Episode 65: Formalizing how plants live and adapt through algorithms

Today we are joined by Dr. Alexander Buczk, a plant phenomicist and an associate professor in the School of Plant Sciences at the University of Arizona. He has over 10 years of experience in developing and applying mathematical and computational methods to study plant morphology and physiology across scales and domains from organismal to... ecosystem level and from above to below ground. His current research focuses on root phenomics ranging from the population level to the molecular level.

He develops computational imaging and stimulation techniques that can be applied in the lab and in the field. He has received the NSF Career Award, the Early Career Award of the North American Plant Phenotyping Network, and the Fred C. Davidson Award for his contributions to the field. Thank you, Alex, for joining us today.

Yeah. Thank you for inviting me. Yeah, absolutely. So we like to start every podcast off with a couple of just fun, rapid fire questions that are meant to be easy to answer. So what is your favorite word? It's Wunster. It's Dutch. And when I learned touch, I always wondered what are the wounds.

Yeah, I thought it's little animals or something like that in my mind. And when I asked my Dutch course, they told me it's coming from the day of Marcus and there's a whole story about it. But yeah, I always thought about little animals that are wounds to thunder. Okay. What's your favorite breakfast food? Oh, pizza from the day before. Oh, cold pizza. Cold pizza. Yeah, exactly. Money or happiness? Happiness, definitely. Yeah.

Yeah. No question. It's an easy one to answer. Yeah. It was not difficult. So your research is motivated through the impositions that climate change creates on the agricultural and natural plant ecosystem.

Can you tell us how you became interested in this field of study? And can you also tell us what is a phenomicist? area of computer science, very mathy with algorithms. But I always, I like nature. I try to combine it all the time. And so that's how it got together. So that's simply the concerns I had as an...

as a person outside of academia, that we're about climate and nature, how it changes over time now, combined with my interests. That merged together into what we call now a phenomena. That's kind of a new word. just invented in a working group in the International Plant Phenotyping Network, right? So we tried to define what are we when we do things outside, when we just start measuring plants. How do we define ourselves? That was the question behind. And we think now...

terms of like, okay, a phenomenicist is making formal systems to study plant life, right? And to describe how plants interact with the environment and how do they change suddenly. Do they survive? So we basically are making formal tools to study that. And I think that's how this word now starts to exist slowly. We just started using it about half a year ago within this working group.

start putting it into grants and everywhere. It's more specific than scientists, right? Yeah, it's more specific. It's like physicists, right? A physicist, then you know what they're doing. They're doing the physics of the planet, right? Or of the universe. We are basically doing now the phenomena. of plants as phenomena by using formal systems. And formal systems is a very broad term in the way we defined it. It could be an imaging system, a hardware that is defined based on whatever.

electrical circuits right this is a formal system but it can be an algorithm it can be a simulation it can be an imaging system that you just combine algorithm halfway algorithm halfway electronics right could be everything okay yeah it's very broadly defined exactly yeah i know genomics but what is phenomics well so the first thing to recognize is the appendix which is omics right it's basically talking about

tools. Phenomics deals with everything that is the phenotype. For me that's plants, right? How does a plant look like and how do I quantify how it looks like? So that it could be like the length of a branch, the number of leaves. area of leaves and then in my case right i go below ground it's like how many roots single roots are actually in this root system right roots are also just like a lot of branches like taking a tree crown upside down

You can measure length, diameters, angles on that. But you can also, in our case, we try to find ways to summarize all these diameters and angles into one mathematical expression. That's a way then you can make. a whole system of branches comparable to each other. And then if you have that, either single measurements like angles or these summary descriptions, you can start from.

for example, using genomics and link it to genes. You can say, hey, is this angle, this shape of this whole plant, is there any genetic control? Okay. Then you say, okay, there's a genetic control to it. And you go back and say, okay, this shape also seems to be very often in a desert landscape, in whatever, a very humid environment. And you have a relationship to the environment.

So you can make this link. How do these plants survive in these very extreme environments or even normal environments? Okay. It's just a relationship that you can make then. from the phenotype what you see is how is the plant actually living in an environment.

Okay, thank you for explaining that. And how does it interact with us. Yeah, sure. Where did you study to obtain your degrees? Oh, I studied originally in Cottbus. That's where I obtained my bachelor and master. That's in Germany, southeast of Berlin. towards the Polish border. So when you ran out of money as an undergrad, you go over the border to get cheaper food. Basically, that was the benefit of it.

So we did that a lot to save some money back then. I don't think it's possible nowadays anymore. It's expensive everywhere. Yeah, I think it just rose up the price. But yeah, it was a good time there. And then a bachelor master there. And then I went to Delft in the Netherlands, which is... basically the other side of Europe. It's at the ocean, or close to the ocean, to be honest. And that's where I did my PhD on trees and how tree grounds are structured and about that.

And basically we made the first algorithm to just analyze complete Tariq rounds. Back then, there was not a field like we talk now, like phenotyping or there was no phenomicist. So there were a few people that started looking at plants and how to measure them in general. And we started doing actually... kind of was like a little algebra that we found to make algorithms that really take every branch out of a tree and make it measurable.

So that was the challenge back then. And if you go even two or three years back, so in my master, we started making the first surface reconstruction of a barley plant. You see always these 3D models in television. animation videos and so on. If you look at a plant, it becomes inherently more difficult because a plant, right, if you just, it's a podcast, so we cannot look outside. But if you look at the plant, so it's, okay, a tree has a stem, right? It's like 3D.

and then you have leaves and they're basically just like flat. They're kind of 2D in your data. So if you turn it into data, it becomes two dimensional. And then if you look at like, for example, a little grass, it becomes like a line.

in your data. So if you, for example, take a laser, shoot it on and measure a lot of points on it to become a point cloud, right? And then there will be suddenly just a line that is one dimensional. So we have really challenges in dimensionality in the object. It's not just like your puppet or your...

These comic ducks that you have and they're like, oh, it's just 3D and nice surfaces. No, it's not like that. And then these surfaces, you know, there's water on it. And if you go outside into the real world, it becomes a whole different story. Then you have, for example, your instrument that you use to measure a plant interacting with all of that. You shoot a laser, which is basically light, put it on a water drop, what happens? Or it has a size and it hits just one part of the plant.

that is, let's say, a piece of the leaf, and then the other piece hits the stem of a tree. So you get some measurement in between. So it becomes very noisy and messy compared to what you see in the movie theaters when you see your 3D. animation movies so yeah basically that's what we do we put in either 3d 2d imaging and then we start measuring these plans but with all of that noise

messy chaos around. Makes sense out of all of that, huh? Yeah, makes sense out of that. And then, well, what do you do with all of these measurements is the question. And so if you have a lot of plants measured, we... can now relate that for example to genes right we can say okay look this length of this branch or

or this size of the root that we get always related to a certain gene or several genes that the plant has. And then if you link this trait that you measure this measurement to, okay, that makes more yield under drought, like here in Tucson. So you can say, okay, if we know these genes, we can tell them to breeders. They work with that to get more yield somewhere in the middle of the desert. So that's where it starts getting very interesting. But it's the step before making the plant.

Right. That's important to know. Yeah. Yeah. Thank you for explaining that. So what was it that brought you to the University of Arizona and Bio 5? Oh, another long story. So back in my postdoc. I was at Georgia Tech back then. Okay. in Atlanta. And I collected a lot of the data I work now even on in Wilcox, Arizona. So in Wilcox, Arizona, there was a root research center for a long time. I don't think so. It was closed.

But that's where I collected my data initially. And I got a job in Georgia, UGA back then. So that's how it went. There was no position here in Arizona. And even so, I collaborate since a long, long time with the folks of Cybers here at Bio5. So they're all here. I ended up or started my position in Georgia at UGA, my first professor position.

But yeah, I'm still working on that data and still collecting data here. And then there was a position three here. I applied and people asked me, do you want to apply? And I said, yeah, let's try that. That's wonderful. Yeah, you're a lot closer to Wilcox, but it sounds like it's closed now. Well, there's still a campus farm up here, so that's very similar. So I'm just really in the desert where I get my data from.

originally i'm not just closer to my data i don't have to fly out or do anything you know i just drive 20 minutes from my house to the field actually all my my lab is now sampling in the field right now Nice. Have you had any cool breakthroughs lately in the lab that you would be willing to share with us? Well, I think two, I would say. So most of the things we do, I think we didn't cover that at the beginning, are actually its own roots, right?

And so one thing we found over the last 10 years, if you want to call that a breakthrough, it takes a while to build up. It seems like always like a breakthrough in the public, but it's really building up to it. a long time yeah so over the last 10 years what became very clear to us is that if we look at how plant breeding

approached plants. It was always about this one trait, this one feature that we want to improve. And that if we go out in the field, we just take a few plants and we assume everything is the same, right? We look at it, we measure it and all these measurements are more or less the same on all plants that we look at.

But then we said, okay, let's take a whole field. We have all these nice methods, these formal systems that we built, right? We can look at so many images. We can simulate things that we cannot see. Just look at a whole field, what's going on there below ground. And we saw who? There's a lot more variegation. There's so much stuff out there. So there's small roots, big roots. There are like roots with very flat angles, deep angles, all in one population. Okay. So in one field.

So that should be kind of the same genes, should be the same variety all the time. You don't expect so much changes between. And then we looked at, okay, can we group that? put that in different groups, could compute out of this data and link them to certain functions. It turned out, hey, they're like all specialists. They're like, oh, I'll take up nitrogen. I take up phosphorus. They take basically different kinds of foods in. So why should they do that? What we think.

It's not proven right now, but we're on the way to do that. So we think they help each other by being specialists in these extreme climates like here in Tucson. It's a desert. You want to be good at something and then help others. So I think that's kind of the biggest thing we found out right now. Another thing, more of a side project of four or five years, not side project really, but it was not the main focus initially, is that we also found it's kind of a...

a little hair. So roots have hairs that help with the uptake of nutrients from the soil and they're very much extending the surface that is in contact with us. They were always thought to be at the end of each root, called a root branch, very end. But we found them very early on now in development. at the end of these long roots that are a bit older, and now we found them in the first few days. But they look different.

They just had a very different shape. So we put it on the microscope. We looked at the shape. Oh, it's really different than a root hair. But it's also different than any other little hair we find buff ground. So the question is, what is that? It seems also that it has to do with nutrient uptake.

in the very early days when the seedling established. And then we looked around already a little bit, not fully, but it does not seem to be in every... plant it seems to be very specific to a common being like that's what you have in your burrito oh yeah so these beans have that which makes sense now they're also from around here

So if you think about Wilcox, there's still the Bonita bean canning. So beans are very popular here. We're very excited about that. So if this can help to lower the seedling depth. So when the seedling establishes... It's very vulnerable, basically like a baby. And if you put it in extreme environment, it easily dies. And we have sometimes about, yeah, up to 20% seedling death. Okay.

And if you have this in some plants, but a bean somehow has this hair that helps them. It's just one cell basically that extends out. interesting very early on like in the first five six days already yeah other planes don't have that so we're very excited what was going on there we did now the first genetic studies on that where we saw, okay, it's really somehow related to either nutrient uptake and also a little bit to defense against pests. So there's some antimicrobial.

function involved okay so that kind of leads into my next question which is what are the implications of these discoveries into society it's it's about plants and how they survive in extreme environments and that's the whole climate change links link the more we know yeah

And then plants with all their services that they provide for us, right? If you look at crops, yeah, it's definitely food. Can go further. It could be like, you know, you can build houses out of plants. True. Right? Yeah. Or we did that along. long time, still built. Still many houses in the US are built with a lot of wood. It's also an energy source, right?

burn wood or make biofuels out of it, maybe medicinal plants that you want to use. So it's generally how do we get any kind of product from the plant in extreme environments. That's it about. Okay. Thank you for explaining that. Can you share how the computational imaging and simulation techniques that you develop are applied in the lab and in the field? More or less the same way, right? We make images. So one thing we do if we go into the field is we have...

So we put a plant inside the scanner. It takes a lot of photos from all sides of the plant. And then we take all these photos, put them into an algorithm, and the algorithm gives us a 3D model back. And that's the basis on what we measure any kind of trait on the plant. In the lab, it's more or less the same, it's just under easier conditions. So there's a difference in how the imaging instrument looks like. So it's not as robust in the lab.

can be any kind of photo camera you know whatever you have at home could be iphones okay we have that how it really looks like in the field is we have to dig up these plants And there it gets messy, right? So there's the soil. The problem that we face with imaging, especially in the field, is soil has a lot of iron. And if you think about either electromagnetic waves, like whatever, X-ray or any other...

wave you want to send down there, your resolution gets very low. So we are still digging them up. In the lab, you have other possibilities, right? If the volume, you put a plant in a pot, the volume is very small and you can... put in a controlled way a lot of energy there. There are no people around. You can put it from the side, right? There's the soil. Your plant might be in the soil. It's hard to get from the side without destroying too much. I think there are the limitations that we...

just face in the field. So we are starting to dig them up, wash them with water. and thousands of plants. And we basically say, hey, just take a lot of plants, like thousands. And then we destroy a lot. But we have a lot of that. So we can start making statistics because of the number of plants. We have to just look at very many and then we figure out what is well.

in this data and whatnot, while you have in the lab, perhaps just like three, four, five samples, we have thousands. So that's how we attack the problem and just look, what do we see over and over and over again. and also an incomplete field, right? This is just not small. You'd need 15, 20 people to process 5,000 plants in.

two weeks think of that and that's hard work yeah you're going out with a shovel yeah and you're digging them up to get them out of the ground and you have to be very careful not to sever the root or you know i would imagine you got to get really far down there Yeah, it is kind of a standard way how to destruct the root. So we are not claiming that we get the whole root out. That is impossible, but you can say, okay, you're getting a good part of it out.

out of the ground and we can measure consistently certain things like we can measure an angle we can measure a diameter at this root we certainly cannot measure the length reliably or the depth because it's limited by how deep our shuffle goes right right

But that's just something to take into account. What is the data telling you? I think this is often also a misconception out there that if you are destructive, you cannot measure anything. So especially if you talk to researchers in the lab that are just trained in labs. environments, they have these perfect systems. In the field, it's not perfect.

Right. That makes sense. Yeah. It's just not perfect. And their plants are dying in between. We're living with this incomplete data, but we try to get information out of incomplete data. That's what... what is the easiest description about it. Okay. But you're aware that it's incomplete. Right. That's the same. You're not saying no. You know what you're working with. Yeah. All right.

That makes complete sense. Thank you. Digital imaging of root traits or dirt. Is that one of the techniques that you use when you're measuring? This is the platform that your lab created. And I think it was that your lab in Georgia created this or. My postdoc is over 10 years old now. Okay. Okay, do you still use that technique or have you kind of evolved? We still use dirt to some extent. So, for example, if you look at the specialization of

fruits in a whole population, a whole field, as I described before. The first results we have are still made with dirt and it's based on how can you summarize a root system in kind of one value, one construct. It's a bit of an abstract thing.

to think about, but we look at the shape of the root and we try to find only one thing to describe it. And then we can take this one thing very, very often and start finding groups. So for example, it's like you find more round shapes, more triangular shapes. more square shapes overall. And that's where we are with these whole field studies right now. But if you look a little bit in the future, I also mentioned a scanner, a 3D scanner. That's where we are right now and we restart.

bringing that to these massive throughputs in the field. So it takes a lot of technology development to get the throughput simply for thousands of plants in two weeks. And we are doing right now the first study in 3D on that level.

we don't know what comes out of that but i mean it makes sense technology uh evolves over time and so absolutely like it completely makes sense and thank you for explaining that yeah but yeah dirt is still there but it's also 10 years old so we are yeah we are now already having dirt 3d it's already published as a system just the scale to the field is now the first time that we go

to 5,000 plants. We have already little studies of like four or five hundred plants on sorghum that we did last year up in Maricopa at the Precultural Center and we have also maize or corn studies out there to just distinguish genotypes from each other, like more like classic phenotyping, I call it. You say, okay, we just look at many different varieties and then we just look at the differences between the varieties where you have like two or three samples.

per variety that is out there. So it's not just looking at the whole variation. So it's like pre-sample where you go out and say, okay, I'll take five plants and then the most three similar ones I accept as being. The phenotype. Okay, that's kind of the classic way to do it. I gotcha. Okay, okay

Do you have any collaborators on campus or elsewhere that have contributed to the success of your research? Or has your research contributed to the success of any collaborators? Oh, many. Exactly. Okay, let's keep it on the route. So here on campus, I think the biggest study I did until now was with Giovanni Melandri. He's also here at Bio5.

Oh, okay. So, and that was the sorghum up at Maricopa. Sure. For many years, I collaborate with Jonathan Lynch in Penn State. Yeah. And I think we helped each other evolving our research. He's the one that actually brought me into root. okay research at the beginning when i came here i was in a project where he was involved and that's where i met him was my postdoc project and that's since then since over almost 15 years oh my god 15 years yeah we collaborate he's retiring now maybe

You never know. He tried to retire a few times, but I think he also likes to keep on working. He's very, very passionate. He's an amazing researcher out there. He's a researcher, collaborator, but also a lot of them. a mentor in some sense because he brought me during my postdoc to south africa and yeah and then we did studies there and i just started learning about roots and

why roots are important and how they are able to feed the world. He was a big, big influence in getting into the root phenotyping and applying technology to roots. yeah he he started digging digging upwards yeah so but the all the part that we can actually scale up and do the imaging was in a collaboration for the first time with him for me okay so yeah and then there are many more collaborators i have

collaborator in Thailand that I met in South Africa on that farm back then. And we started doing student exchange with each other. over also already five, six years, seven, seven years. Oh my God. Since seven years you're already collaborating. So that's Kwan in Thailand. And then there are many more collaborators over the years that I had. And are the collaborators interdisciplinary nature kind of labs? Let's turn the question around.

When a student comes into my lab and I explain what our lab is, I tell them, okay, our lab is... not the lab you normally see because we only have each background once or twice. It's not like you're coming in a molecular biology lab or everybody knows PCR or any kind of standard technique.

We have a mathematician. We have a computer scientist. We have a cell biologist. There is a molecular biologist. And even then, if you go down to the undergrad level, even the undergrads are very unique. So there we have a biosystems engineer. a physiologist so everybody's basically just the boss in their own field in their own yeah and they often know more much more than i do so that's different than in a in a

in many of the typical labs where one person could take over for the other. That's hardly possible for us. We have a little bit of overlap, but not too much. The lab is interdisciplinary itself. I was going to say, your lab is the definition of interdisciplinary. My background is not even in biology. But what happens then is when we collaborate, we often collaborate with very specialized labs that do one thing.

and that's where we draw on and then that's also how we can help them right we help them with the breadth of different disciplines and we get a lot of expertise very specialized expertise back that we often cannot cover ourselves So that's how it works. That makes sense. Yeah. Okay. Well, thank you. Sorry for turning that upside down. No, it's okay. That makes complete sense. It was a...

you know, great way to paint the picture and to understand what it is. You kind of talked about this a little bit, but do you have a mentor or a few mentors that have impacted your life? Oh, yeah, there's certainly a whole list of mentors from all kinds of fields. The typical ones are, you know, it's your PhD advisor, your postdoc advisor. But the reality is there's actually everybody is your mentor.

your whole lab everybody if you if you're willing to listen everybody can be your mentor always and you always learn something from From the undergrad to the big professor that you meet at a conference. I believe in that too. Yeah. You just need to be willing to listen. So I think everybody is a mentor. But sure, there are people that hired you as a postdoc or as a PhD that influenced you more than others.

others because it's just the time they spend with you right that's a i love that answer that's wonderful i believe in that too so what is your why why do you get up in the morning what keeps you going i mean the nice thing about our job maybe it's a dichotomy you know our job In one sense, you come home as a scientist and you have every day a new problem, right? Most people don't come home with a problem every day for us.

That's the thing, but it's also the progression that you every day can make a step towards a little bit of a better world. Either it's the food problem, how do we feed people under this climate pressure? get more energy under this climate pressure using plants. So there's always this little step that you make towards a better world that is nice, despite having a new problem that you bring home every night. Yeah. Wow. And we, us non-scientists, thank you for...

We're always being curious and taking home the problems, but also making the progress in the labs. to ultimately inform the greater population and provide more information on how to help humans. So thank you for your work. Sure. Thank you. Thank you for being here and talking with us on Science Talks today. Oh, definitely. It was a lot of fun. Good. Yay.

Thanks to our listeners for tuning in to another episode of Science Talks. Continue the conversation with us next time as we learn more about the amazing science happening at the University of Arizona's Bio5 Institute.