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.

A Graph-Based Molecular Communications Model Analysis of the Human Gut Bacteriome.

Alterations in the human Gut Bacteriome (GB) can be associated with human health issues, such as type-2 diabetes and obesity. Both external and internal factors can drive changes in the composition and in interactions of the human GB, impacting negatively on the host cells. This paper focuses on the human GB metabolism and proposes a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in the human GB network system. Our results show that regulation of molecular inputs strongly affects bacterial population growth and creates an unbalanced network, as shown by shifts in the node weights based on the produced molecular signals. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human GB network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviours of the human GB depending on molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial population growth and exchange of metabolites.