In this episode of Shared Everything, Dr. Subramanian Kartik traces the tectonic shifts in computational infrastructure driven by the rise of life sciences as a data-intensive domain. From the advent of long-read nanopore sequencing to the seismic influence of AlphaFold and cryo-EM, Kartik outlines a field no longer tethered to traditional HPC assumptions. As genomic data explodes and microscopes spill out petabytes, he argues, the industry must abandon legacy parallel file systems in favor of architectures purpose-built for random I/O, GPU-rich workflows, and relentless uptime. Storage isn’t a peripheral—it’s the platform. What’s emerging, Kartik suggests, is a model-native science built on new languages of biology: proteins, base pairs, and the in silico folding of life itself.
00:00 – 02:00
Intro and Kartik’s background in physics and industry evolution; early days of personalized medicine and genomic research.
02:00 – 04:30
Breakdown of three main advances in life sciences: genomics, gene editing (CRISPR), and long-read sequencing technologies like Oxford Nanopore and PacBio.
04:30 – 06:30
Deep technical dive into nanopore sequencing: how it works, why it matters, and why it requires GPU acceleration.
06:30 – 08:30
The computational bottleneck: memory mapping, random I/O, why short-read sequencers are now limiting, and why SSDs are necessary.
08:30 – 10:00
Parallel file systems break under modern life sciences loads; shift toward storage architectures that can handle random I/O at scale.
10:00 – 12:30
How AlphaFold reshaped structural biology and compute expectations; protein folding as a graph neural network challenge.
12:30 – 15:00
LLMs in pharma, managing clinical trial data, and the rise of mixed, hybrid workloads in research computing.
15:00 – 17:00
Microscopy at scale (cryo-EM, light sheet imaging) and the data tsunami—petabytes per microscope, per year.
17:00 – 19:30
Shifting away from HPC-era assumptions: new workloads, new storage expectations, and lessons from vendors like Oxford Nanopore.
19:30 – 20:36
What’s next: generative AI models trained on molecular sequences and protein structures; a vision of disease-free future.