Spring, 2025
Lourdes Peña-Castillo is looking to understand bacteria to find ways to strategically manipulate them.
“Bacteria or, more generally, microbes are everywhere,” says Peña-Castillo, who is jointly appointed as a professor in the departments of computer science and biology at Memorial University. “They are in the house, they are in the soil, they are in the environment and they interact with everything. We know that some bacteria cause diseases, but they are a minority. All other bacteria are beneficial for plants, animals and for us.”
Given that, Peña-Castillo’s research, which she describes as being computational microbiology, applies machine learning to understand patterns in the genome of bacteria that signal to them how to “turn on” or express their genes.
“Right now, if we want to treat a disease, we basically take antibiotics and kill every single bacterium, the good ones along with the ones causing the disease,” says Peña-Castillo, who did her PhD in Computer Science in Germany and her postdoctoral work at the University of Toronto. “With my research, we are understanding in more detail every single bacterium, and then potentially we could actually either modify the gene expression of that bacterium or create treatments specifically designed for that bacterium. Instead of killing everything, let's try to just control a specific part of a bacterium.”
She refers to this work as a foundational endeavour in “smart biotechnology,” noting that in industrial processes, for example, they sometimes want bacteria to help to create more of a certain substance.
Peña-Castillo says she couldn’t do her job without ACENET.
“In my lab, we work with collections of sequencing data,” she says. “Each raw uncompressed sequencing file can be tens of gigabytes (GBs). As each dataset can have several of these files, it can quickly add up to hundreds of GBs in disk space. Add to that the fact that the software used to process these data can easily require tens of GBs of random access memory (RAM), often at least 50, and most laptops only have 8 to 16 GBs of RAM, and you can see that ACENET is not only necessary but indispensable.”
She says her team could run a single experiment in a high-end computer, but in many cases, it runs dozens of experiments to optimize its models.
“Using supercomputers allows us to run these experiments in parallel,” she says, adding that in one recent project her team combined more than 20 different datasets in an effort to train its models.
“ACENET enables us to do all the computational analysis that we do,” she says. “Most of my students run their calculations on ACENET, so it basically allows us to run all the experiments and analysis in an efficient way. Without ACENET, I would have to buy a lot of very expensive computers to do my job.”
