Biofilm research is undergoing a significant transformation due to a growing need to address biological complexity, temporal dynamics, and environmental sensitivity. These growing needs demand experimental platforms that are far more autonomous, scalable, and computationally integrated than current microbiology lab workflows. This paper presents a vision for a digital twin-driven “cyber-physical laboratory” that combines modular robotics, automated imaging, an Internet of Things network, and AI-based phenotyping into a cohesive system for next-generation computational biofilm engineering. We outline an eight-component architecture comprising twins for physical, environmental, biological, protocol, data, AI/ML, control, and safety constraints. This architecture will enable virtual experimentation, predictive simulation, and self-correction, with the goal of interactive total lab automation and the “robot scientist”. Although we demonstrate early prototypes, the main objective of this paper is to articulate a forward-looking blueprint for how cyber-physical labs with digital twinning can fundamentally reshape biofilm research. Two use cases implementation in our lab shows the feasibility with up to 500 samples run per day (10,000 samples per week). By integrating real-time data streams with predictive biological models and closed-loop automated control, this architecture sets the stage for autonomous, reproducible, and computationally guided biofilm experimentation. We propose this platform as a foundational vision for the future of computational biofilm engineering.

In the modern era, data availability has exponentially increased. From different universities, institutions, and laboratories across the globe, the wealth of information brings potential for integration and downstream analysis. This paper focuses specifically on the integration of RNA-sequencing data using data harmonization, a technique that reduces the inter-institutional batch effects that confound data quality with non-biological factors. One such framework is the DeepSeqDock framework, which creates data harmonization pipelines through training and tuning in specific domains and is evaluated on human RNA data from the SEQC. In this study, we present an alternate application of the adjusted DeepSeqDock framework for bacterial biofilm data. By adjusting for non-human data, we show the generalization of data harmonization and the DeepSeqDock for its applications in specific bacterial domains of interest for downstream analysis.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

The advent of transformer-based architectures such as TransUNet has revolutionized image segmentation as this approach combines the strengths of transformers for capturing contextual information with convolutional neural networks (CNNs) for localized feature identification. Microbes, known for their complex behaviors, present challenges in various fields, especially biomedicine. Image segmentation is crucial for ana-lyzing microbes, allowing quantitative analysis, growth tracking, and understanding host-pathogen interactions. This study is dedicated to a comparative analysis of TransUNet alongside two other popular segmentation methods, UNet and DoubleUNet, in the context of segmenting scanning electron microscope (SEM) images of microbes on layered graphene-nickel specimens. The TransUNet architecture employs a pre-defined ResNet-50 and Vision Transformer (ViT) as the encoder and a custom-built decoder trained on SEM data of Oleidesulfovibrio alaskensis (OA-G20) exposed to graphene-nickel specimens for 30 days. Using the Intersection Over Union (IoU) score as a performance metric, we observed that TransUNet achieved a maximum IoU of 79.58%, DoubleUNet exhibited a maximum IoU of 76.28%, and UNet attained a maximum IoU of 72.38%. We believe that this comparative study of the segmentation approach is invaluable for selecting the best model for the practitioner as per need. This study is the first step in our aim of developing an end-to-end framework with automated model selection based on dataset characteristics for microbial image segmentation.

In the ever-evolving landscape of research and clinical practices, the role of microscope-based image capture and analysis cannot be overstated. While current methodologies for micrograph analysis offer substantial power, there exist critical gaps, particularly in user functionality and reproducibility. To bridge these gaps, we introduce the Smart Imaging of Micrographs Process and Labeling (SIMPL) system as an open-source, semi-automated framework designed for image and video capture, analysis, and particle tracking. SIMPL aims to meet the demands of high-throughput applications, especially for reproducible microbe tracking.

Biological and biomedical microscope image (bioimage) comparison remains useful to approach many research challenges—from biofilms to human diseases. This powerful technology allows researchers to provide the community with a quick visual snapshot of varying experimental conditions. But a two-condition comparison still relies on a researcher’s eyes to draw conclusions despite the availability of multiple— often complex—digital image analysis tools. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an easy, objective, reproducible comparison leveraging inferential statistics to bridge image data analysis with other biomedical data modalities such as gene expression. Users have access to a machine learning module to assist with image segmentation using modern, trainable algorithms. BASIN also provides several key data points including images’ object counts, net and mean pixel intensities, net and mean object surface areas, plus a variety of other potentially useful data. Hypothesis testing is performed on mean object intensities and surface areas using the statistical power of the R programming language. These features allow BASIN to extend the current scope of image comparison. It gives researchers a multi-model knowledge about matters such as drug protein marker response, the significance of cell population changes, and changes in cell morphology. To improve BASIN’s accessibility and transparency we implemented it in R using Shiny framework and provided both an online trial version and a customizable offline version. We also have a batch version to run on datasets with hundreds of biomedical images. BASIN workflows consist of five core modules including image upload, feature extraction, statistical analysis, visualization, and report generation.