Mertins Susan D
Department of Science, Mount St. Mary's University, Emmitsburg, MD, United States.
Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Limited Liability Company (LLC), Frederick, MD, United States.
Front Oncol. 2022 Jan 21;11:805592. doi: 10.3389/fonc.2021.805592. eCollection 2021.
Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer and molecular targets in the Machine Learning setting.
描述生化反应的细胞功能计算动态常微分方程（ODE）模型已经创建了几十年，但规模较小。尽管如此，它们在描述和预测行为方面一直非常有效。例如，在包含正反馈和负反馈回路的丝裂原活化蛋白激酶（MAPK）信号传导中，振荡的磷酸化细胞外信号调节激酶（phospho-ERK）水平得到了预测和证实。这些模型通常具有局限性，不适合当今常见的大型数据集。但重要的是，ODE模型描述了代表细胞的充分混合系统中的反应网络，并且可以用确定性求解的常微分方程进行模拟。在某些情况下，能够考虑有噪声反应网络的随机解也能改善预测。如今，动态ODE模型很少涵盖整个细胞，尽管可能预计大型基因组、转录组和蛋白质组数据集的上传可能会产生全细胞模型。本文提出将模拟动态ODE模型的输出与组学数据相结合，以便在机器学习环境中发现癌症中的生物标志物和分子靶点。
