Artificial Intelligence is already helping to grow some of the food we eat.
The glass-encased cabinet looks like a display meant to hold reasonably priced watches, or drugstore beauty creams shipped from France. But instead of this stagnant merchandise, each of its five shelves is overgrown with leaves — moss-soft pea sprouts, spikes of Lolla rosa lettuces, pale bok choy, dark kale, purple basil or red-veined sorrel or green wisps of dill. The glass structure isn’t a cabinet, but rather a “micro farm.”
The gadget is on display at the Richmond, Virginia headquarters of Babylon Micro-Farms, a company that aims to make indoor farming in the U.S. more accessible and sustainable. Babylon’s soilless hydroponic growing system, which feeds plants via nutrient-enriched water, allows chefs on cruise ships, cafeterias and elsewhere to provide home-grown produce to patrons, just seconds after it’s harvested. Currently, there are over 200 functioning systems, either sold or leased to customers, and more of them are on the way.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Recent advancements in engineering mean that the first preclinical trials for an artificial kidney could happen soon.
Still, the devices aren’t ready for testing in humans—yet. But recent advancements in engineering mean that the first preclinical trials for an artificial kidney could happen soon, according to Jonathan Himmelfarb, a nephrologist at the University of Washington.
“It would liberate people with kidney failure,” Himmelfarb says.
An engineering marvel
Compared to the heart or the brain, the kidney doesn’t get as much respect from the medical profession, but its job is far more complex. “It does hundreds of different things,” says UCLA’s Ira Kurtz.
Kurtz would know. He’s worked as a nephrologist for 37 years, devoting his career to helping those with kidney disease. While his colleagues in cardiology and endocrinology have seen major advances in the development of artificial hearts and insulin pumps, little has changed for patients on hemodialysis. The machines remain bulky and require large volumes of a liquid called dialysate to remove toxins from a patient’s blood, along with gallons of purified water. A kidney transplant is the next best thing to someone’s own, functioning organ, but with over 600,000 Americans on dialysis and only about 100,000 kidney transplants each year, most of those in kidney failure are stuck on dialysis.
Part of the lack of progress in artificial kidney design is the sheer complexity of the kidney’s job. Each of the 45 different cell types in the kidney do something different.
Part of the lack of progress in artificial kidney design is the sheer complexity of the kidney’s job. To build an artificial heart, Kurtz says, you basically need to engineer a pump. An artificial pancreas needs to balance blood sugar levels with insulin secretion. While neither of these tasks is simple, they are fairly straightforward. The kidney, on the other hand, does more than get rid of waste products like urea and other toxins. Each of the 45 different cell types in the kidney do something different, helping to regulate electrolytes like sodium, potassium, and phosphorous; maintaining blood pressure and water balance; guiding the body’s hormonal and inflammatory responses; and aiding in the formation of red blood cells.
There's been little progress for patients during Ira Kurtz's 37 years as a nephrologist. Artificial kidneys would change that.
UCLA
Dialysis primarily filters waste, and does so well enough to keep someone alive, but it isn’t a true artificial kidney because it doesn’t perform the kidney’s other jobs, according to Kurtz, such as sensing levels of toxins, wastes, and electrolytes in the blood. Due to the size and water requirements of existing dialysis machines, the equipment isn’t portable. Physicians write a prescription for a certain duration of dialysis and assess how well it’s working with semi-regular blood tests. The process of dialysis itself, however, is conducted blind. Doctors can’t tell how much dialysis a patient needs based on kidney values at the time of treatment, says Meera Harhay, a nephrologist at Drexel University in Philadelphia.
But it’s the impact of dialysis on their day-to-day lives that creates the most problems for patients. Only one-quarter of those on dialysis are able to remain employed (compared to 85% of similar-aged adults), and many report a low quality of life. Having more flexibility in life would make a major different to her patients, Harhay says.
“Almost half their week is taken up by the burden of their treatment. It really eats away at their freedom and their ability to do things that add value to their life,” she says.
Art imitates life
The challenge for artificial kidney designers was how to compress the kidney’s natural functions into a portable, wearable, or implantable device that wouldn’t need constant access to gallons of purified and sterilized water. The other universal challenge they faced was ensuring that any part of the artificial kidney that would come in contact with blood was kept germ-free to prevent infection.
As part of the 2021 KidneyX Prize, a partnership between the U.S. Department of Health and Human Services and the American Society of Nephrology, inventors were challenged to create prototypes for artificial kidneys. Himmelfarb’s team at the University of Washington’s Center for Dialysis Innovation won the prize by focusing on miniaturizing existing technologies to create a portable dialysis machine. The backpack sized AKTIV device (Ambulatory Kidney to Increase Vitality) will recycle dialysate in a closed loop system that removes urea from blood and uses light-based chemical reactions to convert the urea to nitrogen and carbon dioxide, which allows the dialysate to be recirculated.
Himmelfarb says that the AKTIV can be used when at home, work, or traveling, which will give users more flexibility and freedom. “If you had a 30-pound device that you could put in the overhead bins when traveling, you could go visit your grandkids,” he says.
Kurtz’s team at UCLA partnered with the U.S. Kidney Research Corporation and Arkansas University to develop a dialysate-free desktop device (about the size of a small printer) as the first phase of a progression that will he hopes will lead to something small and implantable. Part of the reason for the artificial kidney’s size, Kurtz says, is the number of functions his team are cramming into it. Not only will it filter urea from blood, but it will also use electricity to help regulate electrolyte levels in a process called electrodeionization. Kurtz emphasizes that these additional functions are what makes his design a true artificial kidney instead of just a small dialysis machine.
One version of an artificial kidney.
UCLA
“It doesn't have just a static function. It has a bank of sensors that measure chemicals in the blood and feeds that information back to the device,” Kurtz says.
Other startups are getting in on the game. Nephria Bio, a spinout from the South Korean-based EOFlow, is working to develop a wearable dialysis device, akin to an insulin pump, that uses miniature cartridges with nanomaterial filters to clean blood (Harhay is a scientific advisor to Nephria). Ian Welsford, Nephria’s co-founder and CTO, says that the device’s design means that it can also be used to treat acute kidney injuries in resource-limited settings. These potentials have garnered interest and investment in artificial kidneys from the U.S. Department of Defense.
For his part, Burton is most interested in an implantable device, as that would give him the most freedom. Even having a regular outpatient procedure to change batteries or filters would be a minor inconvenience to him.
“Being plugged into a machine, that’s not mimicking life,” he says.
This article was first published by Leaps.org on May 5, 2022.
New research focuses on methods that could change medicine-making worldwide. The scientists propose bursting cells open, removing their DNA and using the cellular gears inside to make therapies.
Rao and his team of collaborators, which spans multiple research institutions, believe they have a better approach that may change medicine-making worldwide. They suggest forgoing the concept of using living cells as medicine-producers. Instead, they propose breaking the cells and using the remaining cellular gears for assembling the therapeutic compounds. Instead of inserting the DNA into living cells, the team burst them open, and removed their DNA altogether. Yet, the residual molecular machinery of ribosomes, polymerases and other cogwheels still functioned the way it would in a cell. “Now if you drop your DNA drug-making instructions into that soup, this machinery starts making what you need,” Rao explains. “And because you're no longer worrying about living cells, it becomes much simpler and more efficient.” The collaborators detail their cell-free protein synthesis or CFPS method in their recent paper published in preprint BioAxiv.
While CFPS does not use living cells, it still needs the basic building blocks to assemble proteins from—such as amino acids, nucleotides and certain types of enzymes. These are regularly added into this “soup” to keep the molecular factory chugging. “We just mix everything in as a batch and we let it integrate,” says James Robert Swartz, professor of chemical engineering and bioengineering at Stanford University and co-author of the paper. “And we make sure that we provide enough oxygen.” Rao likens the process to making milk from milk powder.
For a variety of reasons—from the field’s general inertia to regulatory approval hurdles—the method hasn’t become mainstream. The pandemic rekindled interest in medicines that can be made quickly and easily, so it drew more attention to the technology.
The idea of a cell-free protein synthesis is older than one might think. Swartz first experimented with it around 1997, when he was a chemical engineer at Genentech. While working on engineering bacteria to make pharmaceuticals, he discovered that there was a limit to what E. coli cells, the workhorse darling of pharma, could do. For example, it couldn’t grow and properly fold some complex proteins. “We tried many genetic engineering approaches, many fermentation, development, and environmental control approaches,” Swartz recalls—to no avail.
“The organism had its own agenda,” he quips. “And because everything was happening within the organism, we just couldn't really change those conditions very easily. Some of them we couldn’t change at all—we didn’t have control.”
It was out of frustration with the defiant bacteria that a new idea took hold. Could the cells be opened instead, so that the protein-forming reactions could be influenced more easily? “Obviously, we’d lose the ability for them to reproduce,” Swartz says. But that also meant that they no longer needed to keep the cells alive and could focus on making the specific reactions happen. “We could take the catalysts, the enzymes, and the more complex catalysts and activate them, make them work together, much as they would in a living cell, but the way we wanted.”
In 1998, Swartz joined Stanford, and began perfecting the biochemistry of the cell-free method, identifying the reactions he wanted to foster and stopping those he didn’t want. He managed to make the idea work, but for a variety of reasons—from the field’s general inertia to regulatory approval hurdles—the method hasn’t become mainstream. The pandemic rekindled interest in medicines that can be made quickly and easily, so it drew more attention to the technology. For their BioArxiv paper, the team tested the method by growing a specific antiviral protein called griffithsin.
First identified by Barry O’Keefe at National Cancer Institute over a decade ago, griffithsin is an antiviral known to interfere with many viruses’ ability to enter cells—including HIV, SARS, SARS-CoV-2, MERS and others. Originally isolated from the red algae Griffithsia, it works differently from antibodies and antibody cocktails.
Most antiviral medicines tend to target the specific receptors that viruses use to gain entry to the cells they infect. For example, SARS-CoV-2 uses the infamous spike protein to latch onto the ACE2 receptor of mammalian cells. The antibodies or other antiviral molecules stick to the spike protein, shutting off its ability to cling onto the ACE2 receptors. Unfortunately, the spike proteins mutate very often, so the medicines lose their potency. On the contrary, griffithsin has the ability to cling to the different parts of viral shells called capsids—namely to the molecules of mannose, a type of sugar. That extra stuff, glued all around the capsid like dead weight, makes it impossible for the virus to squeeze into the cell.
“Every time we have a vaccine or an antibody against a specific SARS-CoV-2 strain, that strain then mutates and so you lose efficacy,” Rao explains. “But griffithsin molecules glom onto the viral capsid, so the capsid essentially becomes a sticky mess and can’t enter the cell.” Mannose molecules also don’t mutate as easily as viruses’ receptors, so griffithsin-based antivirals do not have to be constantly updated. And because mannose molecules are found on many viruses’ capsids, it makes griffithsin “a universal neutralizer,” Rao explains.
“When griffithsin was discovered, we recognized that it held a lot of promise as a potential antiviral agent,” O’Keefe says. In 2010, he published a paper about griffithsin efficacy in neutralizing viruses of the corona family—after the first SARS outbreak in the early 2000s, the scientific community was interested in such antivirals. Yet, griffithsin is still not available as an off-the-shelf product. So during the Covid pandemic, the team experimented with synthesizing griffithsin using the cell-free production method. They were able to generate potent griffithsin in less than 24 hours without having to grow living cells.
The antiviral protein isn't the only type of medicine that can be made cell-free. The proteins needed for vaccine production could also be made the same way. “Such portable, on-demand drug manufacturing platforms can produce antiviral proteins within hours, making them ideal for combating future pandemics,” Rao says. “We would be able to stop the pandemic before it spreads.”
Top: Describes the process used in the study. Bottom: Describes how the new medicines and vaccines could be made at the site of a future viral outbreak.
Image courtesy of Rao and team, sourced from An approach to rapid distributed manufacturing of broad spectrumanti-viral griffithsin using cell-free systems to mitigate pandemics.
Rao’s idea is to perfect the technology to the point that any hospital or pharmacy can load up the media containing molecular factories, mix up the required amino acids, nucleotides and enzymes, and harvest the meds within hours. That will allow making medicines onsite and on demand. “That would be a self-contained production unit, so that you could just ship the production wherever the pandemic is breaking out,” says Swartz.
These units and the meds they produce, will, of course, have to undergo rigorous testing. “The biggest hurdles will be validating these against conventional technology,” Rao says. The biotech industry is risk-averse and prefers the familiar methods. But if this approach works, it may go beyond emergency situations and revolutionize the medicine-making paradigm even outside hospitals and pharmacies. Rao hopes that someday the method might become so mainstream that people may be able to buy and operate such reactors at home. “You can imagine a diabetic patient making insulin that way, or some other drugs,” Rao says. It would work not unlike making baby formula from the mere white powder. Just add water—and some oxygen, too.