Sommariva Sara, Berra Silvia, Biddau Giorgia, Caviglia Giacomo, Benvenuto Federico, Piana Michele
Methods for Image and Data Analysis Group, Dipartimento di Matematica, Università di Genova, Genova, Italy.
Life Science Computational Laboratory, Ospedale Policlinico San Martino IRCCS, Genova, Italy.
Front Syst Biol. 2023 Aug 23;3:1207898. doi: 10.3389/fsysb.2023.1207898. eCollection 2023.
Chemical reaction networks (CRNs) are powerful tools for describing the complex nature of cancer's onset, progression, and therapy. The main reason for their effectiveness is in the fact that these networks can be rather naturally encoded as a dynamical system whose asymptotic solution mimics the proteins' concentration profile at equilibrium. This paper relies on a complex CRN previously designed for modeling colorectal cells in their G1-S transition phase and presents a mathematical method to investigate global and local effects triggered on the network by partial and complete mutations occurring mainly in its mitogen-activated protein kinase (MAPK) pathway. Further, this same approach allowed the modeling and dosage of a multi-target therapeutic intervention that utilizes MAPK as its molecular target. Overall the results shown in this paper demonstrate how the proposed approach can be exploited as a tool for the in-silico comparison and evaluation of different targeted therapies. Future effort will be devoted to refine the model so to incorporate more biologically sound partial mutations and drug combinations.
化学反应网络（CRNs）是描述癌症发生、发展和治疗复杂本质的有力工具。其有效性的主要原因在于，这些网络可以相当自然地编码为一个动态系统，其渐近解模拟了平衡状态下蛋白质的浓度分布。本文基于先前设计的用于模拟处于G1-S转变阶段的结肠直肠细胞的复杂CRN，并提出了一种数学方法，以研究主要在其丝裂原活化蛋白激酶（MAPK）途径中发生的部分和完全突变对网络触发的全局和局部影响。此外，同样的方法还能对以MAPK为分子靶点的多靶点治疗干预进行建模和剂量计算。总体而言，本文所示结果证明了所提出的方法如何能够用作不同靶向治疗的计算机模拟比较和评估工具。未来的工作将致力于完善模型，以便纳入更多生物学上合理的部分突变和药物组合。
