The Journey to Scientific Greatness: Garry Nolan’s Story

Garry Nolan is an innovator and a Stanford professor in the Department of Pathology. He boasts over 350 published research papers and 50 U.S. patents, earning him recognition as one of the top 25 inventors at Stanford University.

In an interview with EpochTV’s “Bay Area Innovators,” Nolan shares insights into some of his inventions and the factors contributing to his achievements.

“Many have told me that my ideas won’t succeed. My usual response is, ‘If you can’t envision the future I see, I won’t argue with you endlessly. I’ll head back to the lab and demonstrate it,’” Nolan explained.

He completed his doctorate under Leonard Herzenberg and conducted postdoctoral work with Nobel laureate David Baltimore.

One of his notable inventions is a 293T cell retroviral production system designed for gene therapy, facilitating the delivery of genes to produce proteins or other RNA.

“It’s a retroviral vector capable of transporting genes to the targeted cells. Simultaneously, it deactivates the virus’s harmful aspects, then reconfigures it to serve as a vehicle—like a car—that carries our desired cargo,” Nolan elaborated.

Nolan noted that the retroviral vector systems were initially created by Richard Mulligan, and it took three months to engineer the retrovirus.

While working with other researchers in Baltimore’s lab, where Baltimore received a Nobel Prize for his work on retroviruses, another postdoc, Warren Pear, faced challenges in creating a retroviral vector as Mulligan had.

“I realized that if we rapidly introduced the DNA into this cell line, we could achieve a 60 percent transfection rate for producing the retrovirus. That would significantly speed up the process. Warren and I collaborated to develop the first of these cell lines,” Nolan recounted.

However, they soon discovered that these lines were unstable and lost their ability to produce retrovirus.

Once Nolan secured his position at Stanford and established his own lab, he developed a stable cell line. Working together, he and Pear revitalized the cells and named them the Phoenix lines.

“The term ‘Phoenix’ refers to the ancient Egyptian myth of rising from the ashes. The concept is that the DNA, having been transformed, can regenerate into a retrovirus, coming back to life and serving as a gene delivery mechanism,” Nolan explained.

The popularity of the Phoenix lines soared, leading to their distribution to thousands of laboratories worldwide.

“As we started exploring gene delivery methods for gene therapy aimed at treating genetic defects in humans, this system became essential for developing retroviruses for human applications,” he said. “Researchers have utilized it for delivering genes to the brain, the retina, and various other locations.”

Nolan emphasized that this DNA modification process only alters the DNA without affecting future generations.

His lab now focuses on developing updated or more effective methods for data production and tackling challenging questions.

“My goal is to provide so much data that individuals feel immersed in an ocean of opportunities. It’s disheartening to see people engage in slower methodologies, so I aim to equip them not just with tools but also with the analytical processes,” he stated.

Apart from his research, Nolan has initiated several biotech enterprises, with two actively trading on NASDAQ.

Recently, he has begun exploring artificial intelligence to better interpret large datasets. He and his team launched another company, revealing that they could integrate raw data into large language models. They structured the data to formulate a systematic algorithm for generating new data for subsequent algorithms in the programming framework.

“We essentially engineered a scientist in a box. We train the large language model to function like a scientist, think critically, and thus, when provided with data, it generates insights. It’s comparable to a highly capable graduate student or professor; although it isn’t flawless, we strive to mitigate errors,” Nolan explained.

He mentioned that the model can deduce what a user seeks from just a few concise phrases. For instance, framing the correct question can prompt it to propose a hypothesis and outline the necessary experiments.

While some argue this could encourage laziness among students, Nolan contends that motivated students will always find meaningful work, asserting, “If they don’t, perhaps they don’t belong in science.”

“What frustrates me most is witnessing lost opportunities,” Nolan lamented. “I often advise students, ‘If you’re confident in your correct approach and can visualize the pathway to success clearly, don’t allow anyone to dissuade or demoralize you. Simply pursue it.’”