Unraveling causality in microbiome research

By eating mozzarella, you can contribute to the number of civil engineering doctorates awarded. Don’t believe us? Just look at the following graph:

It is even statistically significant, yielding a p-value of 0.000012. But before you go emptying out your local store’s mozzarella supply, you might want to take this with a grain of salt: correlation does not imply causation. While this peculiar relation between these two facts could mean that one causes the other, it is a lot more plausible that these either have a common cause or just happen to coincide in this particular timeframe.

However, testing every association in the lab takes time and can even be unethical, since we can’t just give people a disease to see how they respond. In the context of a VLAIO research grant, BioLizard is now investigating the causality behind microbiome-disease associations. Taking into account the broadest picture of microbes, patient background and interactions, we are building a framework using state-of-the-art AI tools to evaluate causal links in the human microbiome, purely from data. This way we can check whether microbe A causes disease B or if they’re only distantly related, refining our focus. Our goal with this research is to support biopharmaceuticals by discovering drug targets and biomarkers supported by real cause-effect relations.

References:

[1] https://www.nature.com/articles/nature07540/

[2] https://www.nature.com/articles/s41598-017-13601-y/

[3] https://www.sciencedirect.com/science/article/pii/S0016508520347016

[4] https://www.nature.com/articles/d41586-019-01654-0?proof=true