Redox, a native modality in biology involving the flow of electrons, energy, and information, is used for energy-harvesting, biosynthesis, immune-defense, and signaling. Because electrons (in contrast Show more
Redox, a native modality in biology involving the flow of electrons, energy, and information, is used for energy-harvesting, biosynthesis, immune-defense, and signaling. Because electrons (in contrast to protons) are not soluble in the medium, electron-flow through the redox modality occurs through redox reactions that are sometimes organized into pathways and networks (e.g., redox interactomes). Redox is also accessible to electrochemistry, which enables electrodes to receive and transmit electrons to exchange energy and information with biology. In this Perspective, efforts to develop electrochemistry as a tool for redox-based bio-information processing: to interconvert redox-based molecular attributes into interpretable electronic signals, are described. Using a series of Case Studies, how the information-content of the measurements can be enriched using: diffusible mediators; tuned electrical input sequences; and cross-modal measurements (e.g., electrical plus spectral), is shown. Also, theory-guided feature engineering approaches to compress the information in the electronic signals into quantitative metrics (i.e., features) that can serve as correlating variables for pattern recognition by data-driven analysis are described. Finally, how redox provides a modality for electrogenetic actuation is illustrated. It is suggested that electrochemistry's capabilities to provide real-time, low-cost, and high-content data in an electronic format allow the feedback-control needed for autonomous learning and deployable sensing/actuation. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation â and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less