Computing with Proteins (original) (raw)
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Towards computing with proteins
Proteins: Structure, Function, and Bioinformatics, 2006
Can proteins be used as computational devices to address difficult computational problems? In recent years there has been much interest in biological computing, that is, building a general purpose computer from biological molecules. Most of the current efforts are based on DNA because of its ability to self-hybridize. The exquisite selectivity and specificity of complex protein-based networks motivated us to suggest that similar principles can be used to devise biological systems that will be able to directly implement any logical circuit as a parallel asynchronous computation. Such devices, powered by ATP molecules, would be able to perform, for medical applications, digital computation with natural interface to biological input conditions. We discuss how to design protein molecules that would serve as the basic computational element by functioning as a NAND logical gate, utilizing DNA tags for recognition, and phosphorylation and exonuclease reactions for information processing. A solution of these elements could carry out effective computation. Finally, the model and its robustness to errors were tested in a computer simulation.
Breaking the Box: Simulated Protein Computing
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2012
Computers since the 1940s have shared the same basic architecture described by Turing and von Neumann, in which one central processor has access to one contiguous block of main memory. This architecture is challenged by modern applications that require greater parallelism, distribution, coordination, and complexity. Here we show that a model of protein interactions can serve as a new architecture, performing useful calculations in a way that provides for much greater scalability, flexibility, adaptation, and power than does the traditional von Neumann architecture. We found that even this simple simulation of protein interactions is universal, being able to replicate the calculation performed on a digital computer, yet without relying upon a central processor or main memory. We anticipate that the convergence of information-and life-sciences is poised to deliver a platform that invigorates computing as it provides insight into the complexity of living systems.
Biomolecular computing—a shape of computation to come
SIGACT News, 1997
1 Introduction Biomolecular computing is the computing methodology in which biologically important molecules are used as memory. The'aperiodic'nature of these polymeric molecules makes them suitable as memory units [GRY56, WHR+ 87]. Instead of monotonous repeating units of most synthetic polymers (such as polyethylene), certain biological polymers (such as DN A, RN A and protein) ha ve sets of repeating (or information encoding) units, and these units can appear in any order. The use of atomic or molecular- ...
Connecting Transistors and Proteins
Proceedings of the 9th International Conference on Artificial Life (AlifeIX), 2004
We connect transistors and proteins in two ways. The first is by showing that they have much in common as fundamental devices of electronics and life. The second is by describing how an evolvable wiring of electronic devices can parallel the wiring of proteins into genetic regulatory networks. We then transform this connection into a methodology for the study of the evolutionary properties of circuits. The approach is based on the use of analog electronic circuit simulators. We present an example of implementation with the first results obtained
Biomolecular computing: Is it ready to take off ?
Biomolecular computing is an emerging field at the interface of computer science, biological science and engineering. It uses DNA and other biological materials as the building blocks for construction of living computational machines to solve difficult combinatorial problems. In this article, notable advances in the biomolecular computing are reviewed and challenges associated with this multidisciplinary research are addressed. Finally, several perspectives are given based on the review of biomolecular computing.
Molecular Computing with Artificial Neurons
2000
Abstract—Today’s computers are built up from a minimal set of standard pattern recognition opera- tions. Logic gates, such as NAND, are common ex- amples. Biomolecular materials offer an alternative approach, both in terms of variety and context sensi- tivity. Enzymes, the basic switching elements in bio- logical cells, are notable for their ability to discrimi- nate specific molecules in a
Biosystems, 2004
Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.
Information processing in biological molecular machines
ABSTRACTBiological molecular machines are enzymes that simultaneously catalyze two processes, one donating free energy and second accepting it. Recent studies show that most native protein enzymes have a rich stochastic dynamics of conformational transitions which often manifests in fluctuating rates of the catalyzed processes and the presence of short-term memory resulting from the preference of certain conformations. For arbitrarily complex stochastic dynamics of protein machines, we proved the generalized fluctuation theorem predicting the possibility of reducing free energy dissipation at the expense of creating some information stored in memory. That this may be the case has been shown by interpreting results of computer simulations for a complex model network of stochastic transitions. The subject of the analysis was the time course of the catalyzed processes expressed by sequences of jumps at random moments of time. Since similar signals can be registered in the observation o...