G1: Computational Microbial Biology
Antimicrobial resistance (AMR) is already a major global public health threat. By 2050, it is projected to cause 10 million deaths annually unless urgent measures are taken to curb its spread. We are leveraging genomic and proteomic data to address AMR, deepen our understanding of pathogen evolution, and identify novel antigens for vaccine development. Our research provides critical insights into how established and emerging pathogenic bacteria evolve and circulate across clinical, livestock, agricultural, wastewater, and aquatic environments.
Enlist areas of research
Genomics and Metagenomics
Microbial genome sequencing and assembly. Comparative genomics, metagenomic data analysis, Pan-genome and core-genome analysis, horizontal gene transfer getection
Transcriptomics and Proteomics
Microbial transcriptome analysis (RNA-seq), differential gene expression in microbial communities, microbial proteomics data mining, post-translational and modifications in microbes
Microbiome Research
Host-microbiome interaction modeling, microbiome-wide association studies, dysbiosis and disease prediction
Antimicrobial Resistance (AMR) and Pathogen Informatics
Pathogen genome surveillance, prediction of antimicrobial resistance genes, vaccine and drug target discovery
Computational Epidemiology and Evolution
Microbial phylogenetics and phylogenomic, molecular epidemiology of infectious diseases, microbial evolution and population genomics
Structural and Functional Bioinformatics
Protein structure prediction in microbes, molecular docking and dynamics for microbial proteins, Protein–protein and host–pathogen interaction modeling
Research Title
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Computational Microbial Biology
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Head Name
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Dr. Muhammad Ibrahim, Associate Professor
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1
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Dr. Sumaira Kanawal Associate Professor
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2
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Ms. Annam Hussain Research Associate
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3.
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Ms. Khalida Maqbool MS Student
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4
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Ms. Noor ul Ain MS Student
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