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Applying Machine Learning to…..Yeast?

Wednesday, April 29, 2020

Posted by Ted Baltz, Senior Staff Software Engineer, Google Research, Accelerated Science TeamSaccharomyces cerevisiae,sequence homologCalico Life SciencesLearning causal networks using inducible transcription factors and transcriptome-wide time seriesMolecular Systems Biologygene expressionS. cerevisiaeInduction Dynamics gene Expression AtlaspythonGitHubGene Expression OmnibusUsing Yeast to Provide Insight into Aging

Bud scars on old yeast cells (5 μm bar for scale) — Photo Credit: Ian Foe, (Calico)

Calico Life SciencesThe Gene Expression ExperimentDNARNAribosomesproteinmicroarraystranscription factorsabcd

Schematic of the genome perturbation experiment: yeast strain with switchable gene “a”. Turning on a single gene (A) can result in differing levels of gene expression over time (B). Tracking these changes in comparison to those induced by turning on other genes (C and D) can provide insight into the regulation mechanisms (E).

The Gene Expression ModelL1-normModel Resultswithout prior biological knowledgecommentaryAcknowledgementsWe wish to thank Marc Coram, Minjie Fan, and Marc Berndl for their foundational contributions to this work, the Google Accelerated Science team for their continual support, and the entire team at Calico for the opportunity to collaborate on this experiment.