Bacteria employ restriction enzymes to cut or restrict DNA at or near specific words in a unique way. Many restriction enzymes cut the two strands of double-stranded DNA at different positions leaving overhangs of single-stranded DNA. Two pieces of DNA may be rejoined or ligated if their terminal overhangs are complementary. Using these operations fragments of DNA, or oligonucleotides, may be inserted and deleted from a circular piece of plasmid DNA. We propose an encoding for the transition table of a Turing machine in DNA oligonucleotides and a corresponding series of restrictions and ligations of those oligonucleotides that, when performed on circular DNA encoding an instantaneous description of a Turing machine, simulate the operation of the Turing machine encoded in those oligonucleotides. DNA based Turing machines have been proposed by Charles Bennett but they invoke imaginary enzymes to perform the state-symbol transitions. Our approach differs in that every operation can be pe...

We show how DNA molecules and stan- dard lab techniques may be used to create a nondeterministic Turing machine. This is the first scheme that shows how to make a universal computer with DNA. We claim that both our scheme and previous ones will work but they probably cannot be scaled up to be of practical computational importance. In vivo,

This article reviews the growing body of scientific work in Artificial Chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discussed along three application dimensions: modelling, information processing and optimization. Finally, common phenomena among the different systems are summarized. It is argued here that Artificial Chemistries are "the right stuff" for the study of pre-biotic and bio-chemical evolution, and they provide a productive framework for questions regarding the origin and evolution of organizations in general. Furthermore, Artificial Chemistries have a broad application range to practical problems as shown in this review.

Recently, biochemical systems have been shown to possess interesting computational properties. In a parallel development, the chemical computation metaphor is becoming more and more frequently used as part of the emergent computation paradigm in Computer Science. We review in this contribution the idea behind the chemical computational metaphor and outline its relevance for nanotechnology. We set up a simulated reaction system of mathematical objects and examine its dynamics by computer experiments. Typical problems of computer science, like sorting, parity checking or prime number computation are placed within this context. The implications of this approach for nanotechnology, parallel computers based on molecular devices and DNA-RNA-protein information processing are discussed. 1 Introduction The idea of using natural systems for computational purposes has long been pondered. Haken, for instance, has proposed to use laser mode competition as a way to recognize patterns [1]. Others h...

. We propose a mapping from digital logic circuits into genetic regulatory networks with the following property: the chemical activity of such a genetic network in vivo implements the computation specified by the corresponding digital circuit. Logic signals are represented by the synthesis rates of cytoplasmic DNA binding proteins. Gates consist of structural genes for output proteins, fused to promoter/operator regions that are regulated by input proteins. The modular approach for building gates allows a free choice of signal proteins and thus enables the construction of complex circuits. This paper presents simulation results that demonstrate the feasibility of this approach. Furthermore, a technique for measuring gate input/output characteristics is introduced. We will use this technique to evaluate gates constructed in our laboratory. Finally, this paper outlines automated logic design and presents BioSpice, a prototype system for the design and verification of genetic ...

This paper demonstrates a new method of programming artificial chemistries. It uses the emerging capabilities of the system's dynamics for information processing purposes. By evolution of metabolisms that act as control programs for a small robot one achieves the adaptation of the internal metabolic pathways as well as the selection of the most relevant available exteroreceptors. The underlying artificial chemistry evolves efficient information processing pathways with most benefit for the desired task, robot navigation. The results show certain relations to biological systems like motile bacteria.

In this paper we develop an algebra to describe organizations. Its application is demonstrated with five examples. We start from definitions given by Fontana (1992) of an organization as a closed and self-maintaining set of interacting objects. We develop a formal framework to describe the inner structure of an organization and a relationship between different organizations. The definitions of intersection and union of organizations are developed. Those definitions naturally give rise to a lattice (an algebraic structure over a partially ordered set) which provides a precise basis to study the hierarchical nature of organizations. Some fundamental properties are described and the usefulness of the mathematical concepts demonstrated by application.

Summary. Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and

Abstract: Almost all proteomic signaling networks in prokaryotes and eukaryotes are based on the simple phosphorylation/dephosporylation cycle; from this simple unit it is possible to construct a huge variety of control and computational circuits, both analog and digital. With the characterization of many signaling networks, researchers are turning to address the question of how a particular physiological response can be understood in terms of the proteins that make up the network; this is one of the central questions in “Systems Biology”. In this article I wish to summarize the great versatility of the basic protein cycle as a means to construct complex functional behaviors including the central role that feedback plays in determining the properties of protein based networks. 1.

All information processing systems found in living organisms are based on chemical processes. Harnessing the power of chemistry for computing might lead to a new unifying paradigm coping with the rapidly increasing complexity and autonomy of computational systems. Chemical computing refers to computing with real molecules as well as to programming electronic devices using principles taken from chemistry. The paper focuses on the latter, called artificial chemical computing, and discusses several aspects of how the metaphor of chemistry can be employed to build technical information processing systems. In these systems, computation emerges out of an interplay of many decentralized relatively simple components analogized to molecules. Chemical programming encompassed then the definition of molecules, reaction rules, and the topology and dynamics of the reaction space. Due to the selforganizing nature of chemical dynamics, new programming methods are required. Potential approaches for chemical programming are discussed and a road map for developing chemical computing into a unifying and well grounded approach is sketched.