AI was integral to creating Moderna's mRNA vaccine against COVID.
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But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
In 2013, the Human Brain Project set out to build a realistic computer model of the brain over ten years. Now, experts are reflecting on HBP's achievements with an eye toward the future.
The Human Brain Project
Scholars have found that the project did help advance neuroscience more than some detractors initially expected, specifically in the area of brain simulations and virtual models. Using an interdisciplinary approach of combining technology, such as AI and digital simulations, with neuroscience, the HBP worked to gain a deeper understanding of the human brain’s complicated structure and functions, which in some cases led to novel treatments for brain disorders. Lastly, through online platforms, the HBP spearheaded a previously unmatched level of global neuroscience collaborations.
Simulating a human brain stirs up controversy
Right from the start, the project was plagued with controversy and condemnation. One of its prominent critics was Yves Fregnac, a professor in cognitive science at the Polytechnic Institute of Paris and research director at the French National Centre for Scientific Research. Fregnac argued in numerous articles that the HBP was overfunded based on proposals with unrealistic goals. “This new way of over-selling scientific targets, deeply aligned with what modern society expects from mega-sciences in the broad sense (big investment, big return), has been observed on several occasions in different scientific sub-fields,” he wrote in one of his articles, “before invading the field of brain sciences and neuromarketing.”
"A human brain model can simulate an experiment a million times for many different conditions, but the actual human experiment can be performed only once or a few times," said Viktor Jirsa, a professor at Aix-Marseille University.
Responding to such critiques, the HBP worked to restructure the effort in its early days with new leadership, organization, and goals that were more flexible and attainable. “The HBP got a more versatile, pluralistic approach,” said Viktor Jirsa, a professor at Aix-Marseille University and one of the HBP lead scientists. He believes that these changes fixed at least some of HBP’s issues. “The project has been on a very productive and scientifically fruitful course since then.”
After restructuring, the HBP became a European hub on brain research, with hundreds of scientists joining its growing network. The HBP created projects focused on various brain topics, from consciousness to neurodegenerative diseases. HBP scientists worked on complex subjects, such as mapping out the brain, combining neuroscience and robotics, and experimenting with neuromorphic computing, a computational technique inspired by the human brain structure and function—to name just a few.
Simulations advance knowledge and treatment options
In 2013, it seemed that bringing neuroscience into a digital age would be farfetched, but research within the HBP has made this achievable. The virtual maps and simulations various HBP teams create through brain imaging data make it easier for neuroscientists to understand brain developments and functions. The teams publish these models on the HBP’s EBRAINS online platform—one of the first to offer access to such data to neuroscientists worldwide via an open-source online site. “This digital infrastructure is backed by high-performance computers, with large datasets and various computational tools,” said Lucy Xiaolu Wang, an assistant professor in the Resource Economics Department at the University of Massachusetts Amherst, who studies the economics of the HBP. That means it can be used in place of many different types of human experimentation.
Jirsa’s team is one of many within the project that works on virtual brain models and brain simulations. Compiling patient data, Jirsa and his team can create digital simulations of different brain activities—and repeat these experiments many times, which isn’t often possible in surgeries on real brains. “A human brain model can simulate an experiment a million times for many different conditions,” Jirsa explained, “but the actual human experiment can be performed only once or a few times.” Using simulations also saves scientists and doctors time and money when looking at ways to diagnose and treat patients with brain disorders.
Compiling patient data, scientists can create digital simulations of different brain activities—and repeat these experiments many times.
The Human Brain Project
Simulations can help scientists get a full picture that otherwise is unattainable. “Another benefit is data completion,” added Jirsa, “in which incomplete data can be complemented by the model. In clinical settings, we can often measure only certain brain areas, but when linked to the brain model, we can enlarge the range of accessible brain regions and make better diagnostic predictions.”
With time, Jirsa’s team was able to move into patient-specific simulations. “We advanced from generic brain models to the ability to use a specific patient’s brain data, from measurements like MRI and others, to create individualized predictive models and simulations,” Jirsa explained. He and his team are working on this personalization technique to treat patients with epilepsy. According to the World Health Organization, about 50 million people worldwide suffer from epilepsy, a disorder that causes recurring seizures. While some epilepsy causes are known others remain an enigma, and many are hard to treat. For some patients whose epilepsy doesn’t respond to medications, removing part of the brain where seizures occur may be the only option. Understanding where in the patients’ brains seizures arise can give scientists a better idea of how to treat them and whether to use surgery versus medications.
“We apply such personalized models…to precisely identify where in a patient’s brain seizures emerge,” Jirsa explained. “This guides individual surgery decisions for patients for which surgery is the only treatment option.” He credits the HBP for the opportunity to develop this novel approach. “The personalization of our epilepsy models was only made possible by the Human Brain Project, in which all the necessary tools have been developed. Without the HBP, the technology would not be in clinical trials today.”
Personalized simulations can significantly advance treatments, predict the outcome of specific medical procedures and optimize them before actually treating patients. Jirsa is watching this happen firsthand in his ongoing research. “Our technology for creating personalized brain models is now used in a large clinical trial for epilepsy, funded by the French state, where we collaborate with clinicians in hospitals,” he explained. “We have also founded a spinoff company called VB Tech (Virtual Brain Technologies) to commercialize our personalized brain model technology and make it available to all patients.”
The Human Brain Project created a level of interconnectedness within the neuroscience research community that never existed before—a network not unlike the brain’s own.
Other experts believe it’s too soon to tell whether brain simulations could change epilepsy treatments. “The life cycle of developing treatments applicable to patients often runs over a decade,” Wang stated. “It is still too early to draw a clear link between HBP’s various project areas with patient care.” However, she admits that some studies built on the HBP-collected knowledge are already showing promise. “Researchers have used neuroscientific atlases and computational tools to develop activity-specific stimulation programs that enabled paraplegic patients to move again in a small-size clinical trial,” Wang said. Another intriguing study looked at simulations of Alzheimer’s in the brain to understand how it evolves over time.
Some challenges remain hard to overcome even with computer simulations. “The major challenge has always been the parameter explosion, which means that many different model parameters can lead to the same result,” Jirsa explained. An example of this parameter explosion could be two different types of neurodegenerative conditions, such as Parkinson’s and Huntington’s diseases. Both afflict the same area of the brain, the basal ganglia, which can affect movement, but are caused by two different underlying mechanisms. “We face the same situation in the living brain, in which a large range of diverse mechanisms can produce the same behavior,” Jirsa said. The simulations still have to overcome the same challenge.
Understanding where in the patients’ brains seizures arise can give scientists a better idea of how to treat them and whether to use surgery versus medications.
The Human Brain Project
A network not unlike the brain’s own
Though the HBP will be closing this year, its legacy continues in various studies, spin-off companies, and its online platform, EBRAINS. “The HBP is one of the earliest brain initiatives in the world, and the 10-year long-term goal has united many researchers to collaborate on brain sciences with advanced computational tools,” Wang said. “Beyond the many research articles and projects collaborated on during the HBP, the online neuroscience research infrastructure EBRAINS will be left as a legacy even after the project ends.”
Those who worked within the HBP see the end of this project as the next step in neuroscience research. “Neuroscience has come closer to very meaningful applications through the systematic link with new digital technologies and collaborative work,” Jirsa stated. “In that way, the project really had a pioneering role.” It also created a level of interconnectedness within the neuroscience research community that never existed before—a network not unlike the brain’s own. “Interconnectedness is an important advance and prerequisite for progress,” Jirsa said. “The neuroscience community has in the past been rather fragmented and this has dramatically changed in recent years thanks to the Human Brain Project.”
According to its website, by 2023 HBP’s network counted over 500 scientists from over 123 institutions and 16 different countries, creating one of the largest multi-national research groups in the world. Even though the project hasn’t produced the in-silico brain as Markram envisioned it, the HBP created a communal mind with immense potential. “It has challenged us to think beyond the boundaries of our own laboratories,” Jirsa said, “and enabled us to go much further together than we could have ever conceived going by ourselves.”
