ABSTRACT
Artificial chemistries are candidates for methodologies that model and design biochemical systems. If artificial chemistries can deal with such systems in beneficial ways, they may facilitate activities in the new area of biomolecular engineering. In order to explore such possibilities, we illustrate four models of biochemical pathways described in our artificial chemistry based on string pattern matching and recombination. The modeled pathways are the replication of DNA, transcription from DNA to mRNA, translation from mRNA to protein, and the oxidation of fatty acids. The descriptions show that the present approach has good modularity and scalability that will be useful for modeling a huge network of pathways. Moreover, we give a procedure to perform reasoning in the artificial chemistry, which checks whether a specified collection of molecules can be generated in a given model, and we demonstrate that it works on a model that describes a natural biochemical pathway.
Subject(s)
Biochemical Phenomena , Computer Simulation , Models, Genetic , Algorithms , DNA Replication , Fatty Acids/metabolism , Models, Biological , Oxidation-Reduction , Protein Biosynthesis , Transcription, GeneticABSTRACT
Artificial chemistries are mainly used to construct virtual systems that are expected to show behavior similar to living systems. In this study, we explore possibilities of applying an artificial chemistry to modeling natural biochemical systems-or, to be specific, molecular computing systems-and show that it may be a useful modeling tool for molecular computation. We previously proposed an artificial chemistry based on string pattern matching and recombination. This article first demonstrates that this artificial chemistry is computationally universal if it has only rules that have one reactant or two reactants. We think this is a good property of an artificial chemistry that models molecular computing, because natural elementary chemical reactions, on which molecular computing is based, are mostly unimolecular or bimolecular. Then we give two illustrative example models for DNA computing in our artificial chemistry: one is for the type of computation called the Adleman-Lipton paradigm, and the other is for a DNA implementation of a finite automaton. Through the construction of these models we observe preferred properties of the artificial chemistry for modeling molecular computing, such as having no spatial structure and being flexible in choosing levels of abstraction.