A Framework for Implementing Metaheuristic Algorithms Using Intercellular Communication

Synthetic biological neural networks: From current implementations to future perspectives

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

Revealing gene regulation-based neural network computing in bacteria

Bacteria are known to interpret a range of external molecular signals that are crucial for sensing environmental conditions and adapting their behaviors accordingly. These external signals are processed through a multitude of signaling transduction networks that include the gene regulatory network (GRN). From close observation, the GRN resembles and exhibits structural and functional properties that are similar to artificial neural networks. An in-depth analysis of gene expression dynamics further provides a new viewpoint of characterizing the inherited computing properties underlying the GRN of bacteria despite being non-neuronal organisms. In this study, we introduce a model to quantify the gene-to-gene interaction dynamics that can be embedded in the GRN as weights, converting a GRN to gene regulatory neural network (GRNN). Focusing on Pseudomonas aeruginosa, we extracted the GRNN associated with a well-known virulence factor, pyocyanin production, using an introduced weight extraction technique based on transcriptomic data and proving its computing accuracy using wet-lab experimental data. As part of our analysis, we evaluated the structural changes in the GRNN based on mutagenesis to determine its varying computing behavior. Furthermore, we model the ecosystem-wide cell-cell communications to analyze its impact on computing based on environmental as well as population signals, where we determine the impact on the computing reliability. Subsequently, we establish that the individual GRNNs can be clustered to collectively form computing units with similar behaviors to single-layer perceptrons with varying sigmoidal activation functions spatio-temporally within an ecosystem. We believe that this will lay the groundwork toward molecular machine learning systems that can see artificial intelligence move toward non-silicon devices, or living artificial intelligence, as well as giving us new insights into bacterial natural computing.