Prediction of long-term outcomes from short-term measurements remains a fundamental challenge. Quantitative assessment of signaling dynamics, and the resulting transcriptomic and proteomic responses, has yielded fundamental insights into cellular outcomes. However, the utility of these measurements is limited by their short timescale (hours to days), while the consequences of these events frequently unfold over longer timescales. Here, we discuss the predictive power of static and dynamic measurements, drawing examples from fields that have harnessed the predictive capabilities of such measurements. We then explore potential approaches to close this timescale gap using complementary measurements and computational approaches, focusing on the example of dynamic measurements of signaling factors and their impacts on cellular outcomes.

从短期测量预测长期结果仍然是一个根本挑战。对信号动力学以及由此产生的转录组和蛋白质组反应的定量评估已经对细胞结果产生了基本的见解。然而，这些测量的效用因其较短的时间尺度（几小时到几天）而受到限制，而这些事件的后果往往会在较长的时间尺度内显现出来。在这里，我们讨论静态和动态测量的预测能力，并从利用此类测量的预测能力的领域中抽取示例。然后，我们探索使用互补测量和计算方法来缩小这一时间尺度差距的潜在方法，重点关注信号因子的动态测量及其对细胞结果的影响的示例。