Xiaofan Jin, PhD
BASc Engineering Science - Biomedical, University of Toronto, 2012MS Bioengineering, Stanford University, 2014
PhD Bioengineering, Stanford University, 2018
Postdoc Bioinformatics, Gladstone Institutes, 2024
Areas of Research
Synthetic biology
We are interested in using genetic engineering to rationally modify surface adhesion properties in bacteria, toward an ultimate goal of controlling structure in microbial communities and thus modulating community function. For instance, we have developed synthetic biology toolkits that allow for precise patterning of heterogenous biofilm communities, and used these toolkits in conjunction with biophysical modeling to characterize how patterning affects community functions, such as resistance to antimicrobial stress. In ongoing work, we are generalizing these approaches beyond simple communities of laboratory bacterial strains, and into more complex contexts such as the human gut microbiome. These efforts will pave the way toward rational manipulation of microbial communities for desired characteristics, such as the development of next-generation microbial therapeutics.
We are interested in using genetic engineering to rationally modify surface adhesion properties in bacteria, toward an ultimate goal of controlling structure in microbial communities and thus modulating community function. For instance, we have developed synthetic biology toolkits that allow for precise patterning of heterogenous biofilm communities, and used these toolkits in conjunction with biophysical modeling to characterize how patterning affects community functions, such as resistance to antimicrobial stress. In ongoing work, we are generalizing these approaches beyond simple communities of laboratory bacterial strains, and into more complex contexts such as the human gut microbiome. These efforts will pave the way toward rational manipulation of microbial communities for desired characteristics, such as the development of next-generation microbial therapeutics.
Gut microbiome
We are applying engineering approaches to study the gut microbiome by generating model microbial communities from the ground up, with tunable control over community composition and structure. For instance, we have developed in vitro culture platforms with synthetic mucosal surfaces, to profile how surface adhesion modulates function in a defined consortia of human gut microbes. In ongoing work, we are extending these platforms to explore the interplay between community composition, surface adhesion, and a variety of environmental stressors such as antimicrobial exposure or pathogenic invasion. This work will enable precise, quantitative characterization of how host-associated microbial communities critical to human health are affected by changes to their environments.
We are applying engineering approaches to study the gut microbiome by generating model microbial communities from the ground up, with tunable control over community composition and structure. For instance, we have developed in vitro culture platforms with synthetic mucosal surfaces, to profile how surface adhesion modulates function in a defined consortia of human gut microbes. In ongoing work, we are extending these platforms to explore the interplay between community composition, surface adhesion, and a variety of environmental stressors such as antimicrobial exposure or pathogenic invasion. This work will enable precise, quantitative characterization of how host-associated microbial communities critical to human health are affected by changes to their environments.
Metagenomics and bioinformatics
We are developing computational tools to track ecology and evolution in defined microbial communities, at the level of individual microbial strains and genes. These workflows analyze next generation sequencing datasets produced by shotgun metagenomic analysis of microbial communities, to reveal how these communities adapt at an eco-evolutionary level with resolution. In ongoing work, we are developing tools to track de novo mutations, genomic inversions, and instances of horizontal gene transfer across bacteria. We envision that such tools will be broadly applicable for a wide range of complex microbial communities, and will allow us to accurately identify critical eco-evolutionary events with consequences for human and environmental health, such as the emergence and spread of antimicrobial resistance.
We are developing computational tools to track ecology and evolution in defined microbial communities, at the level of individual microbial strains and genes. These workflows analyze next generation sequencing datasets produced by shotgun metagenomic analysis of microbial communities, to reveal how these communities adapt at an eco-evolutionary level with resolution. In ongoing work, we are developing tools to track de novo mutations, genomic inversions, and instances of horizontal gene transfer across bacteria. We envision that such tools will be broadly applicable for a wide range of complex microbial communities, and will allow us to accurately identify critical eco-evolutionary events with consequences for human and environmental health, such as the emergence and spread of antimicrobial resistance.
Supervising degrees
Biomedical Engineering - Masters: Seeking Students
Biomedical Engineering - Doctoral: Accepting Inquiries
More information
Working with this supervisor
Our lab applies synthetic and computational biology approaches to study microbial communities such as the gut microbiome. We engineer and manipulate microbial communities with defined spatial structure, and use integrated experimental-bioinformatic approaches to measure / model how spatial structure affects community ecology, evolution, and function. Students interested in these topics and who have biology, engineering, or computational backgrounds can contact Xiaofan directly about potential positions. Please email a cover letter, CV, transcripts, and a sample of your own scientific writing (e.g., a research manuscript, report, or fellowship application you authored).