The vision of biocomputing is to develop computing paradigms using biological systems, ranging from micron-level components to collections of cells, such as organoids. This paradigm shift exploits hidden natural computing properties, developing miniaturized wet computing devices deployable in harsh environments, and exploring designing novel energyefficient systems. Parallelly, we witness the emergence of AI hardware including neuromorphic processors aiming to improve computational capacity. This study brings together the concept of bio-computing and neuromorphic systems by focusing on the Bacterial gene regulatory networks and their transformation into Gene Regulatory Neural Networks (GRNNs) that can be used for biocomputing. We explore the intrinsic properties of gene regulations, map this to a gene-perceptron function, and propose an application-specific sub-GRNN search algorithm that maps the network structure to match a problem. Focusing on the model organism Escherichia coli (E. coli), the base-GRNN is initially extracted and validated for accuracy. Subsequently, a comprehensive feasibility analysis of the derived GRNN confirms its computational prowess in classification and regression tasks. Furthermore, we discuss the possibility of performing a wellknown digit classification task as a use case. Our analysis and simulation experiments show promising results in offloading computation to GRNN in bacterial cells, advancing wet-neuromorphic computing using natural cells.

As the world searches for groundbreaking, unconventional computing technologies, especially for intelligent edge applications, biological AI is emerging as an energy-efficient, robust, and reliable alternative. Researchers have unveiled the immense computing capacity inherent in biocomputing elements such as bacterial cells. The computing power of bacteria can be harnessed through Gene Regulatory Neural Networks (GRNNs). Biofilms, acting as sophisticated collections of GRNNs, leverage the natural distributed computing architecture with capabilities like parallel processing and analog computing in individual cells while consuming very little energy relative to conventional computing systems. This study introduces the concept of Biofilm Living AI Devices (BLAIDs), which proposes engineering biofilms using optogenetics to function as self-sustaining AI edge devices that interface with modern telecommunications architectures. Our simulation-based analysis demonstrates the computing complexity and reliability of BLAID, establishing it as a compelling candidate for the next generation of low-energy computing and advanced AI technologies.

As we delve into a life governed by AI, ongoing research continues to discover new forms of intelligence that are efficient and closely mimic an organism's brain in terms of performance. This article presents a new concept termed wet-neuromorphic computing, where neural network or similar structures from living cells and organisms are extracted to do computing. The wet-neuromorphic computing concept allow the (i) use biological cells or organisms to perform computing task by exploiting their natural molecular functions as living machines, or (ii) get inspiration to create novel bio-inspired algorithms diverging from traditionally from neural systems. We first present background on neuromorphic computing, and match their key properties to biological AI neural networks that are found in bacteria, 3D organoids, as well as neural system of C.Elegans. In each of these systems, we analyze their natural computing functions and review state-of-the-art research that has proposed using each of these systems for AI computing. As a case study, we detail experiments using bacterial cells for AI computing, harnessing their gene regulatory mechanisms to control growth levels, where we present wet-lab experimental results demonstrating the use of bacteria for pattern recognition application. Lastly, we discuss challenges and future directions for wet-neuromorphic computing.

The Gene Regulatory Network (GRN) in biological cells orchestrates essential functions for adaptation and survival in diverse environments, drawing on structural similarities with the Artificial Neural Network (ANN) to transform into the Gene Regulatory Neural Network (GRNN). This transformation enables exploration of their natural computing capabilities regarding network reconfigurability and controllability, facilitating dynamic adjustments of gene-gene interaction weights to regulate biological processes. In this paper, we present a control-theoretic model for the GRNN that determines optimal chemical input concentrations, steering the GRNN towards desired weight configurations using the Linear Quadratic Regulator (LQR) approach. This method enhances network robustness by optimizing stability and minimizing plasticity, ensuring adaptive reconfigurability in dynamic environments. We develop mathematical models to identify critical genes using a Continuous-Time Markov Chain (CTMC) and derive temporal weight configurations, providing insights into the system's reconfiguration dynamics, while also quantifying stability and plasticity. Our findings demonstrate the effectiveness of the control model in mitigating Clostridioides difficile biofilm formation, outperforming sub-optimal and stochastic inputs, and highlighting the importance of determining optimal inputs for robust network behavior across diverse complexities.