Zhao Ziwei, Liang Jingxuan, Zhang Xianbao, Li Wenyan, Wang Yun
Information Engineering Research Center for Traditional Chinese Medicines, Beijing University of Chinese Medicine, Beijing, 100029, China.
Heliyon. 2024 Sep 6;10(18):e37578. doi: 10.1016/j.heliyon.2024.e37578. eCollection 2024 Sep 30.
Understanding the state transitions in biological systems and identifying critical steady states are crucial for investigating disease development and discovering key therapeutic targets. To advance the study of state transitions in specific biological entities, we proposed the Ternary Entity State Inference System (T-ESIS). T-ESIS builds upon the Entity State Inference System by providing richer information on entity states, where states can take values of 0, 1, or 1/2, representing activation, inhibition, and normal states, respectively. This method infers state transition pathways based on interaction relationships and visualizes them through the Entity State Network. Furthermore, the cyclic structures within the Entity State Network capture positive and negative feedback loops, providing a topological foundation for the formation of steady states. To demonstrate the applicability of T-ESIS, entity states were modeled, and attractor analysis was conducted in non-small cell lung cancer (NSCLC) networks. Our analysis provided valuable insights into targeted therapy for NSCLC, highlighting the potential of T-ESIS in uncovering therapeutic targets and understanding disease mechanisms. Moreover, the proposed T-ESIS framework facilitated the inference of entity state transitions and the analysis of steady states in biological systems, offering a novel approach for studying the dynamic principles of these systems. This ternary dynamic modeling approach not only deepened our understanding of biological networks but also provided a methodological reference for future research in the field.
了解生物系统中的状态转换并识别关键稳态对于研究疾病发展和发现关键治疗靶点至关重要。为了推进对特定生物实体中状态转换的研究，我们提出了三元实体状态推理系统（T-ESIS）。T-ESIS在实体状态推理系统的基础上构建，通过提供关于实体状态的更丰富信息，其中状态可以取0、1或1/2的值，分别代表激活、抑制和正常状态。该方法基于相互作用关系推断状态转换途径，并通过实体状态网络将其可视化。此外，实体状态网络中的循环结构捕获正反馈和负反馈回路，为稳态的形成提供拓扑基础。为了证明T-ESIS的适用性，对实体状态进行了建模，并在非小细胞肺癌（NSCLC）网络中进行了吸引子分析。我们的分析为NSCLC的靶向治疗提供了有价值的见解，突出了T-ESIS在揭示治疗靶点和理解疾病机制方面的潜力。此外，所提出的T-ESIS框架促进了生物系统中实体状态转换的推断和稳态分析，为研究这些系统的动态原理提供了一种新方法。这种三元动态建模方法不仅加深了我们对生物网络的理解，还为该领域未来的研究提供了方法学参考。
