Statistical models of organisms have existed for decades. The earliest ones relied on simple linear regression and attempted to correlate genetic vari

Models of Life - by Asimov Press and Abhishaike Mahajan

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2024-09-29 17:30:04

Statistical models of organisms have existed for decades. The earliest ones relied on simple linear regression and attempted to correlate genetic variations with observable traits or disease risks — such as drug metabolization rates or cancer susceptibility. As computational power increased and machine learning techniques advanced, the models’ sophistication grew.

The definition was nebulous, but there were agreed-upon themes. All models of life were aimed at improving our understanding of the cellular mechanisms underlying biology and were neither constrained by human intuition nor limited to predefined hypotheses. They operated in high-dimensional spaces that defied simple visualization while incorporating vast layers of interconnected variables that no human mind could fully grasp. Unlike traditional scientific models, which often simplified reality, these models embraced its messy and chaotic nature.

Given the fuzziness of the definition, it was unclear which of the released projects deserved the name. There was scFormer in 2022, scGPT in 2023, and plenty of others. But, regardless of which was first, they all operated with the same core data as their mechanism for understanding life: messenger RNA ( mRNA).