MIDA, Dipartimento di Matematica, Dipartimento di Eccellenza 2023-2027, Università di Genova, Genova, Italy.
IRCCS Ospedale Policlinico San Martino, LISCOMP, Genova, Italy.
Sci Rep. 2024 Jul 31;14(1):17706. doi: 10.1038/s41598-024-67862-5.
Chemical reaction networks are powerful tools for modeling cell signaling and its disruptions in diseases like cancer. Realistic chemical reaction networks involve hundreds of proteins and reactions, resulting in a model depending on a consistently large number of kinetic parameters. Since finely calibrating all the parameters would require an unrealistic amount of data, proper sensitivity analysis is required to identify a subset of parameters for which fine tuning is needed and thus provide a fundamental tool for the qualitative analysis of the network. We present a multidisciplinary approach for computing a set of synthetic sensitivity indices. These indices rank the kinetic parameters, based on the impact that errors in their values would have on the protein concentration profile at equilibrium. Our tests on a chemical reaction network devised for colorectal cells demonstrate the effectiveness of the considered sensitivity indices in different scenarios including in-silico drug dosage and novel therapeutic target discovery. The Matlab code for computing the synthetic sensitivity indices and the data concerning the network for colorectal cells are available at https://github.com/theMIDAgroup/CRN_sensitivity.
化学反应网络是用于模拟细胞信号及其在癌症等疾病中的紊乱的强大工具。现实的化学反应网络涉及数百种蛋白质和反应，导致模型取决于大量的动力学参数。由于精细调整所有参数需要不切实际的数据量，因此需要进行适当的敏感性分析，以确定需要精细调整的参数子集，并为网络的定性分析提供基本工具。我们提出了一种用于计算一组综合敏感性指数的多学科方法。这些指数根据其值的误差对平衡时蛋白质浓度分布的影响，对动力学参数进行排序。我们在为结直肠细胞设计的化学反应网络上的测试表明，所考虑的敏感性指数在不同情况下（包括虚拟药物剂量和新的治疗靶标发现）的有效性。用于计算综合敏感性指数的 Matlab 代码以及关于结直肠细胞的网络数据可在 https://github.com/theMIDAgroup/CRN_sensitivity 上获得。
