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Redox is a unique, programmable modality capable of bridging communication between biology and electronics. Previous studies have shown that theE. coliredox-responsive OxyRS regulon can be re-wired to accept electrochemically generated hydrogen peroxide (H₂O₂) as an inducer of gene expression. Here we report that the redox-active phenolic plant signaling molecule acetosyringone (AS) can also induce gene expression from the OxyRS regulon. AS must be oxidized, however, as the reduced state present under normal conditions cannot induce gene expression. Thus, AS serves as a “pro-signaling molecule” that can be activated by its oxidation—in our case by application of oxidizing potential to an electrode. We show that the OxyRS regulon is not induced electrochemically if the imposed electrode potential is in the mid-physiological range. Electronically sliding the applied potential to either oxidative or reductive extremes induces this regulon but through different mechanisms: reduction of O₂to form H₂O₂or oxidation of AS. Fundamentally, this work reinforces the emerging concept that redox signaling depends more on molecular activities than molecular structure. From an applications perspective, the creation of an electronically programmed “pro-signal” dramatically expands the toolbox for electronic control of biological responses in microbes, including in complex environments, cell-based materials, and biomanufacturing.

Diversity, group representation, and similar needs often apply to query results, which in turn require constraints on the sizes of various subgroups in the result set. Traditional relational queries only specify conditions as part of the query predicate(s), and do not support such restrictions on the output. In this paper, we study the problem of modifying queries to have the result satisfy constraints on the sizes of multiple subgroups in it. This problem, in the worst case, cannot be solved in polynomial time. Yet, with the help of provenance annotation, we are able to develop a query refinement method that works quite efficiently, as we demonstrate through extensive experiments.

Microelectronic devices can directly communicate with biology, as electronic information can be transmitted via redox reactions within biological systems. By engineering biology’s native redox networks, we enable electronic interrogation and control of biological systems at several hierarchical levels: proteins, cells, and cell consortia. First, electro-biofabrication facilitates on-device biological component assembly. Then, electrode-actuated redox data transmission and redox-linked synthetic biology allows programming of enzyme activity and closed-loop electrogenetic control of cellular function. Specifically, horseradish peroxidase is assembled onto interdigitated electrodes where electrode-generated hydrogen peroxide controls its activity.E. coli’s stress response regulon,oxyRS, is rewired to enable algorithm-based feedback control of gene expression, including an eCRISPR module that switches cell-cell quorum sensing communication from one autoinducer to another—creating an electronically controlled ‘bilingual’ cell. Then, these disparate redox-guided devices are wirelessly connected, enabling real-time communication and user-based control. We suggest these methodologies will help us to better understand and develop sophisticated control for biology.

Relational queries are commonly used to support decision making in critical domains like hiring and college admissions. For example, a college admissions officer may need to select a subset of the applicants for in-person interviews, who individually meet the qualification requirements (e.g., have a sufficiently high GPA) and are collectively demographically diverse (e.g., include a sufficient number of candidates of each gender and of each race). However, traditional relational queries only support selection conditions checked against each input tuple, and they do not support diversity conditions checked against multiple, possibly overlapping, groups of output tuples. To address this shortcoming, we present Erica, an interactive system that proposes minimal modifications for selection queries to have them satisfy constraints on the cardinalities of multiple groups in the result. We demonstrate the effectiveness of Erica using several real-life datasets and diversity requirements.