Russian Clinical Research Center of Gerontology, Pirogov Russian National Research Medical University, Ministry of Healthcare of the Russian Federation, 129226 Moscow, Russia.
Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, 354340 Sochi, Russia.
Int J Mol Sci. 2024 Nov 2;25(21):11782. doi: 10.3390/ijms252111782.
Ferroptosis is a regulated cell death process characterized by iron ion catalysis and reactive oxygen species, leading to lipid peroxidation. This mechanism plays a crucial role in age-related diseases, including cancer and cardiovascular and neurological disorders. To better mimic iron-induced cell death, predict the effects of various elements, and identify drugs capable of regulating ferroptosis, it is essential to develop precise models of this process. Such drugs can be tested on cellular models. Systems biology offers a powerful approach to studying biological processes through modeling, which involves accumulating and analyzing comprehensive research data. Once a model is created, it allows for examining the system's response to various stimuli. Our goal is to develop a modular framework for ferroptosis, enabling the prediction and screening of compounds with geroprotective and antiferroptotic effects. For modeling and analysis, we utilized BioUML (Biological Universal Modeling Language), which supports key standards in systems biology, modular and visual modeling, rapid simulation, parameter estimation, and a variety of numerical methods. This combination fulfills the requirements for modeling complex biological systems. The integrated modular model was validated on diverse datasets, including original experimental data. This framework encompasses essential molecular genetic processes such as the Fenton reaction, iron metabolism, lipid synthesis, and the antioxidant system. We identified structural relationships between molecular agents within each module and compared them to our proposed system for regulating the initiation and progression of ferroptosis. Our research highlights that no current models comprehensively cover all regulatory mechanisms of ferroptosis. By integrating data on ferroptosis modules into an integrated modular model, we can enhance our understanding of its mechanisms and assist in the discovery of new treatment targets for age-related diseases. A computational model of ferroptosis was developed based on a modular modeling approach and included 73 differential equations and 93 species.
铁死亡是一种受铁离子催化和活性氧物质影响，导致脂质过氧化的调节性细胞死亡过程。该机制在与年龄相关的疾病中发挥着关键作用，包括癌症以及心血管和神经退行性疾病。为了更好地模拟铁诱导的细胞死亡、预测各种元素的作用并鉴定能够调节铁死亡的药物，开发这种过程的精确模型是至关重要的。可以在细胞模型上测试这些药物。系统生物学提供了一种通过建模来研究生物学过程的强大方法，建模涉及积累和分析综合研究数据。一旦创建了模型，就可以检查系统对各种刺激的反应。我们的目标是开发一个铁死亡的模块化框架，以预测和筛选具有抗衰老和抗铁死亡作用的化合物。对于建模和分析，我们使用了 BioUML（生物通用建模语言），它支持系统生物学中的关键标准、模块化和可视化建模、快速模拟、参数估计和多种数值方法。这种组合满足了建模复杂生物系统的要求。集成的模块化模型在各种数据集上进行了验证，包括原始实验数据。该框架包含了基本的分子遗传过程，如 Fenton 反应、铁代谢、脂质合成和抗氧化系统。我们确定了每个模块内分子剂之间的结构关系，并将其与我们提出的调节铁死亡起始和进展的系统进行了比较。我们的研究表明，目前没有任何模型全面涵盖铁死亡的所有调节机制。通过将铁死亡模块的数据集成到一个集成的模块化模型中，我们可以加深对其机制的理解，并有助于发现与年龄相关的疾病的新治疗靶点。基于模块化建模方法开发了铁死亡的计算模型，其中包括 73 个微分方程和 93 个物种。
