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Journal Article

Solving Exact Cover Instances with Molecular-Motor-Powered Network-Based Biocomputation.

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Surendiran,  Pradheebha
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Meinecke,  Christoph Robert
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Zhu,  Jingyuan
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Diez,  Stefan
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Reuter,  Danny
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Kugler,  Hillel
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Korten,  Till
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Citation

Surendiran, P., Meinecke, C. R., Salhotra, A., Heldt, G., Zhu, J., Månsson, A., et al. (2022). Solving Exact Cover Instances with Molecular-Motor-Powered Network-Based Biocomputation. ACS nanoscience Au, 2(5), 396-403. doi:10.1021/acsnanoscienceau.2c00013.


Cite as: https://hdl.handle.net/21.11116/0000-000E-AA44-6
Abstract
Information processing by traditional, serial electronic processors consumes an ever-increasing part of the global electricity supply. An alternative, highly energy efficient, parallel computing paradigm is network-based biocomputation (NBC). In NBC a given combinatorial problem is encoded into a nanofabricated, modular network. Parallel exploration of the network by a very large number of independent molecular-motor-propelled protein filaments solves the encoded problem. Here we demonstrate a significant scale-up of this technology by solving four instances of Exact Cover, a nondeterministic polynomial time (NP) complete problem with applications in resource scheduling. The difficulty of the largest instances solved here is 128 times greater in comparison to the current state of the art for NBC.