Laura F. Landweber – författare

The fledgling field of DNA computers began in 1994 when Leonard Adleman surprised the scientific community by using DNA molecules, protein enzymes, and chemicals to solve an instance of a hard computational problem. This volume presents results from the second annual meeting on DNA computers held at Princeton only one and one-half years after Adleman's discovery. By drawing on the analogy between DNA computing and cutting-edge fields of biology (such as directed evolution), this volume highlights some of the exciting progress in the field and builds a strong foundation for the theory of molecular computation. DNA computing is a radically different approach to computing that brings together computer science and molecular biology in a way that is wholly distinct from other disciplines. This book outlines important advances in the field and offers comprehensive discussion on potential pitfalls and the general practicality of building DNA based computers.

The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. Genetic algorithms meanwhile grew out of the realization that a computer program could use the biologically-inspired processes of mutation, recombination, and selection to solve hard optimization problems. Genetic and evolutionary programming provide further approaches to a wide variety of computational problems. A synthesis of these experiences reveals fundamental insights into both the computational nature of biological evolution and processes of importance to computer science. Topics include biological models of nucleic acid information processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the origin and evolution of the genetic code; and the interface with genetic algorithms and genetic and evolutionary programming.

The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. Genetic algorithms meanwhile grew out of the realization that a computer program could use the biologically-inspired processes of mutation, recombination, and selection to solve hard optimization problems. Genetic and evolutionary programming provide further approaches to a wide variety of computational problems. A synthesis of these experiences reveals fundamental insights into both the computational nature of biological evolution and processes of importance to computer science. Topics include biological models of nucleic acid information processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the origin and evolution of the genetic code; and the interface with genetic algorithms and genetic and evolutionary programming.