Genetic Timer Reveals Moments of Gene Activation and Deactivation via AI Technology
In a groundbreaking study, researchers at Yale University have developed a new computational tool called chronODE to model genetic activation and chromatin changes during brain development. The team, led by Mark Gerstein, includes computational biologist Beatrice Borsari and biomedical engineer Frank, along with research associates Wattenberg, Xu, Liu, and Yu.
The name "chronODE" is a fusion of the concept of time and the mathematical framework of ordinary differential equations. This innovative method is designed to model gene-expression and chromatin kinetics from longitudinal studies.
According to the researchers, genes are grouped into three major kinetic patterns: accelerators, switchers, and decelerators. Two parameters in chronODE capture biophysical constraints governing the initial cooperativity and later saturation in gene expression.
The study reveals that most genes (approximately 87%) follow simple logistic kinetics. Genes with rapid acceleration and high saturation values are rare, indicating biochemical limitations. Interestingly, early- and late-emerging cell types display distinct kinetic patterns, with essential genes ramping up faster.
One of the key features of chronODE is a bidirectional recurrent neural network, which predicts changes in gene expression from corresponding chromatin changes. This aspect of the framework allows for the investigation of the kinetics of gene regulation in diverse biological systems.
Moreover, the study extends to chromatin, revealing that genes regulated by both enhancer and silencer cis-regulatory elements are enriched in brain-specific functions.
In summary, chronODE offers a powerful new tool for analysing time-series biological data and has the potential to shed light on the complex biological phenomena at the intersection of biology and mathematics.
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