Desulfovibrio alaskensis G20 (DA-G20) is utilized as a model for sulfate-reducing bacteria (SRB) that are associated with corrosion issues caused by microorganisms. SRB-based biofilms are thought to be responsible for the billion-dollar-per-year bio-corrosion of metal infrastructure. Understanding the extraction of the bacterial cells' shape and size properties in the SRB-biofilm at different growth stages will assist with the design of anti-corrosion techniques. However, numerous issues affect current approaches, including time-consuming geometric property extraction, low efficiency, and high error rates. This paper proposes BiofilScanner, a Yolact-based deep learning method integrated with invariant moments to address these problems. Our approach efficiently detects and segments bacterial cells in an SRB image while simultaneously invariant moments measure the geometric characteristics of the segmented cells with low errors. The numerical experiments of the proposed method demonstrate that the BiofilmScanner is 2.1x and 6.8x faster than our earlier Mask-RCNN and DLv3+ methods for detecting, segmenting, and measuring the geometric properties of the cell. Furthermore, the BiofilmScanner achieved an F1-score of 85.28% while Mask-RCNN and DLv3+ obtained F1-scores of 77.67% and 75.18%, respectively.

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.

An amazing aspect of living in The Fourth Industrial Era is that we are at a new inflection point in bringing emerging technologies to life. We are in an era of scientific breakthroughs that will change the way of life as we currently know it. While there are many technological areas of fascination for me, the meshing of biology with machine is one of the most intriguing. I have highlighted two frontiers of "mind-bending" developments that are on the horizon, Neuromorphic technologies, and human-machine biology. Human computer interaction (HCI) was an area of research that started in the 1980s and has come a long way in a short period of time.

Superbugs capable of everything from curing diseases to mopping up pollution have come a step closer after scientists created an artificial lifeform in a lab. The new bacterial cell, nicknamed Synthia 3.0, has fewer genes than any other bacterium, making it the most basic form of life on Earth. Its creation paves the way for microbes that can be customised with genes so they churn out clean biofuels, soak up carbon dioxide from the atmosphere or pump out vaccines in industrial quantities. Researchers have designed and synthesized a minimal bacterial genome, containing only the 473 genes necessary for life. Dr Craig Venter who led the research team, said: 'I think it's the start of a new era.'