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Escherichia coli

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Antibacterial effects of thyme oil loaded solid lipid and chitosan nano-carriers against Salmonella Typhimurium and Escherichia coli as food preservatives.

PloS one
OBJECTIVES: Escherichia coli and Salmonella Typhimurium are frequent causes of foodborne illness affecting many people annually. In order to develop natural antimicrobial agents against these microorganisms, thyme oil (TO) was considered as active an...

Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery.

PloS one
Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are even better at eliminating infections. The fundamental transformation in a variety of scientific discip...

Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.

PLoS computational biology
Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most antibiotics, which means that for any given bacterial infection, the bacteria may be resistant to one or several antibiotics. It has been suggested that...

Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth.

Communications biology
A complex interplay of genetic and environmental factors influences bacterial growth. Understanding these interactions is crucial for insights into complex living systems. This study employs a data-driven approach to uncover the principles governing ...

Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli.

New biotechnology
In order to improve predictability of outcome and reduce costly rounds of trial-and-error, machine learning models have been of increasing importance in the field of synthetic biology. Besides applications in predicting genome annotation, process par...

Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.

Nature microbiology
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptide...

A Paradigm of Computer Vision and Deep Learning Empowers the Strain Screening and Bioprocess Detection.

Biotechnology and bioengineering
High-performance strain and corresponding fermentation process are essential for achieving efficient biomanufacturing. However, conventional offline detection methods for products are cumbersome and less stable, hindering the "Test" module in the ope...

ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer.

Computational biology and chemistry
The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying re...

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Science advances
Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained ...

Active Microrobots for Dual Removal of Biofilms via Chemical and Physical Mechanisms.

ACS applied materials & interfaces
Bacterial biofilms are complex multicellular communities that adhere firmly to solid surfaces. They are widely recognized as major threats to human health, contributing to issues such as persistent infections on medical implants and severe contaminat...