Improving red blood cell transfusion through systems biology

描述（由申请人提供）：红细胞（RBC）储存在批准的添加剂溶液中，会经历一系列代谢和物理化学变化，称为“储存损伤”，降低了旧输注红细胞单位的有效性和安全性。尽管储存损伤的后果正在慢慢得到充分的记录，但开发红细胞输血质量和安全新技术进展缓慢的一个主要原因是缺乏对储存过程中代谢下降的全面了解。人们一直对利用高通量代谢物分析来全面了解红细胞代谢下降感兴趣，但复杂数据集的数据分析一直是一项艰巨的挑战。该计划将开发第一个强大的计算平台，包括统计分析、代谢网络的系统生物学和数据驱动的动力学模型，以在完整的网络环境中充分解释和分析RBC代谢物谱。该计划将利用时间过程全球，定量代谢物分析，以跟踪细胞内和细胞外红细胞代谢物在标准储存条件下。基于预期的生物功效、物理化学因素、化合物成本和潜在的监管障碍，将通过计算分析来定量预测最佳的添加剂解决方案策略，从而获得深入的代谢理解。预计的添加剂将在第二阶段进行实验测试。

DESCRIPTION (provided by applicant): Red blood cells (RBC) stored in approved additive solutions undergo a set of metabolic and physicochemical changes referred to as 'storage lesions' reducing the efficacy and safety of older transfused RBC units. Though the consequences of the storage lesion are slowly becoming well documented, a major reason for delayed progress in developing new technologies for quality and safety of RBC transfusion is the lack of global understanding of metabolic decline during storage. There has been interest to utilize high-throughput metabolite profiling for global understanding of RBC metabolic decline but data analysis of complex datasets has been a daunting challenge. The proposed program will develop the first, robust computational platform involving statistical analysis, systems biology of metabolic networks, and data-driven kinetic models to fully interpret and analyze RBC metabolite-profiles in a complete network context. The program will utilize time- course global, quantitative metabolite profiling to track intracellular and extracellular RBC metabolites under standard storage conditions. Deep metabolic understanding will be obtained through computational analysis to quantitatively predict optimal additive solution strategies based on expected biological efficacy, physico- chemical considerations, compound cost, and potential regulatory hurdles. Predicted additives will be chosen for experimental testing in Phase II.