Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
Integrative Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA.
Bull Math Biol. 2019 Jun;81(6):1645-1664. doi: 10.1007/s11538-019-00587-z. Epub 2019 Feb 22.
Paracrine PDGF signaling is involved in many processes in the body, both normal and pathological, including embryonic development, angiogenesis, and wound healing as well as liver fibrosis, atherosclerosis, and cancers. We explored this seemingly dual (normal and pathological) role of PDGF mathematically by modeling the release of PDGF in brain tissue and then varying the dynamics of this release. Resulting simulations show that by varying the dynamics of a PDGF source, our model predicts three possible outcomes for PDGF-driven cellular recruitment and lesion growth: (1) localized, short duration of growth, (2) localized, chronic growth, and (3) widespread chronic growth. Further, our model predicts that the type of response is much more sensitive to the duration of PDGF exposure than the maximum level of that exposure. This suggests that extended duration of paracrine PDGF signal during otherwise normal processes could potentially lead to lesions having a phenotype consistent with pathologic conditions.
旁分泌 PDGF 信号参与体内许多正常和病理过程,包括胚胎发育、血管生成和伤口愈合以及肝纤维化、动脉粥样硬化和癌症。我们通过对脑组织中 PDGF 的释放进行建模,并改变这种释放的动态,从数学角度探讨了 PDGF 这种看似双重(正常和病理)的作用。结果表明,通过改变 PDGF 源的动态,我们的模型预测了 PDGF 驱动的细胞募集和病变生长的三种可能结果:(1)局部、短暂的生长;(2)局部、慢性生长;(3)广泛的慢性生长。此外,我们的模型还预测,与 PDGF 暴露的最大水平相比,反应类型对 PDGF 暴露的持续时间更为敏感。这表明,在正常过程中,旁分泌 PDGF 信号的持续时间延长可能导致具有病理状态表型的病变。