Imagine trying to understand a complex machine with millions of tiny parts, each interacting in intricate ways. That's the challenge scientists face when studying the human body at the cellular level. Single-cell RNA sequencing (scRNA-seq) offers a glimpse into the activity of individual cells, but making sense of this massive amount of data is like finding a needle in a haystack. Now, a new method called sc-OTGM is making the search easier. This innovative approach uses a clever trick: it models cellular changes as the movement of mass between different cell states. Imagine each cell state as a mountain, and changes in gene expression as the flow of water between these mountains. sc-OTGM calculates the most efficient way to move this "water," revealing how cells transition from one state to another, especially in response to disruptions like gene knockdowns. This method is particularly useful when dealing with limited or noisy data, a common problem in single-cell research. Unlike larger, more complex AI models, sc-OTGM is streamlined and efficient, making it a powerful tool for researchers. In tests using CRISPRi, a gene editing technique, sc-OTGM accurately pinpointed the genes that were targeted and predicted how their expression changed. This ability to predict the ripple effects of gene editing is crucial for understanding disease and developing new treatments. While sc-OTGM offers a powerful new lens for studying single cells, it also highlights the ongoing challenge of capturing the full complexity of biological systems. Future research could explore incorporating nonlinear models to further refine these predictions and unlock even more secrets hidden within our cells.