Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements.

信号网络的预测模型对于理解细胞群异质性和设计合理的疾病干预措施至关重要。然而，由于细胞群之间生化参数的广泛变异性，使用计算模型来预测信号动力学的异质性通常具有挑战性。在这里，我们描述了一个基于最大熵的框架，用于推断信号网络动态的异质性（MERIDIAN）。 MERIDIAN 估计信号网络参数的联合概率分布，该分布与实验测量的生化物种的细胞间变异性一致。我们应用开发的方法来研究 EGFR/Akt 信号网络中的反应异质性。我们的分析表明，很大一部分细胞在 EGF 刺激数小时后表现出高磷酸化 Akt (pAkt) 水平。我们的研究结果还表明，具有高 EGFR 水平的细胞主要构成具有高 pAkt 活性的细胞亚群。我们还讨论了如何扩展 MERIDIAN 以适应各种实验测量。