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Climate change is affecting farming all over the world, and solutions are seldom simple. But if you could plant crops that resisted the heat, cold or drought instead of moving a thousand miles away, wouldnt you? Avalo helps plants like these become a reality using AI-powered genome analysis that can reduce the time and money it takes to breed hardier plants for this hot century.

Founded by two friends who thought theyd take a shot at a startup before committing to a life of academia, Avalo has a very direct value proposition, but it takes a bit of science to understand it.

Big seed and agriculture companies put a lot of work into creating better versions of major crops. By making corn or rice ever so slightly more resistant to heat, insects, drought or flooding, they can make huge improvements to yields and profits for farmers, or alternatively make a plant viable to grow somewhere it couldnt before.

There are big decreases to yields in equatorial areas and its not that corn kernels are getting smaller, said co-founder and CEO Brendan Collins. Farmers move upland because salt water intrusion is disrupting fields, but they run into early spring frosts that kill their seedlings. Or they need rust resistant wheat to survive fungal outbreaks in humid, wet summers. We need to create new varieties if we want to adapt to this new environmental reality.

To make those improvements in a systematic way, researchers emphasize existing traits in the plant; this isnt about splicing in a new gene but bringing out qualities that are already there. This used to be done by the simple method of growing several plants, comparing them, and planting the seeds of the one that best exemplifies the trait like Mendel in Genetics 101.

Nowadays, however, we have sequenced the genome of these plants and can be a little more direct. By finding out which genes are active in the plants with a desired trait, better expression of those genes can be targeted for future generations. The problem is that doing this still takes a long time as in a decade.

The difficult part of the modern process stems (so to speak) from the issue that traits, like survival in the face of a drought, arent just single genes. They may be any number of genes interacting in a complex way. Just as theres no single gene for becoming an Olympic gymnast, there isnt one for becoming drought-resistant rice. So when the companies do what are called genome-wide association studies, they end up with hundreds of candidates for genes that contribute to the trait, and then must laboriously test various combinations of these in living plants, which even at industrial rates and scales takes years to do.

Numbered, genetically differentiated rice plants being raised for testing purposes. Image Credits: Avalo

The ability to just find genes and then do something with them is actually pretty limited as these traits become more complicated, said Mariano Alvarez, co-founder and CSO of Avalo. Trying to increase the efficiency of an enzyme is easy, you just go in with CRISPR and edit it but increasing yield in corn, there are thousands, maybe millions of genes contributing to that. If youre a big strategic [e.g., Monsanto] trying to make drought-tolerant rice, youre looking at 15 years, 200 million dollars its a long play.

This is where Avalo steps in. The company has built a model for simulating the effects of changes to a plants genome, which they claim can reduce that 15-year lead time to two or three and the cost by a similar ratio.

The idea was to create a much more realistic model for the genome thats more evolutionarily aware, said Collins. That is, a system that models the genome and genes on it that includes more context from biology and evolution. With a better model, you get far fewer false positives on genes associated with a trait, because it rules out far more as noise, unrelated genes, minor contributors and so on.

He gave the example of a cold-tolerant rice strain that one company was working on. A genomewide association study found 566 genes of interest, and to investigate each costs somewhere in the neighborhood of $40,000 due to the time, staff and materials required. That means investigating this one trait might run up a $20 million tab over several years, which naturally limits both the parties who can even attempt such an operation, and the crops that they will invest the time and money in. If you expect a return on investment, you cant spend that kind of cash improving a niche crop for an outlier market.

Were here to democratize that process, said Collins. In that same body of data relating to cold-tolerant rice, We found 32 genes of interest, and based on our simulations and retrospective studies, we know that all of those are truly causal. And we were able to grow 10 knockouts to validate them, three in a three-month period.

In each graph, dots represent confidence levels in genes that must be tested. The Avalo model clears up the data and selects only the most promising ones. Image Credits: Avalo

To unpack the jargon a little there, from the start Avalos system ruled out more than 90% of the genes that would have had to be individually investigated. They had high confidence that these 32 genes were not just related, but causal having a real effect on the trait. And this was borne out with brief knockout studies, where a particular gene is blocked and the effect of that studied. Avalo calls its method gene discovery via informationless perturbations, or GDIP.

Part of it is the inherent facility of machine learning algorithms when it comes to pulling signal out of noise, but Collins noted that they needed to come at the problem with a fresh approach, letting the model learn the structures and relationships on its own. And it was also important to them that the model be explainable that is, that its results dont just appear out of a black box but have some kind of justification.

This latter issue is a tough one, but they achieved it by systematically swapping out genes of interest in repeated simulations with what amount to dummy versions, which dont disrupt the trait but do help the model learn what each gene is contributing.

Avalo co-founders Mariano Alvarez (left) and Brendan Collins by a greenhouse. Image Credits: Avalo

Using our tech, we can come up with a minimal predictive breeding set for traits of interest. You can design the perfect genotype in silico [i.e., in simulation] and then do intensive breeding and watch for that genotype, said Collins. And the cost is low enough that it can be done by smaller outfits or with less popular crops, or for traits that are outside possibilities since climate change is so unpredictable, who can say whether heat- or cold-tolerant wheat would be better 20 years from now?

By reducing the capital cost of undertaking this exercise, we sort of unlock this space where its economically viable to work on a climate-tolerant trait, said Alvarez.

Avalo is partnering with several universities to accelerate the creation of other resilient and sustainable plants that might never have seen the light of day otherwise. These research groups have tons of data but not a lot of resources, making them excellent candidates to demonstrate the companys capabilities.

The university partnerships will also establish that the system works for fairly undomesticated plants that need some work before they can be used at scale. For instance it might be better to supersize a wild grain thats naturally resistant to drought instead of trying to add drought resistance to a naturally large grain species, but no one was willing to spend $20 million to find out.

On the commercial side, they plan to offer the data handling service first, one of many startups offering big cost and time savings to slower, more established companies in spaces like agriculture and pharmaceuticals. With luck Avalo will be able to help bring a few of these plants into agriculture and become a seed provider as well.

The company just emerged from the IndieBio accelerator a few weeks ago and has already secured $3 million in seed funding to continue their work at greater scale. The round was co-led by Better Ventures and Giant Ventures, with At One Ventures, Climate Capital, David Rowan and of course IndieBio parent SOSV participating.

Brendan convinced me that starting a startup would be way more fun and interesting than applying for faculty jobs, said Alvarez. And he was totally right.

Originally posted here:
Avalo uses machine learning to accelerate the adaptation of crops to climate change - TechCrunch

CHAMPAIGN, Ill. Scientists have developed and deployed a series of new imaging and machine-learning tools to discover attributes that contribute to water-use efficiency in crop plants during photosynthesis and to reveal the genetic basis of variation in those traits.

The findings are described in a series of four research papers led by University of Illinois Urbana-Champaign graduate students Jiayang (Kevin) Xie and Parthiban Prakash, and postdoctoral researchers John Ferguson, Samuel Fernandes and Charles Pignon.

The goal is to breed or engineer crops that are better at conserving water without sacrificing yield, said Andrew Leakey, a professor of plant biology and of crop sciences at the University of Illinois Urbana-Champaign, who directed the research.

Drought stress limits agricultural production more than anything else, Leakey said. And scientists are working to find ways to minimize water loss from plant leaves without decreasing the amount of carbon dioxide the leaves take in.

Plants breathe in carbon dioxide through tiny pores in their leaves called stomata. That carbon dioxide drives photosynthesis and contributes to plant growth. But the stomata also allow moisture to escape in the form of water vapor.

A new approach to analyzing the epidermis layer of plant leaves revealed that the size and shape of the stomata (lighter green pores) in corn leaves strongly influence the crops water-use efficiency.

Micrograph by Jiayang (Kevin) Xie

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The amount of water vapor and carbon dioxide exchanged between the leaf and atmosphere depends on the number of stomata, their size and how quickly they open or close in response to environmental signals, Leakey said. If rainfall is low or the air is too hot and dry, there can be insufficient water to meet demand, leading to reduced photosynthesis, productivity and survival.

To better understand this process in plants like corn, sorghum and grasses of the genus Setaria, the team analyzed how the stomata on their leaves influenced plants water-use efficiency.

We investigated the number, size and speed of closing movements of stomata in these closely related species, Leakey said. This is very challenging because the traditional methods for measuring these traits are very slow and laborious.

For example, determining stomatal density previously involved manually counting the pores under a microscope. The slowness of this method means scientists are unable to analyze large datasets, Leakey said.

There are a lot of features of the leaf epidermis that normally dont get measured because it takes too much time, he said. Or, if they get measured, its in really small experiments. And you cant discover the genetic basis for a trait with a really small experiment.

To speed the work, Xie took a machine-learning tool originally developed to help self-driving cars navigate complex environments and converted it into an application that could quickly identify, count and measure thousands of cells and cell features in each leaf sample.

Jiayang (Kevin) Xie converted a machine-learning tool originally designed to help self-driving cars navigate complex environments into an application that can quickly analyze features on the surface of plant leaves.

Photo by L. Brian Stauffer

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To do this manually, it would take you several weeks of labor just to count the stomata on a seasons worth of leaf samples, Leakey said. And it would take you months more to manually measure the sizes of the stomata or the sizes of any of the other cells.

The team used sophisticated statistical approaches to identify regions of the genome and lists of genes that likely control variation in the patterning of stomata on the leaf surface. They also used thermal cameras in field and laboratory experiments to quickly assess the temperature of leaves as a proxy for how much water loss was cooling the leaves.

This revealed key links between changes in microscopic anatomy and the physiological or functional performance of the plants, Leakey said.

By comparing leaf characteristics with the plants water-use efficiency in field experiments, the researchers found that the size and shape of the stomata in corn appeared to be more important than had previously been recognized, Leakey said.

Along with the identification of genes that likely contribute to those features, the discovery will inform future efforts to breed or genetically engineer crop plants that use water more efficiently, the researchers said.

The new approach provides an unprecedented view of the structure and function of the outermost layer of plant leaves, Xie said.

There are so many things we dont know about the characteristics of the epidermis, and this machine-learning algorithm is giving us a much more comprehensive picture, he said. We can extract a lot more potential data on traits from the images weve taken. This is something people could not have done before.

Leakey is an affiliate of the Carl R. Woese Institute for Genomic Biology at the U. of I. He and his colleagues report their findings in a study published in The Journal of Experimental Botany and in three papers in the journal Plant Physiology (see below).

The National Science Foundation Plant Genome Research Program, the Advanced Research Projects Agency-Energy, the Department of Energy Biosystems Design Program, the Foundation for Food and Agriculture Research Graduate Student Fellows Program, The Agriculture and Food Research Initiative from the U.S. Department of Agriculture National Institute of Food and Agriculture, and the U. of I. Center for Digital Agriculture supported this research.

More:
New imaging, machine-learning methods speed effort to reduce crops' need for water - University of Illinois News

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Link:
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In this event, we will talk about how AI is changing the tech industry, and how the talent pool is not growing fast enough to meet the demands

Companies across all industries have been scrambling to secure top AI talent from a pool thats not growing fast enough. Even during the economic disruptions and layoffs caused by the COVID-19 pandemic, the demand for AI talent has been strong. Leaders are looking to reduce costs through automation and efficiency, and AI has a real role to play in that effort

Our panel will be comprised of amazing people in the industry

Koyuki Nakamori Head of Machine Learning at HeadSpace

Nehar Poddar Machine Learning Engineer at DEKA Research and Development

Excerpt from:
How AI and Machine Learning are changing the tech industry - refreshmiami.com