Intramuscular fat (IMF) marbling is a key determinant of beef quality, yet predicting how breed-specific gene expression translates into tissue-scale fat patterning remains a major challenge. Using a small public transcriptomic dataset (n = 3 per breed), this study presents a proof-of-concept omics-to-tissue modeling framework that converts RNA-seq data into biophysically interpretable parameters governing intramuscular adipogenesis. Using transcriptomic profiles from GSE161967 (Japanese Black Wagyu versus Chinese Red Steppes), we derived composite indices capturing the adipogenic commitment (φ) and lipid droplet capacity (ψ) from curated gene modules. These indices were mapped via calibrated linear functions to a Cellular Potts Model (CPM), parameterizing the fibro-adipogenic progenitor (FAP) differentiation probability, lipogenesis rate, adipocyte cohesion, and progenitor abundance. The gene-derived parameters placed Wagyu in a high-adipogenic regime (pF→Abase = 0.65; klipogenesis = 0.12), while Chinese Red Steppes resided in a low-adipogenic regime (0.25; 0.04). The CPM simulations revealed a sharp, predictive threshold at pF→Abase ≈ 0.55, below which IMF remained negligible and above which stable adipocyte clusters and 8–9% IMF emerged. Without post hoc tuning, the gene-derived parameters correctly predicted robust marbling in Wagyu and a lean phenotype in Chinese Red Steppes. A sensitivity analysis identified the adipogenic commitment as the primary control parameter, with lipogenesis acting as an amplifier. Together, these results demonstrate that transcriptomic measurements can quantitatively predict emergent marbling phenotypes through a small set of interpretable biophysical parameters, establishing a generalizable framework for forecasting complex tissue traits from omics data.
Cabrera et al. (Mon,) studied this question.