08-31-2023 06:00 AM
The hunt for new cures and treatments is at the leading edge of life sciences research. HPE GreenLake for File Storage delivers the next-gen file storage needed to accelerate that search. Learn how.
–By David Yu, HPE Storage Product Marketing
In recent decades, experts in life sciences and healthcare have made significant progress in their search to improve human health and quality of life. The effort spans everything from new medical devices, treatments, and vaccines to cures for diseases such as diabetes, leukemia, cancer, and Alzheimer’s.
Key research areas in the hunt for exciting breakthroughs are genomic sequencing and cryogenic electron microscopy (Cryo-EM). These two fields provide a leading-edge representation of current efforts in life sciences and healthcare. The research is driven by new artificial intelligence (AI) capabilities and powerful new compute and data storage architectures, with modern file storage playing a key role. Let’s take a closer look at genomic sequencing and Cryo-EM.
Sequencing fundamental genetic information
Genomic sequencing takes a lot of lab work using special instruments and requires significant compute and data storage resources. However, it holds the promise of insights into diseases at the genetic level (including pre-dispositions to certain disorders) and specialized treatments based on a patient’s unique genetic profile.
One example: targeted cancer treatments that use drugs specifically to attack cancer cells. In this process, genomic tests identify cancerous cells with mutated genes. Clinicians then prescribe an appropriate drug to target and attack those cancer cells by attaching to certain molecular cell structures or blocking their function. This is often an iterative process, as cancer cells tend to continue to mutate and grow. Of course, the ultimate goal is not just targeted treatment but to find a cure, and genomic sequencing research also plays an important part in that effort.
Modeling the deep structures of life
Cryo-M allows researchers to see and study cells, viruses, ribosomes, and protein structures at the molecular level using an electron microscope on samples that are frozen at cryogenic temperatures. With this technique, scientists have been able to learn a great deal about proteins and the ribosomes that produce them.
Leveraging Cryo-EM, life sciences researchers build 3-D models from thousands to millions of high-resolution images and video files. To do so, they run compute- and data-intensive tasks with Fourier transforms for image processing and motion correction for video files. The models hold the potential to improve medical diagnosis, aid drug discovery, and find explanations for the side effects and ineffectiveness of certain existing drugs.
The challenge of massive amounts of file data
As mentioned, genomic sequencing and Cryo-EM are representative of the applications at the new frontier of life sciences and healthcare. In the search for cures and personalized treatments, these technologies leverage AI capabilities and hold much promise. However, they also present a challenge because they generate so much file data.
The human genome has 3 billion nucleotide base pairs in DNA, and figuring out the correct sequence of a single human genome is quite complex. The techniques used involve AI and machine learning (ML) workflows through data pipelines that first ingest and then output huge amounts of data — sorting, analyzing, interpreting, and inferencing genomic information. (We outlined the workflows and data pipelines of AI applications in a previous blog on AI.)
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