Integrated Mathematical Modeling of Epigenetic-Wnt-Notch Crosstalk in Mesenchymal Stem Cell Differentiation: A Stochastic Framework with Gillespie Simulations for Precision Regenerative, Oncotherapeutic, Cardiovascular, and Hepatobiliary Applications

The intricate bidirectional crosstalk between Wnt and Notch signaling pathways, modulated by epigenetic mechanisms, plays a crucial role in regulating mesenchymal stem cell (MSC) differentiation and maintaining tissue homeostasis. This study introduces the Shibah Epigenetic Feedback Model (SEFM 2.0), an advanced stochastic ordinary differential equation (SODE) framework integrated with the Gillespie stochastic simulation algorithm (SSA) and Bayesian inference to quantitatively characterize this crosstalk in MSCs. The model is calibrated using empirical transcriptomic data from NCBI GEO dataset GSE104548, which profiles rat bone marrow-derived MSCs undergoing neural transdifferentiation induced by epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), and insulin-like growth factor-1 (IGF-1), identifying 3,252 differentially expressed genes. Supplemented by ENCODE epigenomic annotations and GTEx expression quantitative trait loci (eQTLs), SEFM 2.0 exhibits high predictive accuracy. Stochastic simulations reveal microenvironmental noise impacts, projecting a 12--18% enhancement in regenerative potential via CRISPR-dCas9-mediated epigenetic interventions. Therapeutic implications extend to targeting glioma stem cells, ameliorating ischemic cardiomyopathy, and treating hepatobiliary conditions such as fibrosis and cirrhosis. Reproducible Python code, accompanied by 95% posterior credible intervals, ensures model falsifiability and verifiability. This integrative approach combines molecular techniques (e.g., RNA-seq, CRISPR-Cas9) with rigorous computational modeling, providing actionable strategies for neurodegeneration, osteoarthritis, oncogenesis, cardiovascular disorders, and liver pathologies, in alignment with Molecular Biology Reports standards.Wnt与Notch信号通路之间的复杂双向串扰受表观遗传机制调控，在间充质干细胞（mesenchymal stem cell, MSC）分化调控与组织稳态维持中发挥关键作用。本研究提出Shibah表观遗传反馈模型（Shibah Epigenetic Feedback Model, SEFM 2.0），这是一种整合吉莱斯皮随机模拟算法（Gillespie stochastic simulation algorithm, SSA）与贝叶斯推断的高级随机常微分方程（stochastic ordinary differential equation, SODE）框架，用于定量刻画间充质干细胞中的这类信号串扰。该模型依托NCBI GEO数据集GSE104548中的实验转录组数据进行校准，该数据集对经表皮生长因子（epidermal growth factor, EGF）、碱性成纤维细胞生长因子（basic fibroblast growth factor, bFGF）及胰岛素样生长因子-1（insulin-like growth factor-1, IGF-1）诱导发生神经转分化的大鼠骨髓源性间充质干细胞进行转录组分析，共鉴定出3252个差异表达基因。结合ENCODE表观基因组注释与GTEx表达数量性状位点（expression quantitative trait loci, eQTLs）数据后，SEFM 2.0展现出优异的预测精度。随机模拟结果揭示了微环境噪声的影响，并预测通过CRISPR-dCas9介导的表观遗传干预可使再生潜能提升12%~18%。该模型的治疗应用前景可延伸至靶向神经胶质瘤干细胞、改善缺血性心肌病以及治疗纤维化、肝硬化等肝胆疾病。本研究附带95%后验可信区间的可复现Python代码，确保了模型的可证伪性与可验证性。这种将RNA-seq、CRISPR-Cas9等分子技术与严谨的计算建模相结合的整合研究方法，契合《Molecular Biology Reports》的出版标准，可为神经退行性疾病、骨关节炎、肿瘤发生、心血管疾病及肝脏病变等多种病症提供可行的干预策略。