Biofilms and Biofouling

Biofilms and Biofouling Research Project

Why this project matters

Biofilms shape environmental processes, industrial performance, and many clinically important infections. They are structurally complex, dynamic, and often resistant to conventional treatment. Traditional observaion of biofilm development is informative, but it is frequently slow, labor-intensive, and difficult to scale.

Our project was initiated to change this vision. We develop a digital biology framework in which electron microscopy, high-throughput image processing, neural networks, synthetic data generation, and graph-based representations are combined with experimental microbiology and materials science. The result is a new way to observe biofilms not only as isolated images, but as quantitative systems that can be measured, compared, modeled, and redesigned.

Let us digitize biofilms and gain a new view of biofouling.

Project objectives

Create AI-enabled tools for quantitative and scalable analysis of biofilm morphology and growth.
Translate image-rich experiments into measurable biological and materials-science descriptors.
Connect digital analysis with real applications in sensing, antibiofilm materials, environmental monitoring, and sustainable catalysis.
Open the methodology for collaboration with biologists, chemists, materials scientists, and environmental researchers.

	Publications: AI tools	DOI
	Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy Digital Discovery 2023, 2, 1522-1539	10.1039/d3dd00048f
	Deep generative modeling of annotated bacterial biofilm images npj Biofilms and Microbiomes 2025, 11:16	10.1038/s41522-025-00647-4
	Deep learning-based high-information-content graph representation of early stage bacterial biofilms npj Biofilms and Microbiomes 2026	10.1038/s41522-026-00971-3

Publications: AI tools

DOI

Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy

Digital Discovery 2023, 2, 1522-1539

10.1039/d3dd00048f

Deep generative modeling of annotated bacterial biofilm images

npj Biofilms and Microbiomes 2025, 11:16

10.1038/s41522-025-00647-4

Deep learning-based high-information-content graph representation of early stage bacterial biofilms

npj Biofilms and Microbiomes 2026

10.1038/s41522-026-00971-3

	Publications: Applications	DOI
	"3-in-1" Hybrid Biocatalysts: Association of Yeast Cells Immobilized in a Sol-Gel Matrix for Determining Sewage Pollution ACS Applied Materials & Interfaces 2023, 15, 47779-47789	10.1021/acsami.3c09897
	Multicationic Quaternary Ammonium Compounds: A Framework for Combating Bacterial Resistance ACS Infectious Diseases 2023, 9, 1206-1220	10.1021/acsinfecdis.2c00546
	From Antibacterial to Antibiofilm Targeting: An Emerging Paradigm Shift in the Development of Quaternary Ammonium Compounds (QACs) ACS Infectious Diseases 2023, 9, 394-422	10.1021/acsinfecdis.2c00469
	Electroactive biofilms from activated sludge: Mechanistic insights into electron transport on nanostructured electrodes for the development of biosensors and microbial fuel cell devices Biosensors and Bioelectronics 2025, article 118195	10.1016/j.bios.2025.118195
	Aerobic bacteria-supported biohybrid palladium catalysts for efficient cross-coupling reactions Journal of Catalysis 2024, 429, 115238	10.1016/j.jcat.2023.115238
	Designing Effective Antimicrobial Agents: Structural Insights into the Antibiofilm Activity of Ionic Liquids Journal of Medicinal Chemistry 2025, 68, 2105-2123	10.1021/acs.jmedchem.4c01780
	Preparation of Hybrid Sol-Gel Materials Based on Living Cells of Microorganisms and Their Application in Nanotechnology Nanomaterials 2022, 12, 1086	10.3390/nano12071086
	Sustainable catalysts in a short time: harnessing bacteria for swift palladium nanoparticle production Nanoscale 2025, 17, 5289-5300	10.1039/d4nr03661a