Abstract
MCA (Membrane computing aggregation is experimental computational frame. It is inspired by the inner properties of membrane cells (Bio-inspired system). It is capable of problem solving activities by maintaining a special, “meaningful” relationship with the internal/external environment, integrating its self-reproduction processes within the information flow of incoming and outgoing signals. Because these problem solving capabilities, MCA admits a crucial evolutionary tuning by mutations and recombination of theoretical genetic “bridges” in a so called “aggregation” process ruled by a hierarchical factor that enclosed those capabilities. Throughout the epigenetic capabilities and the cytoskeleton and cell adhesion functionalities, MCA model gain a complex population dynamics specifics and high scalability. Along its developmental process, it can differentiate into meaningful computational tissues and organs that respond to the conditions of the environment and therefore “solve” the morphogenetic/configurational problem. MCA, above all, represents the potential for a new computational paradigm inspired in the higher level processes of membrane cells, endowed with quasi universal processing capabilities beyond the possibilities of cellular automata of and agent processing models.
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References
Adelman, L.M.: Molecular computation of solutions to combinatorial problems. Science 226, 1021–1024 (1994)
Amir-Kroll, H., Sadot, A., Cohen, I.R., Harel, D.: GemCell: a generic platform for modelling multi-cellular. Theor. Comput. Sci. 391, 276–290 (2008)
Arteta, A., Mingo, L.F., Castellanos, J.: An isomorphism based algorithm to solve complex problems. WSEAS Trans. Inf. Sci. Appl. 15, 27–36 (2018)
Arteta, A., Mingo, L.F., Gomez, N.: New approach to optimize membrane systems. J. Bioinform. 1(1), 1–6 (2014)
Arteta, A., Mingo, L.F., Gomez, N.: Membrane systems working with the P-factor: best strategy to solve complex problems. Adv. Sci. Lett. 19(5), 1490–1495 (2012)
Arteta, A.: MEIA systems: membrane encrypted information application systems. Nat. Inf. Technol. Madr. Int. J. Inf. Theor. Appl. 19(2), 103–109 (2012)
Arteta, A., Gomez, N., Gonzalo, R.: Solving diophantine equations with a parallel membrane computing model. Int. J. Inf. Models Anal. 1, 220–225 (2012)
Arteta, A., Mingo, L.F., Gomez, N.: Solving complex problems with a bio-inspired model. Eng. Appl. Artif. Intell. 24(6), 919–927 (2011)
Arteta, A., Fernández, L., Arroyo, F.: P-systems: study of randomness when applying evolution rules. In: International Book Series “Information Science and Computing, pp. 15–24 (2009)
Arteta, A., Goñi, A., Castellanos, J.: Analysis of P-systems under multiagents perspective. In: International Book Series “Information Science and Computing”, pp. 117–128 (2009)
Angeleska, A., et al.: RNA-guided DNA assembly. J. Theor. Biol. 248, 706–720 (2007)
Ardelean, I., et al.: A computational model for cell differentiation. BioSystems 76(1–3), 169–176 (2004)
Balazsi, G., Barabasi, A.-L., Oltvai, Z.N.: Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. PNAS 102(22), 7841–7846 (2005)
Bashor, C.J., Horwitz, A.A., Peisajovich, S.J., Lim, W.A.: Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems. Annu. Rev. Biophys. 39, 515–537 (2010)
Blow, N.: Systems biology: untangling the protein web. Nature 460, 415–418 (2009)
Bray, D.: Wetware: A Computer in Every Living Cell: The Computer in Every Living Cell. Yale University Press, New Haven (2009)
Brooks, R.A.: The relationship between matter and life. Nature 409, 409–411 (2001)
Cao, H., Romero-Campero, F.J., Heeb, S., Camara, M., Krasnogor, N.: Evolving cell models for systems and synthetic biology. Syst. Synth. Biol. 4(1), 55–84 (2010)
Carroll, S.B.: Endless Forms Most Beautiful. W.W. Norton, Chicago (2005)
Danchin, A.: Bacteria as computers making computers. FEMS Microbiol. Rev. 33(1), 3–26 (2009)
Dasgupta, D. (ed.): Artificial Immune Systems and Their Applications. Springer, Heidelberg (1998). https://doi.org/10.1007/978-3-642-59901-9
Dassow, J., Mitrana, V.: On some operations suggested by genome evolution. In: Proceedings of the Second Pacific Symposium on Biocomputing, pp. 97–108 (1997)
Dassow, J., Mitrana, V., Salomaa, A.: Context-free evolutionary grammars and the structural language of nucleic acids. BioSystems 43, 169–177 (1997)
Dassow, J., Mitrana, V., Salomaa, A.: Operations and language generating devices suggested by the genome evolution. Theor. Comput. Sci. 270(1–2), 701–731 (2002)
Edelman, G.M.: Topobiology: An introduction to molecular embriology. Basic Books, New York (1988)
Ehrenfeucht, A., Harju, T., Petre, I., Prescott, D.M., Rozenberg, G.: Computation in Living Cells: Gene Assembly in Ciliate. Natural Computing Series. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-662-06371-2
Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. Wiley, Chichester (2005)
Federici, D., Downing, K.: Evolution and development of a multicellular organism: scalability, resilience, and neutral complexification. Artif. Life 12(3), 381–409 (2006)
Freund, R., Martin-Vide, C., Mitrana, V.: On some operations suggested by gene assembly in ciliates. New Gener. Comput. 20, 279–293 (2002)
de Frutos, J.A., Fernández, L., Luengo, C., Arteta, A.: Improving active rules performance in new P system communication architectures. Inf. Technol. Knowl. 4(1), 3–18 (2010)
Arteta, A., Castellanos, A., Martinez, A.: Membrane computing: non deterministic technique to calculate extinguished multisets of objects. Int. J. Inf. Technol. Knowl. 4(1), 30–41 (2010)
de Frutos, J.A., Arroyo, F., Arteta, A.: Usefulness states in new P system communication architectures. In: Corne, D.W., Frisco, P., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2008. LNCS, vol. 5391, pp. 169–186. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-95885-7_13
Han, J., et al.: Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430, 88–93 (2004)
Haynes, K.A., Silver, P.A.: Eukaryotic systems broaden the scope of synthetic biology. JCB 187(5), 589–596 (2009)
Ho, L., Crabtree, G.R.: Chromatin remodelling during development. Nature 463, 474–484 (2010)
Holcombe, M., Bell, A.: Computational models of immunological pathways. In: Holcombe, M., Paton, R. (eds.) Information Processing in Cells and Tissues. Plenum Press, New York (1998)
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Arteta, A., Mingo, L.F., Gomez, N., Zhao, Y. (2019). Membrane Computing Aggregation (MCA): An Upgraded Framework for Transition P-Systems. In: Compagnoni, A., Casey, W., Cai, Y., Mishra, B. (eds) Bio-inspired Information and Communication Technologies. BICT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-030-24202-2_15
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