Paul Ficek's keynote about using data science to build a bridge from model systems to human disease, in #jobim2024
Starts with history of mouse models. Jackson ("JAX") lab founded in 1929 (by Clarence C. Little), with the objective to study genetic diseases on newly bred JAX mice®️ (sic). Continues to do so today. Data science is —like mice— scientific enablers.
Think about the mouse as a genome model. But different from human, but complementary. Mouse models for rare diseases are very good.
Example of the KIF1A campaign to create a mouse model. #WeNeedAMouse
Transcriptional regulation was thought to be simple, but now we see that conserved regulation is very rare. In fact local sequence effects drive the transcription factors binding. Shown with the Tc1 mice, hosting a human chromosome.
Gene expression evolves differently than TF binding. GE is evolutionary stable, TF binding is neitwer stable or maintained.
Highly expressed genes are more evolutionary stable. The *number* of regulatory elements is a primary driver.
Now, compares genomes and evol. "forces" in great apes and major mammalian models (muridæ!). Observed evol. punctuated equilibriums for mice. Evol. rates are different from human. Six times faster in Muridæ.
Next steps: mouse pangenome project.
Each human could have individualized mouse model. A global catalog of mouse model, made with a data-driven approach.
But mouse model research does not scale. We need to go beyond using a single mouse strain, whnch leads to several problems. Should we pick a single strain, or a set of strains? We can produce an illimited number of "siblings" strains, instead of relying in twins.
We need to make connections between knowledge sources, "like with Knowledge Graphs" (❤!). Tries to make connections between mouse and human. JAX actually develops a KG, for example to build a strain recommender.
Matches disease states to mouse strain. Example in macular degeneration. Can suggest mouse strains highly sensitive, some without the expected mutations.
Can probe for background modifiers of disease. Example in TSC epilepsy. Then back in the KG to find out why a strain is sensitive.
Next. Digital cages, watching mices constantly to quantify behaviours, "digital biomarkers". Example with loss of righting reflex.
Wrap-up: power of complementary models for disease research. Digital+ in vivo + cells.
Bridges are built with many strands working together.
EOT
Johann Dr.EO