Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
WHY THIS MATTERS IN BRIEF
The global pandemic has caused unimaginable havoc but as Sars-Cov-2 mutates there are fears vaccines won’t be effective so researchers are trying to stay two steps ahead of the virus to prevent that from happening.
Coronavirus recently became the first virus ever to be completely digitised, and as new variants of the coronavirus continue to spring up like wildfires across the planet researchers have been frantically trying to determine which new strains might outwit our brand new vaccines as well as some of our new, revolutionary ones too. Artificial Intelligence (AI) may be able to help. In a paper published Friday in the journal Science, researchers at MIT described a machine learning algorithm that can predict which mutations pose the biggest threat to the world’s fledgling immunity. The tool could be used to quickly narrow down which mutations are most likely to evade the immune systems of people who have been vaccinated or previously infected. Researchers can then test suspected strains in the lab and update vaccines accordingly.
“This is a real-time companion to vaccine development,” says Bryan Bryson, a biological engineer at MIT and co-author of the paper. “What we can do with our model right now is a lot faster than what you can do in the lab.”
The tool comes at a crucial moment in the COVID-19 pandemic. Millions of doses of vaccines developed against SARS-CoV-2, the coronavirus that causes COVID-19, are finally rolling out to the public with just over three percent of the US population has been vaccinated. These vaccines were designed to train our immune systems to recognise a particular strain of the coronavirus. But the more the virus mutates, the greater the chance that those already vaccinated and those who were previously infected could enjoy less immunity to the new strains. This harrowing process is called viral escape. Coronavirus mutations that achieve escape would then send vaccine makers scrambling to update their vaccines in a high-stakes game of catch-up.
In recent weeks, new viral variants out of the United Kingdom, South Africa, California, and other regions have begun to spread across the globe. These intractable variants seem to be more contagious than their ancestors, though gratefully not more deadly, and multiple experts have said publicly that our current vaccines should still work against the new strains.
That said though the virus will continue to mutate because that’s what viruses do. Here is where Bryson and his colleagues say their algorithm could help vaccine makers keep up with the game. It would, they say, reduce the laborious experimental techniques currently used to monitor such mutations.
“This is a tool that tells you when to investigate,” says Bonnie Berger, a computer scientist at MIT and co-author of the paper. “As new strains come along, we can flag which ones are worth investigating for escape potential.”
A number of AI based tools aided the early development of COVID-19 vaccines. For example, AI helped researchers identify which segments of a virus’s genetic code are most likely to change, and how some mutations might affect its physical structure. MIT’s new machine learning algorithm broadens AI’s repertoire by applying it to viral escape.
The group’s model was originally developed for machine language comprehension. The algorithm is designed to look for both grammar, syntax, and meaning, semantics. Using those same two principles, the researchers creatively adapted it to perceive changes to viral genetic code.
They call their process Constrained Semantic Change Search (CSCS). As the model learns about the coronavirus genome, it begins to learn what kinds of changes to that genome could be consequential. From that, it then generates a short list of suspicious strains to test in the lab.
To test the strains, researchers would first generate a pseudovirus carrying the suspect mutations identified by the computational model. They would then subject the pseudovirus to antibodies gathered from people previously vaccinated or infected with COVID-19. If the antibodies don’t neutraliSe the virus, that suggests that the new strain is capable of evading the immune system and that updated vaccines are needed. Then it’d be back to the algorithms to look for more suspicious variants.
“It’s like a loop” between the computers and the wet labs, says Bryson. “You just kind of go back and forth and try to understand the pandemic in real time.”
The researchers trained their model on just under 1,000 genetic sequences of the SARS-CoV-2 spike protein, plus another 3,000 spike sequences from other types of coronaviruses, such as those that cause the common cold – the spike is what the virus uses to enter human cells, and also what our immune systems will recognise. Those thousands of examples taught the model the rules governing how amino acids must be sequenced in coronaviruses.
“The nice thing about language models is they can learn the rules directly from a large training set,” says Brian Hie, a PhD candidate in Berger’s group, and co-author of the paper. “That’s why we wanted to use this model in the biological setting, where we don’t know the rules of which amino acids can go together.”
As an experiment, the MIT researchers fed some of the new variants into their algorithm and found that both the UK and South African strains scored “quite high” in terms of their probability of escape. However, they did not rank as high as an escape mutant generated in laboratory experiments, says Berger.
Predicting when a high score will translate into an actual escape from a human immune system is still beyond the model’s capability though, says Hie. In the long term, Hie says he hopes to keep working with the model so that it can predict future mutations in viruses that haven’t yet occurred.
“That’s a moonshot kind of goal for this line of research: Vaccinating against future forms of the virus,” he says.