to the complementary string. The paper deals with an expressive power of deterministic interactionless Watson-Crick Lindenmayer systems. A rather surprising result is obtained: these systems, consisting of iterated morphism and a basic DNA operation, are able to compute any Turing computable function.