Inferring Gene Regulatory Neural Networks for Bacterial Decision Making in Biofilms

• URL: http://arxiv.org/abs/2301.04225v1
• Date: Tue, 10 Jan 2023 22:07:33 GMT
• Title: Inferring Gene Regulatory Neural Networks for Bacterial Decision Making in Biofilms
• Authors: Samitha Somathilaka, Daniel P. Martins, Xu Li, Yusong Li, Sasitharan Balasubramaniam
• Abstract summary: Bacterial cells are sensitive to a range of external signals used to learn the environment. An inherited Gene Regulatory Neural Network (GRNN) behavior enables the cellular decision-making. GRNNs can perform computational tasks for bio-hybrid computing systems.
• Score: 4.459301404374565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bacterial cells are sensitive to a range of external signals used to learn the environment. These incoming external signals are then processed using a Gene Regulatory Network (GRN), exhibiting similarities to modern computing algorithms. An in-depth analysis of gene expression dynamics suggests an inherited Gene Regulatory Neural Network (GRNN) behavior within the GRN that enables the cellular decision-making based on received signals from the environment and neighbor cells. In this study, we extract a sub-network of \textit{Pseudomonas aeruginosa} GRN that is associated with one virulence factor: pyocyanin production as a use case to investigate the GRNN behaviors. Further, using Graph Neural Network (GNN) architecture, we model a single species biofilm to reveal the role of GRNN dynamics on ecosystem-wide decision-making. Varying environmental conditions, we prove that the extracted GRNN computes input signals similar to natural decision-making process of the cell. Identifying of neural network behaviors in GRNs may lead to more accurate bacterial cell activity predictive models for many applications, including human health-related problems and agricultural applications. Further, this model can produce data on causal relationships throughout the network, enabling the possibility of designing tailor-made infection-controlling mechanisms. More interestingly, these GRNNs can perform computational tasks for bio-hybrid computing systems.

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