Large-Scale Compute Cluster for the Institute for Computational Medicine

描述(申请人提供)：这项共享设备赠款申请资金，以购买一台256双四节点、大内存集群计算机。这台计算机将成为约翰·霍普金斯大学计算医学研究所(ICM)的教职员工、实习生和工作人员使用的主要共享计算资源。这台计算机将在三个主要领域支持与疾病相关的研究：a)生物系统建模；b)计算解剖学；c)数学生物信息学。生物系统的研究旨在通过实验和建模相结合的方法来了解心源性猝死(SCD；西方世界的主要死亡原因)的机制。这项工作为SCD的分子和细胞机制提供了新的见解，并使使用植入式心律转复除颤器(ICD)对心脏施加电击来终止危及生命的心律失常的最佳方法成为可能。ICM在计算解剖学(CA)方面的研究旨在开发算法，以发现大脑的解剖和功能变化，以及心脏结构/功能和运动的变化，这些变化可以预测大脑或心脏病的发展。通过分析获取的三维图像体积，在正常人群和疾病人群中测量结构、功能和/或运动 来自使用磁共振成像或计算机断层扫描的患者。CA算法随后被用来发现解剖结构、功能和/或运动的变化，以高精度区分正常和疾病人群。这些方法用于对发育中的大脑或心脏病进行早期诊断，以便进行治疗干预。主要应用领域是发现预示疾病早期发病的特征，如精神分裂症、阿尔茨海默病类型的痴呆、缺血性与特发性扩张型心肌病以及SCD的风险。数学生物信息学的研究旨在开发新的计算算法，这些算法对患者特定的多尺度数据(例如，单核苷酸多态、转录水平、蛋白质表达水平、成像数据和临床数据)进行操作，以发现准确、敏感和特定的预测疾病开始、阶段、风险和治疗方法的生物标志物。这些计算方法的应用范围很广，包括发现癌症生物标记物和早期预测SCD的风险，以便进行ICD放置治疗。要实现这些目标中的每一个都需要开发和应用计算机模型和算法 计算密集的。目前ICM的计算资源不再是最先进的，研究进展正在放缓。公共卫生评论：授予所要求的仪器将极大地加速脑部疾病、心脏病和癌症的早期诊断和治疗的计算模型和算法的开发和应用。

DESCRIPTION (provided by applicant): This shared equipment grant requests funds to acquire a 256 dual-quad node, large memory cluster computer. This computer will become the major shared computing resource used by faculty, trainees, and staff of the Institute for Computational Medicine (ICM) at Johns Hopkins University. This computer will support disease-related research in three major areas: a) modeling of biological systems; b) computational anatomy; and c) mathematical bioinformatics. Research in biological systems is directed at understanding the mechanisms of Sudden Cardiac Death (SCD; the major cause of death in the western world) using combined experimental and modeling approaches. This work is providing novel insights into the molecular and cellular mechanisms of SCD, and is enabling the "in-silico" design of optimal approaches for terminating life-threatening arrhythmias using shocks applied to the heart by implantable cardioverter defibrillators (ICDs). ICM research in computational anatomy (CA) is directed at developing algorithms for discovering changes in the anatomy and function of the brain, as well as changes in the structure/function and motion of the heart, that predictive developing brain or heart disease. Structure, function, and/or motion is measured in both normal and disease populations by analyzing three-dimensional image volumes acquired from patients using magnetic resonance imaging or computed tomography. CA algorithms are then used to discover changes in anatomical structure, function, and/or motion that distinguish normal and diseased populations with high accuracy. These methods are used for the early diagnosis of developing brain or heart disease so that therapeutic interventions can be made. Major application areas are the discovery of features that signal early onset of diseases such as schizophrenia, dementia of the Alzheimer's type, ischemic versus idiopathic dilated cardiomyopathy, and risk of SCD. Research in mathematical bioinformatics is directed at developing novel computational algorithms that operate on patient-specific multi-scale data (e.g., data on single nucleotide polymorphisms, transcript levels, protein expression levels, imaging data, and clinical data) to discover biomarkers for accurate, sensitive, and specific prediction of disease onset, stage, risk and therapeutic approach. Applications of these computational methods are broad, including discovery of cancer biomarkers and early prediction of risk for SCD so that ICD placement therapy may be performed. Achieving each of these goals requires the development and application of computer models and algorithms that are very computationally intensive. The present computing resources of the ICM are no longer state of the art, and research progress is being slowed. PUBLIC HEALTH REVELANCE: Award of the requested instrument will dramatically accelerate the development and application of computational models and algorithms for the early diagnosis and treatment of brain disease, heart disease, and cancer.