Computation and Biostatistics Core

计算核心：生物统计分析和网络建模 核心联席董事：大卫·杰特森博士和亚历山大·霍夫曼博士 摘要 该申请提出了三个研究项目和三个核心共享资源，由调查人员领导，地址为 加州大学洛杉矶分校(UCLA)，围绕研究先天适应性的主题 肝移植缺血再灌注损伤中的免疫调节作用项目I和II的重点是调制 同基因和异基因小鼠肝移植后的IRI免疫反应； 然而，项目III致力于阐明IRI在人类原位肝移植后的免疫调节中的作用。 核心C提供适当分析所需的数据管理、生物统计和计算专业知识 以及由项目生成的数据的网络建模。统计和机械建模将允许 集成不同的数据集以制定预测，并可能通过比较产生新的发现和见解 这三种类型的肝移植的数据集。 我们将在以下方面提供服务： ·数据质量控制、监测和管理。 ·用于高通量分子数据的数据预处理的中央基础设施。 ·生物统计设计，对研究进行统计监测，以确保结果的可解释性。 ·对各种数据集进行统计分析和解释。 ·为这些项目中的统计问题制定生物统计方法。 ·建立分子网络模型，以解释转录组数据并提供基于知识的维度 减少 ·对免疫细胞动力学进行建模，以解释免疫表型数据集，并将分子和 细胞尺度数据和分析。 ·协助准备手稿。

Computational Core: Biostatistical Analysis and Network Modeling Core Co-Directors: David Gjertson PhD and Alexander Hoffmann PhD SUMMARY This application proposes three research Projects and three Cores for shared resources led by investigators at the University of California Los Angeles (UCLA), centered around the theme of investigating innate-adaptive immunoregulation in liver transplant ischemia/reperfusion Injury (IRI). Projects I and II focus on modulation of IRI immune responses following hepatic grafting in syngeneic and allogeneic murine models, respectively; whereas, Project III concentrates on elucidating the role of IRI on immunoregulation following human OLT. Core C provides data management, biostatistical and computational expertise required for the proper analysis and network modeling of data generated by Projects. Statistical and mechanistic modeling will allow the integration of diverse datasets to develop predictions, and may generate new findings and insights by comparing the datasets from the three types of liver transplants. Services will be provided in the following areas: • Data quality control, monitoring and management. • A central infrastructure for data preprocessing of high throughput molecular data. • Biostatistical design, statistical monitoring of studies to ensure interpretability of results. • Statistical analysis and interpretation of diverse datasets. • Development of biostatistical methodology for statistical problems in these projects. • Modeling molecular networks to interpret transcriptome data and provide knowledge-based dimensionality reduction • Modeling the immune-cell dynamics to interpret immune-phenotyping datasets and integrate molecular and cellular scale data and analyses. • Assistance with manuscript preparation.