The distress of this new pandemic highlights what began in the human field a long time ago.
Bacteria, viruses and parasites are a great hazard to human health, and a challenge to prevent and treat. One trait common to most pathogens is their ability to modulate, manipulate, and escape host immune responses. Additionally, a lack of new treatments leads to untreatable infections becoming more widespread with increased morbidity and mortality.
Disciplines such as biological computing and "omics" approaches, integrate high-throughput data making possible a better understanding of complex systems, as well as mechanisms of resistance.
Mass spectrometry provides accurate protein quantification, and together with computational and theoretical analysis has led to a quantitative understanding of these complex biological systems in terms of their components and interactions. The study of the host-pathogen protein interactions – including their evolution, both over time and under different therapeutic strategies – as well as predictive models, are the key for better understanding complex systems and unlocking the door to new strategies.
This Research Topic emerges in a special situation, but also in the framework of new technologies and an interconnected world. The aim of this collection is to unravel the protein-protein interaction network to better understand the dynamics of infectious diseases, with focus on mechanisms of resistance and immune evasion.
This Research Topic welcomes submissions of proteomics (and metabolomics) approaches, as well as bioinformatic analyses with methods such as deep learning or machine learning, which can help to provide novel findings, and also provide a platform for novel strategies for clinical diagnosis and treatments, affecting everything from biomarkers to vaccines.
This Topic welcomes manuscripts in the areas below:
• Quantitative Proteomics of microbial Human pathogens
• Proteomics applied to the understanding of the mechanisms that regulate host-pathogen interaction and mechanism of antimicrobial resistance
• Discovery of novel protein biomarkers for microbial infections
• Application of Machine learning to Proteomics data to target biomarkers for infectious disease diagnostics and drug therapy
The distress of this new pandemic highlights what began in the human field a long time ago.
Bacteria, viruses and parasites are a great hazard to human health, and a challenge to prevent and treat. One trait common to most pathogens is their ability to modulate, manipulate, and escape host immune responses. Additionally, a lack of new treatments leads to untreatable infections becoming more widespread with increased morbidity and mortality.
Disciplines such as biological computing and "omics" approaches, integrate high-throughput data making possible a better understanding of complex systems, as well as mechanisms of resistance.
Mass spectrometry provides accurate protein quantification, and together with computational and theoretical analysis has led to a quantitative understanding of these complex biological systems in terms of their components and interactions. The study of the host-pathogen protein interactions – including their evolution, both over time and under different therapeutic strategies – as well as predictive models, are the key for better understanding complex systems and unlocking the door to new strategies.
This Research Topic emerges in a special situation, but also in the framework of new technologies and an interconnected world. The aim of this collection is to unravel the protein-protein interaction network to better understand the dynamics of infectious diseases, with focus on mechanisms of resistance and immune evasion.
This Research Topic welcomes submissions of proteomics (and metabolomics) approaches, as well as bioinformatic analyses with methods such as deep learning or machine learning, which can help to provide novel findings, and also provide a platform for novel strategies for clinical diagnosis and treatments, affecting everything from biomarkers to vaccines.
This Topic welcomes manuscripts in the areas below:
• Quantitative Proteomics of microbial Human pathogens
• Proteomics applied to the understanding of the mechanisms that regulate host-pathogen interaction and mechanism of antimicrobial resistance
• Discovery of novel protein biomarkers for microbial infections
• Application of Machine learning to Proteomics data to target biomarkers for infectious disease diagnostics and drug therapy