SCOOTI: Single-Cell Optimization Objective and Tradeoff Inference is a data-driven computational framework to identify cell-specific objectives and trade-offs from transcriptomics data. Formulate metabolic objectives for various cell types, such as cancer cell lines and embryos, in different conditions.

2025年1月15日 · How do cells balance competing metabolic needs? This study introduces SCOOTI, a tool to infer objectives and trade-offs in cellular metabolism using machine learning and modeling. SCOOTI revealed trade-offs between stress protection and growth in embryonic development and explains how cells prioritize tasks during key transitions using multi-omics.

2025年1月7日 · SCOOTI can improve our understanding of cellular objectives, with implications for drug development, cellular reprogramming, and bioengineering. At its core, SCOOTI leverages machine learning algorithms to infer the objectives of individual cells by analyzing their metabolic and transcriptomic data.

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SCOOTI: Single-Cell Optimization Objective and Tradeoff Inference is a data-driven computational framework to identify cell-specific objectives and trade-offs from transcriptomics data. SCOOTI is able to. Formulate metabolic objectives for various cell types, such as cancer cell lines and embryos, in different conditions.

SCOOTI relies on the results of flux prediction generated by metabolic modeling methods to infer objectives. Therefore, to prepare context-specific flux prediction based on omics data, we need to identify up- and down-regulated genes from data. The method is mainly designed for integrating transcriptomics and proteomics.

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