The session aims to learn directly from smaller, mid-sized and under-resourced campuses on the research infrastructure capability needs of the institutions – data, supercomputers and/or other research equipment, software, and people. In addition, the
session will explore developing a skilled workforce to support advanced CI and research
and the potential for the Eastern Regional Network (ERN) to provide the broader community access to ACI expertise, resources, tools, events, guidance, data, and collaboration opportunities. The goal is to bring together the community thought leaders to identify challenges, opportunities, collaboration opportunities, and brainstorm on the ERN Implementation in collaboration with RENs to address the needs of smaller, mid-sized and under-resourced campuses. Results of the workshop will also be used for planning the 2021-2022 ERN BTR Working Group workshops.
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The vision of the ERN is to simplify and incentivize multi-campus collaborations and partnerships throughout the nation and beyond that advance the frontiers of research, pedagogy, and innovation. In order to do this successfully the ERN needs to consider current university policies as well as engage with university administrations in developing a policy strategy to help us bring the vision to reality. This session aims to learn from the ERN community the impact of federated and university policies and what considerations need to be taken into account as we create new policies and procedures designed to lower barriers that allow the ease of sharing knowledge, data, infrastructure, and people.

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Cryo-EM/ET is one of the research areas where gaining a deeper understanding of the workflows, research computing and data requirements, collaborations, and challenges will enable the ERN to have the broadest impact across multiple research disciplines, pedagogical approaches, senior level college and university administrators, and other organizations within the region and beyond. We estimate that the Cryo-EM/Cryo-ET community in the Northeast comprises nearly 50 centers serving more than 800 laboratories from Pennsylvania to Maine. Single Particle Reconstruction information produced by these centers is producing transformative insights. Given the cost and value of the instruments involved, fast turnaround and efficient use of resources is key. While all centers are well equipped to deliver images from prepared samples, processing and storage of these images can present significant and unnecessary obstacles, especially for labs that do not have easy access to computing resources and expertise. The Cryo-EM/Cryo-ET microscopy labs in the Northeast have formed a relatively tight knit community, allowing for free flow of information and experience, and reducing duplication of effort, and accelerating the adoption of new techniques. This session will explore possibilities for extending this collaboration to include the community of Research Computing and Networking organizations that serve these labs and the broader impacts.
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Materials Discovery is one of the research areas where gaining a deeper understanding of the workflows, research computing and data requirements, collaborations, and challenges will enable the ERN to have the broadest impact across multiple research disciplines, pedagogical approaches, senior level college and university administrators, and other organizations within the region and beyond. Researchers in materials discovery are realizing their traditional data-intensive HPC workflows are reaching the limits of original progress. For this reason, they are looking to new paradigms that include convergence of HPC and Machine Learning (ML) methodologies, algorithm development, and novel ways to access the data distributed across multiple institutions used in training systems as promising approaches to overcome the major computational performance limitations they are faced with. Exploratory conversations with Penn State, Rutgers, SUNY Buffalo, MIT, and others suggest that Materials Discovery offers an attractive testbed for advanced cyberinfrastructure of the sort the ERN can offer through future funding opportunities such as the Mid-Scale RI-1 program. As with Cryo-EM/Cryo-ET, this session will explore possibilities for extending collaborations to include other institutions as well as the community of Research Computing and Networking organizations.
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The Eastern Regional Network’s (ERN) Broadening the Reach Working Group is pleased to launch the first in a series of workshops in support of leveraging the cloud in research.

The virtual workshop is bringing together researchers, faculty, research computing and central IT professionals, as well as other institutional stakeholders, including students and administrators, to share information and facilitate discussions about successful implementation of the cloud for research from the researcher and institutional perspectives.

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This Institute for Quantitative Biomedicine Crash Course will present a broad overview of how Artificial Intelligence/Machine Learning (AI/ML) methods are being used for de novo protein structure prediction and provide hands-on experience with both AlphaFold2 and RoseTTAFold.

CASP14 revealed that AlphaFold2, developed by Google DeepMind, Inc., can predict threedimensional structures of small globular proteins with accuracies comparable to experimental methods. RoseTTAFold, developed at the University of Washington/Howard Hughes Medical Institute, approaches AlphaFold2 in terms of prediction accuracy while requiring fewer computational resources.

In this Crash Course, expert speakers will provide a solid foundation on the role of AI/ML in structural biology and showcase ongoing research efforts at Rutgers. During the hands-on tutorial, participants will learn how to utilize these new computational tools to compute structure models from amino acid sequences and download precomputed structure models from the AlphaFoldDB database. Local computing resources (Rutgers University Amarel Cluster) and access to Google Colab and the RoseTTAFold server will be made available during the hands-on session.
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