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Mapping gene expression regulation in yeast to understand ageing

Made possible by Calico's research expertise and Google AI's machine learning tech


Key points from article :

Team built a genome-wide model for regulation of gene expression in S. cerevisiae.

They have pioneered a technique to make targeted perturbations to its gene expression.

With the goal of understanding how ageing works at the molecular level.

It will measure, over time, how every other gene in the genome responds.

This allows learning causal relationships, non-linear behaviors.

Experiments were done in parallel, with one yeast strain per culture, running concurrently.

Approach was to model the whole process as a system of differential equations.

Rate of change of gene expression was proportional to weighted sum of expression levels of all genes.

Model made predictions on which genes would code for intermediate regulators of gene expression.

Hopes that understanding ageing in yeast will apply to complex organisms, like humans.

By Google AI, in collaboration with Calico Life Sciences, published in Molecular Systems Biology.

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Combating aging and associated diseases. An Alphabet (Google) subsidiary.

Google AI

Artificial intelligence company

IDEA: Induction dynamics gene expression atlas

Global dynamic model of transcriptional regulation in budding yeast

Molecular Systems Biology

Peer-reviewed journal

Topics mentioned on this page:
AI in Healthcare, Gene Therapy