
Bio Computing 
Alternative Computational Models: A Comparison of Biomolecular and Quantum Computation (1998) 



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Friday, 21 April 2006 
Molecular Computation (MC) is massively parallel computation where data is stored and processed within objects of molecular size. Biomolecular Computation (BMC) is MC using biotechnology techniques, e.g. recombinant DNA operations. In contrast, Quantum Computation (QC) is a type of computation where unitary and measurement operations are executed on linear superpositions of basis states. Both BMC and QC may be executed at the micromolecular scale by a variety of methodologies and technologies.... 

Area Exam: The Limitations of DNA Computing  Schwartz (1998) 



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Friday, 21 April 2006 
This report addresses the theoretical and practical limitations of DNA computing. The discussion focuses on the work of Adleman [1], Winfree [42], and Hagiya et al. [21]. However, we also cover extensions of their techniques by other authors where it is necessary to evaluate the potential of the assigned papers. The possibility of DNA computing has generated considerable interest in recent years. Most recent work on DNA computing was inspired by Adleman [1], who first demonstrated a working... 

Computing with Molecules  Rooss 



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Friday, 21 April 2006 
In 1994 Adleman published the description of a lab experiment, where he computed an instance of the Hamiltonian path problem with DNA in test tubes. He initiated a flood of further research on computing with molecular means in theoretical computer science. A great number of models have been introduced, and their computational power has been examined, with results on universality, complexity, efficient algorithms, and error resistance. The main results are presented in this survey. 

On Constructing A Molecular Computer  Adleman (1995) 



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Friday, 21 April 2006 
It has recently been suggested that under some circumstances computers based on molecular interactions may be a viable alternative to computers based on electronics. Here, some practical aspects of constructing a molecular computer are considered. 

Using DNA to Solve NPComplete Problems  Lipton (1995) 



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Friday, 21 April 2006 
We show how to use DNA experiments to solve the famous "SAT" problem of Computer Science. This is a special case of a more general method that can solve NPcomplete problems, first introduced in [3]. The advantage of these results is the huge parallelism inherent in DNA based computing. It has the potential to yield vast speedups over conventional electronic based computers for such search problems. 1. Introduction In a recent breakthrough Adleman [1] showed how to use biological experiments to solve instances of the famous Hamiltonian Path Problem (HPP). Recall that this problem is: Given a set of "cities" and directed paths between them; Find a directed tour that starts at a given city, ends at a given city, and visits every other city exactly once.
This problem (HPP) is known to be NPcomplete [2]. A computational problem is in NP provided it can be formulated as a "search" problem. Further, a problem is NPcomplete provided, if it has an efficient solution, then so does all of ... 

On The Computational Power of DNA  Boneh, Dunworth, Lipton, Sgall (1995) 



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Friday, 21 April 2006 
We show how DNA based computers can be used to solve the satisfiability problem for boolean circuits. Furthermore, we show how DNA computers can solve optimization problems directly without first solving several decision problems. Our methods also enable random sampling of satisfying assignments. Finally we suggest a procedure for evaluating functions in the polynomial hierarchy. 1 Introduction In the very short history of DNA (deoxyribonucleic acid) based computing there have already been a number of exciting results. It all started with Adleman's [1] beautiful insight that showed that biological experiments could solve the Directed Hamiltonian Path problem (DHP). Then, Lipton [8] showed how to use DNA to solve more general problems, namely to find satisfying assignments for arbitrary (directed) contact networks, which includes the important case of arbitrary formulas. Since then there has been a series of papers on DNA computation. Each of these subsequent results is of the followin... 

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Friday, 21 April 2006 
The debate revolves around two issues: Firstly, is a nucleic acid (DNA or RNA) a part of the agent which determines what the agent does. If there is one, why can we not find it? If there is no nucleic acid, how are agent properties specified? Secondly, PrP (sometimes called prion protein) , is associated with the agent somehow  but what does it do? This debate matters because no life form, including any virus, has been found which does not have nucleic acid as the molecule which encodes the chemical information for its existence. 

Evidence Sets and Contextual Genetic Algorithms  L.M. Rocha 



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Friday, 21 April 2006 
This dissertation proposes a systemstheoretic framework to model biological and cognitive systems which requires both selforganizing and symbolic dimensions. The framework is based on an inclusive interpretation of semiotics as a conceptual theory used for the simulation of complex systems capable of representing, as well as evolving in their environments, with implications for Artificial Intelligence and Artificial Life. This evolving semiotics is referred to as Selected SelfOrganization when applied to biological systems, and Evolutionary Constructivism when applied to cognitive systems. Several formal avenues are pursued to define tools necessary to build models under this framework. 

Genetic Algorithms and Protein Folding  Steffen SchulzeKremer 



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Friday, 21 April 2006 
Evolutionary Computation is, like neural networks, an example par excellence for an information processing paradigm that was originally developed and exhibited by nature and later discovered by man who subsequently transformed the general principle into computational algorithms to be put to work on computers. Nature makes in an impressive way use of the principle of genetic heritage and evolution. Application of the simple concept of performance based reproduction of individuals („survival of the fittest“) led to the rise of well adapted organisms that can endure in a potentially adverse environment. Mutually beneficial interdependencies, cooperation and even apparently altruistic behaviour can emerge solely by evolution. The investigation of those phenomena is part of research in artificial life but cannot be dealt with in this book. 



