Multivariate landscapes constructed by Bayesian estimation over five hundred microbial electrochemical time profiles

Waheed Miran; Wenyuan Huang; Xizi Long; Gaku Imamura; Akihiro Okamoto

doi:10.1016/j.patter.2022.100610

Multivariate landscapes constructed by Bayesian estimation over five hundred microbial electrochemical time profiles

Patterns (N Y). 2022 Oct 19;3(11):100610. doi: 10.1016/j.patter.2022.100610. eCollection 2022 Nov 11.

Authors

Waheed Miran^{1

2}, Wenyuan Huang^{1

3}, Xizi Long¹, Gaku Imamura^{1

4}, Akihiro Okamoto^{1

3}

Affiliations

¹ International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.
² School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.
³ Graduate School of Chemical Sciences and Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan.
⁴ Graduate School of Information Science and Technology, Osaka University, 1-2 Yamadaoka, Suita, Osaka 565-0871, Japan.

Abstract

Data science emerges as a promising approach for studying and optimizing complex multivariable phenomena, such as the interaction between microorganisms and electrodes. However, there have been limited reports on a bioelectrochemical system that can produce a reliable database until date. Herein, we developed a high-throughput platform with low deviation to apply two-dimensional (2D) Bayesian estimation for electrode potential and redox-active additive concentration to optimize microbial current production (I _c ). A 96-channel potentiostat represents <10% SD for maximum I _c . 576 time-I _c profiles were obtained in 120 different electrolyte and potentiostatic conditions with two model electrogenic bacteria, Shewanella and Geobacter. Acquisition functions showed the highest performance per concentration for riboflavin over a wide potential range in Shewanella. The underlying mechanism was validated by electrochemical analysis with mutant strains lacking outer-membrane redox enzymes. We anticipate that the combination of data science and high-throughput electrochemistry will greatly accelerate a breakthrough for bioelectrochemical technologies.

Keywords: Bayesian estimation; Gaussian process; bioelectrochemical system; bound flavin; data science; extracellular electron transfer; high-quality database; high-throughput electrochemistry; mediators; redox shuttles.