週期性機械拉伸在不同組織細胞及拉伸強度，可用來模擬多種疾病的體外細胞模式。但要瞭解生物力學下這些拉伸所造成的機轉調控路徑，必須要進行大規模的實驗，花費許多時間、金錢與人力，才能找到基因與蛋白質生物訊息傳遞，且無法理解整個生物訊息路徑。為了充分利用有限的資源來建立整個生物力學所造成的生物訊息路徑，本研究希望透過結合實驗生物學與系統生物學的方法，將建立常見的生物力學模式之數據資料並整合公用資料庫與大規模實驗的文獻資料，得到各種生物力學下生物訊息路徑，藉此建立不同組織與疾病之系統性交互網路及其重要的控制模組，如此便能用來解析與生物力學相關的疾病機轉，與預測可能存在的基因蛋白質交互網路架構，藉此系統性生物力學數據資料庫，找到具有潛力的標的基因或蛋白成為藥物開發標的，且此系統資料庫將提升拉伸硬體設備之產品價值。 ;Cyclic mechanical stretch in tissues engineering with different tensilestrength play a crucial role in development of in vitro human diseasemodels. However, to understand the mechanics and pathways by cyclicmechanical stretch, we must conduct large-scale experiments, spend a lotof time, money and manpower in order to find the biological signaltransduction. In order to gain a whole picture of biomechanics influenceunder limited resources, this study combined the experimentalbiotechnology and system biology methods to establish biomechanicalgenomics and proteomics data. We also integrated public genomics dateand large-scale experimental literature to systematically catalogue allmolecules and their interaction network (including protein–proteininteraction, signaling transduction and transcription-regulatorynetworks). The systematic interactive network of different tissues anddiseases can predict biomechanical-related mechanisms of disease andidentify potential drug targets for drug discovery. This systemdatabase will enhance the value of the stretching equipment.
