Abstract
Bio-inspired computing is just one of the branches of natural computing which also encompasses different paradigms. This review provides a brief and general overview of natural computing and incorporates comparative study of computational techniques derived from different natural phenomena including molecular and quantum computing which uses a radically different type of hardware. The bio-inspired computation is supposed to be extracted from system biology, which provides the knowledge necessary for the development of synthetic biology tools. This review describes the intertwining between system and synthetic biology. Further, a brief overview of data mining and knowledge discovery process is incorporated including different data mining tasks as well as knowledge discovery processes. Moreover, attempts have been made to justify knowledge and computation as the dual aspects of nature. In addition, inter-linking and inter-dependency of different regulatory networks, e.g., gene regulatory network, protein–protein interaction networks, and transport networks is discussed and it is emphasized that entire genomic regulatory network can be inferred as a computational system mentioned as “genomic computer”. Differences between genomic computer and traditional electronic computer have been briefly discussed. Furthermore, it is reviewed that knowledge generation can be naturalized by adopting computational model of cognition and evolutionary approach. In this naturalized approach of knowledge generation, knowledge is observed as a transformation of input data by an interactive computational process going on in the cognizing agent during the interaction with environment. How fusion of knowledge generation and nature, i.e., naturalized knowledge generation can help towards the realization of computation beyond the Turing limit has been discussed. Finally, granular aspect of information processing in natural computing is also reviewed.
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Pandey, S.C., Nandi, G.C. Convergence of knowledge, nature and computations: a review. Soft Comput 20, 319–342 (2016). https://doi.org/10.1007/s00500-014-1510-7
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DOI: https://doi.org/10.1007/s00500-014-1510-7