Synthetic neuromorphic computing in living cells.

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

Disinfectants role in the prevention of spreading the COVID-19 and other infectious diseases: The need for functional polymers!

The spreading of coronavirus through droplets and aerosols of an infected person is a well-known mechanism. The main protection methods from this virus are using disinfectants/sanitizers, face masks, keeping social distance, and vaccination. With the rapid mutations of the virus accompanied by its features and contagions changing, new advanced functional materials development is highly needed. The usage of disinfectants/sanitizers in excess generates poisonous effects among the general public. Effective and simultaneously, human-friendly sanitizers or disinfectants are required to prevent the poisoning and the associated issues. They minimize the toxic effects of the currently available materials by rapid action, high potential, long-term stability, and excellent biocompatible nature. Here, we summarize the available antiviral materials, their features, and their limitations. We highlight the need to develop an arsenal of advanced functional antiviral polymers with intrinsic bioactive functionalities or released bioactive moieties in a controlled manner for rapid and long-term actions for current and future anticipated viral outbreaks.

Synthetic neural-like computing in microbial consortia for pattern recognition.

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.