R24 Funded Resources - Computation & Modeling

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Center for Integrative Biomedical Computing (CIBC)
University of Utah

R24 Grant Number: R24GM136986
Principal Investigator: Rob S. MacLeod, Ph.D., Christopher R. Johnson, Ph.D., Ross T. Whitaker, Ph.D.
The CIBC Legacy Resource provides open-source software tools for biomedical image-based modeling, biomedical simulation and estimation, and the visualization of biomedical data.

National Resource for Mechanistic Modeling of Cellular Systems
University of Connecticut, School of Medicine

R24 Grant Number: R24GM137787
Principal Investigator: Leslie M. Loew
The National Resource for Mechanistic Modeling of Cellular Systems will provide computational modeling, enabled by COPASI and VCell technologies, will offer deep insights into mechanisms by which cells function and the cellular basis of disease. Projects by our user base focused on cancer, neurological disorders, diabetes, kidney disease, cardiology and aging, for example, all utilize mathematical models developed with the aid of VCell and COPASI. This Resource was previously supported by the BTRR Program (P41GM103313).

Phenix R24 Resource
Lawrence Berkeley National Laboratory

R24 Grant Number: R24GM141254
Principal Investigator: Paul David Adams
Phenix is a software suite that uses reduced data from X-ray diffraction, electron diffraction, neutron diffraction or cryo-EM 3D reconstructions to determine macromolecular structures. The Phenix Resource supports the continued maintenance and optimization of the Phenix code base, the improvements in program usability and integration with other community software resources, and the outreach, training and user support to help grow the community of Phenix users. This Resource was supported by P01GM063210.

Seattle Quant: A Resource for the Skyline Software Ecosystem
University of Washington

R24 Grant Number: R24GM141156
Principal Investigator: Michael MacCoss
Quantitative mass spectrometry measurements offer a promising alternative to immunological based assays that are the standard for quantitative protein measurements in clinical and basic research laboratories. Critical to these experiments is our software, Skyline and the associated ecosystem of tools, which have been developed to handle the generation of instrument methods and the subsequent analysis of the resulting data. This Resource was previously supported by the BTRR Program (P41GM103533).

The Genomic Enzymology Web-Based Resource
University of Illinois at Urbana-Champaign

R24 Grant Number: R24GM141196
Principal Investigator: John Gerlt
The Genomic Enzymology Web-Based Resource integrates three tools to enable the discovery of novel proteins and metabolic pathways. The tool pipeline is comprised of three analysis steps: (1) generation of sequence similarity networks (SSNs) enabling the semi-automated reconstruction of high-quality protein families built around any protein sequence, (2) parallel exploration of the genome neighborhood of a protein family across a diverse set of input genomes to discover functionally linked gene products to infer novel enzymatic functions and metabolic pathways, and (3) determination of metagenome abundance of clusters in the SSNs to discover important targets for functional assignment. This resource was previously supported by U54GM093342 and P01GM118303.


The Resource for Macromolecular Modeling and Visualization
University of Illinois at Urbana-Champaign

R24 Grant Number: R24GM145965
Principal Investigator: Emad Tajkhorshid
The Resource for Macromolecular Modeling and Visualization serves as a unique center for disseminating state-of-the-art software tools enabling structural biologists and molecular biophysicists to model, simulate, analyze, and visualize biomolecular systems. It leverages mature technologies and implements them into accessible software for efficient use by a wide range of molecular scientists working on a broad spectrum of biomedical research problems. The main mission of the Resource is to ensure the utility and availability of its widely used software programs (e.g., VMD and NAMD) and associated technologies to its large user base on diverse hardware platforms, to maintain user access to key features and capabilities in the programs, to ensure continued compatibility with community force fields, databases, and software tools, and to support the users in employing the programs through extensive training and user-support programs. This Resource was previously supported by the BTRR Program (P41-GM104601).


The Resource for Structure-based Computational Drug Discovery and Design (RSD3)
The Scripps Research Institute

R24 Grant Number: R24GM145962
Principal Investigator: Stefano Forli
The Resource for Structure-based Computational Drug Discovery and Design (RSD3) disseminates the AutoDock suite, which is a collection of docking software tools with a large user base for drug discovery, design, and optimization. RSD3 will maintain and modernize this software to adapt to evolving hardware platforms and operating systems, to keep the software up to date and relevant by incorporating the latest algorithmic developments, and to support its large user community through various training and outreach programs.​

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