Computational and Systems Biology
School of Medicine

Computational and Systems Biology

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Driving Innovation

How We Innovate

The Department of Computational and Systems Biology harnesses the power of diverse resources to drive innovation in our field. Browse our resources to learn more about how technology influences our scientific exploration.

Intranet 

Are you a student, staff or faculty member in our department? You can find departmental policies, information and helpful links at our SharePoint site. 

Using the Cluster

Advances in computing power and information storage have played a major role in the emergence of the “-omics” era of science. These advances have enabled scientists to break new ground in the realms of genome assembly, analysis, alignment, computational evolutionary biology, protein structural alignment, interaction network analyses, small RNA species identification and characterization, and many other areas in genomics and proteomics.

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Departmental Hardware

The Department of Computational Biology uses a large number of computers to conduct its research in an efficient and effective manner. These computers include high-end workstations in the offices and a number of rack-mounted Linux clusters at the NOC. The clusters are for running complex simulations, models, and computations that take a long time to complete or can run in parallel across nodes.

Learn more about our Hardware

Cluster  Nodes *   CPU cores total  Mem total (GB)  Storage total (TB)  GPUcards 
CPU  53  1,624  9,489  306  NA 
GPU  30  988  6,969  168  180 
Totals  83  2,612  16,458  475  180 

*Nodes include compute nodes only. The (3) Login and (15) Storage servers are extra.
** Storage includes only available local workspace (scratch). 

GPU Computing: 

In addition to the CPU cluster nodes, we added rackmount servers to house several Graphics Processing Units “GPUs”, which can be used for speeding up scientific computations.  In our GPU cluster are 180 various GPUs. Currently available GPUs consist of 12 nVidia Titan X cards, 18 nVidia GTX 1080ti cards, 62 nVidia RTX 2080Ti cards, 11 nVidia Titan X (Pascal) cards, 4 Titan V cards, 4 Titan RTX cards, 4 RTX 3060 cards, 8 RTX A4000 cards, 16 RTX A4500 cards, 4 Tesla V100 cards, 5 A100 (40GB) cards and 32 nVidia L40 cards. Each server runs Linux and uses nVidia CUDA software development kit (SDK).  Software such as NAMD and Amber already support running on GPU hardware. 

Application Servers: 

The department has a VMware vSphere cluster for running multiple application servers on 3 ESXi hosts configured for N+1 High-Availability with a vCenter server managing all three.  The ESXi hosts each consist of dual 10-core Xeon Gold with 96GB of ram.  The Virtual Machines are stored on a fully redundant vSAN storage array configured with both SSD and SAS drives for reliability and increased performance when needed. The virtualized environment provides for high-availability with no single point of failure. 

The department also uses a number of Windows and Linux servers for sharing files, printers, and other domain functions.  These servers also host several research software tools including VMD, GNM, ANM, and several network accessible databases of biological research data. 

Storage: 

880TB full-redundant network attached storage (NAS) for cluster. 34TB redundant vSAN storage for VMware servers.  100+TB Linux NAS for backing up linux workstations over the network. 270TB Network backup server for cluster data. 

 

Web Applications
Studying the architecture, shape, and dynamics of biological macromolecules is paramount to understanding the basic mechanisms that drive the essential processes of all life. Macromolecules such as proteins and nucleic acids carry out most of the functions of a cell, and are able to perform these functions by adopting ensembles of structures under native state conditions. Structural biology is concerned with the driving forces and interactions that determine the three-dimensional shapes and dynamics of biomolecules. Moreover, by applying the fundamental principles of the physical sciences, we are beginning to establish sequence-structure-dynamics-function relationships that enable deeper levels of discoveries, and summon the possibility of de novo structural and functional predictions at the proteome level.
Browse our Applications

ORF Information App

Enables users to look at yeast-translated orfs and their coexpression relationships, including unannotated noncanonical orfs.

Pharmit

Interactive exploration of chemical space.

Software

Computational and theoretical approaches are revolutionizing Pharmacology and Drug Discovery. Predicting, modeling, and simulating potential therapeutic agents and their interactions with target molecules is a powerful new first step in the drug discovery process. This in silico approach streamlines the often laborious, expensive, and slow process of identifying and testing lead compounds for use as treatments. Combining these advances with high-throughput cellular- and systems-level pharmacological and poly-pharmacological approaches is profoundly impacting medical science.

Browse our Software

BioNetGen

BioNetGen is software designed for modular, structure-based modeling of biochemical reaction networks. It provides a simple, graph-based syntax that lets users build reaction models out of structured objects that can bind and undergo modification.

dNEMO

A tool for detection and tracking of diffraction limited spots from quantitative microscopy experiments. 

GNINA

A deep learning framework for molecular docking.

LI Detector

Enables sensitive colony-based screens regardless of the distribution of fitness effects; iRibo (PMID: 37164009 and PMID: 38217852) enables integrative detection of translated orfs at the genome scale.

popDMS

A method to quantify the functional effects of mutations in deep mutational scanning (DMS) / multiplexed assay of variant effect (MAVE) data sets.

Databases
Computational and theoretical approaches are revolutionizing Pharmacology and Drug Discovery. Predicting, modeling, and simulating potential therapeutic agents and their interactions with target molecules is a powerful new first step in the drug discovery process. This in silico approach streamlines the often laborious, expensive, and slow process of identifying and testing lead compounds for use as treatments. Combining these advances with high-throughput cellular- and systems-level pharmacological and poly-pharmacological approaches is profoundly impacting medical science.
Workshops

Computational and theoretical approaches are revolutionizing Pharmacology and Drug Discovery. Predicting, modeling, and simulating potential therapeutic agents and their interactions with target molecules is a powerful new first step in the drug discovery process. This in silico approach streamlines the often laborious, expensive, and slow process of identifying and testing lead compounds for use as treatments. Combining these advances with high-throughput cellular- and systems-level pharmacological and poly-pharmacological approaches is profoundly impacting medical science.

Secure VPN Tunnel

Access to department resources, including the cluster, requires use of the GlobalProtect VPN software.

For more information and installation instructions, use this link.