Large-Scale Compute Cluster for the Institute for Computational Medicine

描述（由申请人提供）：此共享设备补助金要求资金用于购买一台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）的教师、学员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。生物系统研究的目的是利用实验和建模相结合的方法来理解心源性猝死（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“计算机化”设计的最佳方法能够通过植入式心律转复除颤器（ICDs）对心脏施加冲击来终止危及生命的心律失常。计算解剖学（CA）中的ICM研究旨在开发算法，用于发现大脑解剖结构和功能的变化，以及心脏结构/功能和运动的变化，从而预测大脑或心脏疾病的发展。通过分析获得的三维图像体积来测量正常和疾病人群的结构、功能和/或运动

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.