The capacity of small cerebral arteries (SCAs) to adapt to pressure fluctuations has a fundamental physiological role and appears to be relevant in different pathological conditions. Here, we present a new computational model for quantifying the link, and its contributors, between luminal pressure and vascular tone generation in SCAs. This is assembled by combining a chemical sub-model, representing pressure-induced smooth muscle cell (SMC) signalling, with a mechanical sub-model for the tone generation and its transduction at tissue level. The devised model can accurately reproduce the impact of luminal pressure on different cytoplasmic components involved in myogenic signalling, both in the control case and when combined with some specific pharmacological interventions. Furthermore, the model is also able to capture and predict experimentally recorded pressure-outer diameter relationships obtained for vessels under control conditions, both in a Ca\(^{2+}\)-free bath and under drug inhibition. The modularity of the proposed framework allows the integration of new components for the study of a broad range of processes involved in the vascular function.

大脑小动脉（SCAs）适应压力波动的能力具有基本的生理作用，并且似乎与不同的病理条件相关。在这里，我们提出了一种新的计算模型，用于量化 SCA 中管腔压力和血管张力产生之间的联系及其影响因素。它是通过将代表压力诱导的平滑肌细胞 (SMC) 信号传导的化学子模型与用于音调生成及其在组织水平上的转导的机械子模型相结合来组装的。设计的模型可以准确地再现管腔压力对参与肌源信号传导的不同细胞质成分的影响，无论是在对照情况下还是与一些特定的药理干预措施相结合时。此外，该模型还能够捕获和预测在控制条件下（在无 Ca \(^{2+}\)浴中和在药物抑制下）获得的血管实验记录的压力-外径关系。所提出框架的模块化允许集成新组件，以研究涉及血管功能的广泛过程。