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* Our Design: DNA Killer Automata

DNA Killer Automaton (DKA) The Intelligent Nanomedicine Shaoshan Liu and Jean-Luc Gaudiot Electrical Engineering & Computer Science Dept., The Henry Samueli School of Engineering University of California, Irvine. DKA Algorithm. Object Oriented Simulation. Level 1. Experiment. Cell. DKA.

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* Our Design: DNA Killer Automata

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  1. DNA Killer Automaton (DKA)The Intelligent Nanomedicine Shaoshan Liu and Jean-Luc GaudiotElectrical Engineering & Computer Science Dept., The Henry Samueli School of EngineeringUniversity of California, Irvine DKA Algorithm Object Oriented Simulation Level 1 Experiment Cell DKA Level 2 GJIC mRNA Enzyme Cytotoxin Level 3 * Conclusion Our software simulation model suggests that the bystander effect facilitates the propagation of DKA. Also, our simulation results suggest imply that the efficacy of DKA is linearly dependent on its dose and the degree of connectivity between cancer cells. E = const. * D * N Goal Using nanotechnology to build an automaton that cures cancer at the molecular level. Existing Cancer Therapies * Our Design: DNA Killer Automata • Expensive • Inefficient • Inaccurate • Serious Side Effects 1. DKA enter cells through injection 2. DKA detect the presence of cancer in the cell 3. DKA releases GCVTPs if all cancer indicators are detected 4. GCVTPs propagate to nearby cancer cells through homologous GJIC 5. After propagation, GCVTPs have entered over 90% of all cancer cells * Simulation: a discrete cellular automaton model 3-Stage Simulation • Distribution of DKA • Cancer detection • Cytotoxin propagation DKA efficacy Vs. number of cells hit Efficacy of cancer cell killing DKA efficacy Vs. DKA dose

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