@inproceedings{435733a357254a7aa4b194b479e1ffcf,
title = "Modeling biochemical reactions and gene networks with memristors",
abstract = "This paper investigates qualitative and quantitative analogies between biochemical reactions and memristive devices. It shows that memristors can mimic biochemical reactions and gene networks efficiently, and capture both deterministic and stochastic dynamics at the nanoscale level. We present different abstraction models and memristor-based circuits that inherently model the activity of genetic circuits with low signal-to-noise ratio (SNR). These findings constitute a promising step towards noise-tolerant and energy-efficient electronic circuit design, which can provide a fast and simple emulative framework for large-scale synthetic molecular system design in cell biology.",
keywords = "Cytomorphic, genetics, memristors, molecular biology, synthetic biology, systems biology",
author = "Hanna, {Hanna Abo} and Loai Danial and Shahar Kvatinsky and Ramez Daniel",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 ; Conference date: 19-10-2017 Through 21-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "https://doi.org/10.1109/BIOCAS.2017.8325229",
language = "الإنجليزيّة",
series = "2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings",
pages = "1--4",
booktitle = "2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings",
}