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. ? ? ?
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