Can Gut Bacteria Change the Brain?

The question

This project asks whether biological changes linked to alcohol and caffeine exposure can be used to identify gut microbes worth testing as possible modulators of the gut–brain axis. I compared metabolomic datasets, looked for shared pathway-level changes, and built a computational pipeline to rank probiotic candidates for follow-up testing. The project has since moved toward experimental validation, bringing the work back from computational predictions to the wet lab.

Teaching an AI to search for probiotics

The ranking system began with a practical problem: a long list of possible probiotic species is not useful if there is no principled way to decide which ones deserve experimental attention first.

I combined metabolomic evidence from alcohol- and caffeine-exposure datasets with pathway-level changes and microbial metabolic models, then used those features to prioritize organisms whose predicted behavior best matched the biological signals I wanted to investigate. The goal was not to let a model declare a “best probiotic,” but to use computation as a filter.

Add a pipeline diagram or screenshot of the ranking workflow

Full circle: from computer predictions to the wet lab

My first research experience was entirely experimental. Then I spent several years moving deeper into dry-lab work: transcriptomics, metabolomics, machine learning, and modeling. With this project, that path has started to loop back on itself.

The computational pipeline can suggest what might be worth testing, but eventually the model has to meet biology. Returning to wet-lab validation feels less like changing directions and more like completing the circuit: experiment → model → prediction → experiment.

Methods

Add the detailed methods write-up: sample handling, metabolomic workflow, the scoring criteria used for candidate strains, and the validation assays

Where it stands

Validation is ongoing, and some of the technical detail is being kept back until the work is public.