bioinformatics Archives - Sanford Burnham Prebys
Institute News

Scripting their own futures

AuthorGreg Calhoun
Date

August 8, 2024

At Sanford Burnham Prebys Graduate School of Biomedical Sciences, students embrace computational methods to enhance their research careers

Although not every scientist-in-training will need to be an ace programmer, the next generation of scientists will need to take advantage of advances in artificial intelligence (AI) and computing that are shaping biomedical research. Scientists who understand how to best process, store, access and employ algorithms to analyze ever-increasing amounts of information will help lead the data revolution rather than follow in its wake.

“I think the way to do biology is very different from just a decade or so ago,” says Kevin Yip, PhD, a professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys and the director of the Bioinformatics Shared Resource. “Looking back, I could not have imagined playing much of a role as a data scientist, and now I see that my peers and I are at the core of the whole discovery process.”

In 2017, bioinformatics experts suggested in Genome Biology that graduate education programs should focus on teaching computational biology to all learners rather than just those with a special interest in programming or data science. The authors noted that the changing nature of the life sciences required researchers to respond in kind. Teams of scientists must be able to formulate algorithms to keep pace and detect new discoveries obscured within oceans of data too vast to parse with prior methods.

“I think most people now would agree that data science and the use of computational methods—AI included—are indispensable in biology,” says Yip. “To use these approaches to the greatest effect, computational biologists and bench laboratory scientists need to be trained to speak a common language.”

Kevin Yip, PhD

Kevin Yip, PhD, is a professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys.

When Yip joined Sanford Burnham Prebys in 2022, he was tasked with directing a course on computational biology for the Institute’s Graduate School of Biomedical Sciences.

“We believe that the new generation of graduate students needs to have the ability to understand what algorithms are and how they work, rather than just treating those tools as black boxes,” says Yip. “They may not be able to invent new algorithms right out of the course, but they’ll be better equipped to participate in collaborative projects.”

Andrei Osterman, PhD

Andrei Osterman, PhD, is a professor in the Immunity and Pathogenesis
Program at Sanford Burnham Prebys.

Yip’s work developing the course has been well-received by graduate students based on their evaluations of the class.  

“I loved the computational biology course,” says Katya Marchetti, a second-year PhD student in the lab of  Karen Ocorr, PhD, and the recipient of an Association for Women in Science scholarship.

“It was so helpful to learn skills that I could immediately see incorporating into my own research. I’m so glad I had this course. I know for a fact that I will need this knowledge and experience to be successful in whatever comes after my PhD. The people who have these skills objectively do better in postdoctoral fellowships or in the biotechnology industry.”

Yip and his fellow faculty members in the graduate school see an opportunity to further expand their approach to computational biology and data science topics.

“In the current course, students learn to use computational methods to analyze transcriptomics data,” says Andrei Osterman, PhD, vice dean and associate dean of Curriculum for the Graduate School and a professor in the Immunity and Pathogenesis Program at Sanford Burnham Prebys. “This is very useful hands-on training, but not advanced enough for some students.”

“We are seeing students with a computer science background coming into our graduate program,” notes Yip. “We are thinking about adding a new elective course for students who want to go beyond what our current class is offering.”

Graduate education is quickly evolving at Sanford Burnham Prebys and throughout the biomedical research community to match the demands of an era defined by effectively integrating computation and biology.

“Mutual understanding among data scientists and biologists is very important for where research is heading,” says Yip. “We will keep improving our training to set our students up for success.”

Katya Marchetti

Katya Marchetti is a second-year PhD student at Sanford Burnham Prebys.


Programming in a Petri Dish, an 8-part series

How artificial intelligence, machine learning and emerging computational technologies are changing biomedical research and the future of health care

  • Part 1 – Using machines to personalize patient care. Artificial intelligence and other computational techniques are aiding scientists and physicians in their quest to prescribe or create treatments for individuals rather than populations.
  • Part 2 – Objective omics. Although the hypothesis is a core concept in science, unbiased omics methods may reduce attachments to incorrect hypotheses that can reduce impartiality and slow progress.
  • Part 3 – Coding clinic. Rapidly evolving computational tools may unlock vast archives of untapped clinical information—and help solve complex challenges confronting health care providers.
  • Part 4 – Scripting their own futures. At Sanford Burnham Prebys Graduate School of Biomedical Sciences, students embrace computational methods to enhance their research careers.
  • Part 5 – Dodging AI and computational biology dangers. Sanford Burnham Prebys scientists say that understanding the potential pitfalls of using AI and other computational tools to guide biomedical research helps maximize benefits while minimizing concerns.
  • Part 6 – Mapping the human body to better treat disease. Scientists synthesize supersized sets of biological and clinical data to make discoveries and find promising treatments.
  • Part 7 – Simulating science or science fiction? By harnessing artificial intelligence and modern computing, scientists are simulating more complex biological, clinical and public health phenomena to accelerate discovery.
  • Part 8 – Acceleration by automation. Increases in the scale and pace of research and drug discovery are being made possible by robotic automation of time-consuming tasks that must be repeated with exhaustive exactness.
Institute News

Three big questions for cutting-edge biologist Will Wang

AuthorMiles Martin
Date

January 26, 2023

Will Wang’s spatial omics approach to studying neuromuscular diseases is unique.

He works at the intersection of biology and computer science to study how complex systems of cells interact, specifically focusing on the connections between nerves, muscles, and the immune response and their role in neuromuscular diseases.

We sat down with Wang, who recently joined the Institute as an assistant professor, to discuss his work and how computer technology is shaping the landscape of biomedical research.

How is your team taking advantage of computer technology to study neuromuscular diseases?

No cell exists in isolation. All our cells are organized into complex tissues with different types of cells interacting with each other. We study what happens at these points of interaction, such as where nerves connect to muscle cells. Combining many different types of data such as single cell sequencing, spatial proteomics, and measures of cell-cell signaling helps us get a more holistic look at how interactions between cells determine tissue function, as well as how these interactions are disrupted in injury and disease. Artificial neural networks help us make sense of these different types of data by finding patterns and insights the human brain can’t see on its own. And because computers can learn from the vast modality of data that we gather, we can also use them to help predict how biological systems will behave in the lab. The process goes both ways – from biology to computers and from computers to biology. 

How will these technologies shape the future of biomedical research?

Biology and computer programming are two different languages. There are a lot of mathematicians and programmers who are great at coming up with solutions to process data, but biological questions can get lost in translation and it’s easy to miss the bigger picture. And pure biologists don’t necessarily understand the full scope of what computers can do for them. If we’re going to get the most out of this technology in biomedical research, we need people with enough expertise in both areas that they can bridge the gap, which is what our lab is trying to do. Over time we’re going to see more and more labs that combine traditional biological experiments and data analysis approaches with artificial intelligence and machine learning. 

Are there any potential risks to these new technologies?

Artifical intelligence is here to accelerate discovery. Mundane tasks and measurements that took me weeks to carry out as a graduate student can be automated to a matter of minutes. We can now find patterns in high dimensional images that the human brain can’t easily visualize. However, any kind of artificial intelligence comes with a certain amount of risk if people don’t understand when and how to use the tools. If you just take the absolute word of the algorithm, there will inevitably be times where it’s not correct. As scientists, we use artificial intelligence as a cutting-edge discovery tool, but need to validate the findings in terms of the biology. At the end of the day, it is us, scientists, who are here to drive the discovery process and design real life experiments to make sure our therapies are safe and efficacious. 

Institute News

Suds and science: a night of thinking and drinking

AuthorJessica Moore
Date

July 13, 2016

A brewery might seem like an odd place to be talking about science, but on a recent Monday evening, Jessica Rusert, PhD, a postdoc in the laboratory of Robert Wechsler-Reya, PhD, did exactly that. In front of an enthralled crowd at Stone Brewing in Liberty Station, she discussed how advances in genomics and bioinformatics are changing medicine. The evening was part of the Suds & Science series organized by the Reuben H. Fleet Science Center, which aims to bring science to the masses and give people the opportunity to discuss a hot topic with a scientist doing related research.

Rusert’s topic was precision medicine, also called personalized medicine, which means treating patients as individuals instead of using what works for the majority. Rusert focused on cancer, where some personalized therapies, selected based on markers present in a patient’s tumor, are already available. She is an expert on this topic, as her research aims to find treatments for specific molecular subtypes of medulloblastoma, a devastating pediatric brain cancer.

There were a lot of questions about this future of medicine, including whether this more complex approach to healthcare would be affordable for everyone. But there was also an undercurrent of hope for the future—doctors are gaining the power to predict which treatment will work for each patient, and the recent presidential initiative will accelerate progress.

Rusert promoted the event earlier that day on the CW6 show San Diego Living, which you can watch here.

Suds & Science is held once a month at various establishments in San Diego.