Next-Gen Biofilm Control: Gene Editing and Computational Approaches

Area of Science:

• Microbiology
• Biotechnology
• Bioinformatics

Background:

• Biofilms, microbial communities in extracellular polymeric substance (EPS), significantly contribute to antimicrobial resistance (AMR).
• Their structure protects microbes, hinders antibiotic penetration, and promotes gene transfer, exacerbating the AMR crisis.
• Conventional treatments are often ineffective against biofilms, demanding novel therapeutic approaches.

Purpose of the Study:

• To review biofilm biology and explore next-generation biofilm control strategies.
• To highlight the role of AI-driven bioinformatics in combating biofilm-associated AMR.
• To discuss emerging interventions and their potential impact.

Main Methods:

• Review of current literature on biofilm formation and antimicrobial resistance.
• Exploration of advanced therapeutic strategies including nanotechnology, bacteriophage therapy, and CRISPR-Cas9.
• Analysis of the application of Artificial Intelligence (AI) and Machine Learning (ML) in biofilm research and antimicrobial development.

Main Results:

• Nanotechnology enhances drug delivery, bacteriophages selectively target resistant bacteria, and CRISPR-Cas9 disrupts AMR genes.
• AI and ML are advancing biofilm prediction and antimicrobial optimization for precision interventions.
• Emerging strategies show significant promise in overcoming biofilm-associated AMR.

Conclusions:

• Novel strategies like nanotechnology, phage therapy, and gene editing are crucial for combating biofilm-associated AMR.
• AI-driven bioinformatics offers powerful tools for understanding and controlling biofilms.
• Clinical translation, standardization, and scalable implementation are essential for future success.