QBIOL: A quantum bioelectrochemical software based on point stochastic processes

Simon Grall; Ignacio Madrid; Aramis Dufour; Helen Sands; Masaki Kato; Akira Fujiwara; Soo Hyeon Kim; Arnaud Chovin; Christophe Demaille; Nicolas Clement

arXiv · 2503.07394

QBIOL: A quantum bioelectrochemical software based on point stochastic processes

Simon Grall·Ignacio Madrid·Aramis Dufour·Helen Sands·Masaki Kato·Akira Fujiwara·Soo Hyeon Kim·Arnaud Chovin·Christophe Demaille·Nicolas Clement

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Abstract

Bioelectrochemistry is crucial for understanding biological functions and driving applications in synthetic biology, healthcare, and catalysis. However, current simulation methods fail to capture both the stochastic nature of molecular motion and electron transfer across the relevant picosecond-to-minute timescales. We present QBIOL, a web-accessible software that integrates molecular dynamics, applied mathematics, GPU programming, and quantum charge transport to address this challenge. QBIOL enables quantitative stochastic electron transfer simulations and has the potential to reproduce numerically any (bio) electrochemical experiments. We illustrate this potential by comparing our simulations with experimental data on the current generated by electrode-attached redox-labeled DNA, or by nanoconfined redox species, in response to a variety of electrical excitation waveforms, configurations of interest in biosensing and catalysis. The adaptable architecture of QBIOL extends to the development of devices for quantum and molecular technologies, positioning our software as a powerful tool for enabling new research in this rapidly evolving field.

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Simon Grall, Ignacio Madrid, Aramis Dufour, Helen Sands, Masaki Kato, Akira Fujiwara, Soo Hyeon Kim, Arnaud Chovin, Christophe Demaille, Nicolas Clement. 2025-03-10. QBIOL: A quantum bioelectrochemical software based on point stochastic processes. https://arxiv.org/abs/2503.07394

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