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Genetic Networks Described in Stochastic Pi Machine (SPiM) Programming Language: Compositional Design | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Genetic Networks Described in Stochastic Pi Machine (SPiM) Programming Language: Compositional Design

Andrew Kuznetsov*

Freiburg University, Baden-Wuerttemberg, Germany

*Corresponding Author:
Dr. Andrew Kuznetsov
Freiburg University, Germany

Received date: September 29, 2009; Accepted date: October 29, 2009; Published date: October 29, 2009

Citation: Kuznetsov A (2009) VMD: Genetic Networks Described in Stochastic Pi Machine (SPiM) Programming Language: Compositional Design. J Comput Sci Syst Biol 2:272-282. doi:10.4172/jcsb.1000042

Copyright: © 2009 Kuznetsov A. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

If biological objects are created by natural selection, why are they composed of discrete modules? What has been the nature of mutations since the Darwinian epoch? This paper presents examples of genetic circuits in terms of stochastic -calculus; a new mathematical language for nanosystems. The author used a constructor of five elements such as decay, null gate, gene product, and negative and positive gates. These primitives were applied to design genetic switches, oscillators, feedforward and feedback loops, pulse generators, memory elements, and combinatorial logics. The behaviors of those circuits were investigated – functions, such as oscillations or a spontaneous pulse generation were performed simply, flip-flops between stable states occurred in the noisy environment. The modular essence of -calculus and the following up features of Stochastic Pi Machine (SPiM) programming language allowed us to change the topology of networks that resembled a gene exchange in nature. Other types of mutations were considered as variations in parameters. Perturbations modified system behavior in unpredictable ways that generated diversity for a possible future design by selection of appropriative variants.

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