Cytocomputation in a biologically inspired, dynamically reconfigurable hardware platform

Cytocomputation is a computational paradigm based upon the macromolecular activity inside the cytoplasm of the biological cells. This paradigm can be used either as a source of inspiration for proposing novel computational architectures, or as a framework for modeling biological processes at the intracellular and intercellular levels. This paper presents the main characteristics of the paradigm and describes its implementation on the ubichip, a hardware platform specifically designed to support bioinspired architectures.

[1]  Maria Castillo,et al.  AModified O(n) Leader Election Algorithm for Complete Networks , 2007, 15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing (PDP'07).

[2]  Dario Floreano,et al.  Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies , 2008 .

[3]  Michael Winikoff,et al.  Developing intelligent agent systems - a practical guide , 2004, Wiley series in agent technology.

[4]  Paul F. Cook,et al.  Enzyme Kinetics and Mechanism , 2007 .

[5]  Vikram Krishnamurthy,et al.  Biologically inspired rule-based multiset programming paradigm for soft-computing , 2004, CF '04.

[6]  Scott Hauck,et al.  Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation , 2007 .

[7]  J. Knight Gene regulation: Switched on to RNA , 2003, Nature.

[8]  Andres Upegui,et al.  Chapter 33 – Evolvable FPGAs , 2008 .

[9]  Ray Paton CytoComputational Systems — Perspectives and Tools of Thought , 2004 .

[10]  R. Pfeifer,et al.  Evolving Complete Agents using Artificial Ontogeny , 2003 .

[11]  Andres Upegui,et al.  Dynamic Routing on the Ubichip: Toward Synaptogenetic Neural Networks , 2008, 2008 NASA/ESA Conference on Adaptive Hardware and Systems.

[12]  Grace Jordison Molecular Biology of the Gene , 1965, The Yale Journal of Biology and Medicine.

[13]  Ajith Abraham,et al.  Decision Support Systems Using Ensemble Genetic Programming , 2006, J. Inf. Knowl. Manag..

[14]  Jaime-Alberto PARRA-PLAZA Enzyme Computation Computing the way proteins do , 2022 .

[15]  Colin G. Johnson Computation in Cells and Tissues : Perspectives and Tools of Thought , 2022 .

[16]  Daniel Le Métayer Higher-Order Multiset Programming , 1994, Specification of Parallel Algorithms.

[17]  Peter Van Roy,et al.  Concepts, Techniques, and Models of Computer Programming , 2004 .

[18]  Andres Upegui,et al.  The Perplexus bio-inspired reconfigurable circuit , 2007, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007).

[19]  Ray Paton Computation in cells and tissues : perspectives and tools of thought , 2004 .

[20]  Jeffrey W. Roberts,et al.  遺伝子の分子生物学 = Molecular biology of the gene , 1970 .

[21]  Timothy G. Mattson,et al.  Patterns for parallel programming , 2004 .

[22]  W. Cleland,et al.  pH variation of isotope effects in enzyme-catalyzed reactions. 2. Isotope-dependent step not pH dependent. Kinetic mechanism of alcohol dehydrogenase. , 1981, Biochemistry.

[23]  Mihai Oltean,et al.  Evolving digital circuits using multi expression programming , 2004, Proceedings. 2004 NASA/DoD Conference on Evolvable Hardware, 2004..

[24]  E. Davidson,et al.  Gene Regulatory Networks and the Evolution of Animal Body Plans , 2006, Science.

[25]  D. Rickwood,et al.  Cell and Molecular Biology , 1998, The Journal of Steroid Biochemistry and Molecular Biology.

[26]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[27]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[28]  Radu Dogaru Systematic Design for Emergence in Cellular Nonlinear Networks: With Applications in Natural Computing and Signal Processing , 2008, Studies in Computational Intelligence.

[29]  Andres Upegui,et al.  Neural Development on the Ubichip by Means of Dynamic Routing Mechanisms , 2008, ICES.

[30]  J. Mattick Challenging the dogma: the hidden layer of non-protein-coding RNAs in complex organisms. , 2003, BioEssays : news and reviews in molecular, cellular and developmental biology.

[31]  R. Duin,et al.  The dissimilarity representation for pattern recognition , a tutorial , 2009 .

[32]  Robert P. W. Duin,et al.  The Dissimilarity Representation for Pattern Recognition - Foundations and Applications , 2005, Series in Machine Perception and Artificial Intelligence.

[33]  Steve Grand,et al.  Creation: Life and How to Make It , 2001 .

[34]  H. Lodish Molecular Cell Biology , 1986 .