CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.02 - March/April
Issue No.02 - March/April (2012 vol.9)
I. G. Karafyllidis , Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.104
Bacteria evolved cell to cell communication processes to gain information about their environment and regulate gene expression. Quorum sensing is such a process in which signaling molecules, called autoinducers, are produced, secreted and detected. In several cases bacteria use more than one autoinducers and integrate the information conveyed by them. It has not yet been explained adequately why bacteria evolved such signal integration circuits and what can learn about their environments using more than one autoinducers since all signaling pathways merge in one. Here quantum information theory, which includes classical information theory as a special case, is used to construct a quantum gate circuit that reproduces recent experimental results. Although the conditions in which biosystems exist do not allow for the appearance of quantum mechanical phenomena, the powerful computation tools of quantum information processing can be carefully used to cope with signal and information processing by these complex systems. A simulation algorithm based on this model has been developed and numerical experiments that analyze the dynamical operation of the quorum sensing circuit were performed for various cases of autoinducer variations, which revealed that these variations contain significant information about the environment in which bacteria exist.
Sensors, Microorganisms, Integrated circuit modeling, Computational modeling, Information processing, Numerical models, Biological system modeling,simulation., Quorum sensing, systems biology, quantum information processing, quantum gates, modeling
I. G. Karafyllidis, "Quantum Gate Circuit Model of Signal Integration in Bacterial Quorum Sensing", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 2, pp. 571-579, March/April 2012, doi:10.1109/TCBB.2011.104