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Ecology and Evolutionary Biology and Complex Systems Professor Luis Zaman researches evolution—both biological and digital.

The experiments that Zaman conducts in the wet lab are relatively simple, he says. “We take some microbes, we grow them, then we challenge them in particular environments,” he says.

Zaman and his lab techs will put microbes and viruses together in some specific set of conditions, let them grow for tens of thousands of generations, and measure what happens. “It’s kind of a forward-looking way of studying evolution as opposed to trying to do it historically,” Zaman explains.

“And the cool thing about experimental evolution is all we really have to do is wait,” Zaman says.

Zaman uses a similar train of thought on another track of his research: studying how computational systems evolve too. But the computer-based models are a lot faster, Zaman says, and enable him to run experiments for more generations.

“There’s just a big time-scale difference,” he says—millions of generations instead of only hundreds. He also explains that the information he can get from a computer simulation is different than the information contained in a test tube full of bacteria. With the open-ended computational system, there’s an incredible amount of freedom because it’s not a fixed model. “They’re self-replicating, full-fledged computer programs, so anything you can imagine a computer program doing, these things could evolve to do.”

Through his wet lab and his computer model research, Zaman studies how different populations co-evolve when one’s existence occurs at the expense of the other. He also investigates patterns of how this relationship between host and parasite plays out in their evolutionary dynamics. Often, one approach is more effective than the other, depending on the kinds of answers he’s looking for or the kinds of questions he’s asking.

For example, Zaman might explore the way bacteria moves through space. “The bacteria can swim, and maybe I want to know how that swimming evolves when a virus is added,” Zaman says. “It might make sense that the bacterial population evolves to stop moving, because moving means being exposed to the virus—kind of social distancing at a bacteria scale.

“Or maybe they move faster, and they sort of run away from the virus,” Zaman says. “If I wanted to study that in a computational system, I’d have to tell the organisms how to move, or at least implement some sort of movement into the program, which then constrains the possible set of behaviors that might evolve. So for me that would be a question that I’d rather ask with the microbial system where there will be some nuanced detail about the way the microbe’s behavior has evolved that we might uncover.”

By relying on biological experiments and computational modeling, Zaman is able to better grapple with the surprising nature of evolution.

“Biology is just so amazingly diverse and complicated, and oftentimes simulations don't end up looking that way,” Zaman says. “One of the things I decided to focus on was figuring out what it is about the natural world that produces this level of diversity and complexity and just kind of...amazingness,” he says with a laugh.

Ecology and Evolutionary Biology and Complex Systems Professor Luis Zaman researches evolution—both biological and digital.

The experiments that Zaman conducts in the wet lab are relatively simple, he says. “We take some microbes, we grow them, then we challenge them in particular environments,” he says.

Zaman and his lab techs will put microbes and viruses together in some specific set of conditions, let them grow for tens of thousands of generations, and measure what happens. “It’s kind of a forward-looking way of studying evolution as opposed to trying to do it historically,” Zaman explains.

“And the cool thing about experimental evolution is all we really have to do is wait,” Zaman says.

Zaman uses a similar train of thought on another track of his research: studying how computational systems evolve too. But the computer-based models are a lot faster, Zaman says, and enable him to run experiments for more generations.

“There’s just a big time-scale difference,” he says—millions of generations instead of only hundreds. He also explains that the information he can get from a computer simulation is different than the information contained in a test tube full of bacteria. With the open-ended computational system, there’s an incredible amount of freedom because it’s not a fixed model. “They’re self-replicating, full-fledged computer programs, so anything you can imagine a computer program doing, these things could evolve to do.”

Through his wet lab and his computer model research, Zaman studies how different populations co-evolve when one’s existence occurs at the expense of the other. He also investigates patterns of how this relationship between host and parasite plays out in their evolutionary dynamics. Often, one approach is more effective than the other, depending on the kinds of answers he’s looking for or the kinds of questions he’s asking.

For example, Zaman might explore the way bacteria moves through space. “The bacteria can swim, and maybe I want to know how that swimming evolves when a virus is added,” Zaman says. “It might make sense that the bacterial population evolves to stop moving, because moving means being exposed to the virus—kind of social distancing at a bacteria scale.

“Or maybe they move faster, and they sort of run away from the virus,” Zaman says. “If I wanted to study that in a computational system, I’d have to tell the organisms how to move, or at least implement some sort of movement into the program, which then constrains the possible set of behaviors that might evolve. So for me that would be a question that I’d rather ask with the microbial system where there will be some nuanced detail about the way the microbe’s behavior has evolved that we might uncover.”

By relying on biological experiments and computational modeling, Zaman is able to better grapple with the surprising nature of evolution.

“Biology is just so amazingly diverse and complicated, and oftentimes simulations don't end up looking that way,” Zaman says. “One of the things I decided to focus on was figuring out what it is about the natural world that produces this level of diversity and complexity and just kind of...amazingness,” he says with a laugh.

 

 

Both/And

Zaman is drawn to the element of surprise in his research, which played a part in the story of how he became a scientist, too. “I hated biology in high school and avoided it in college,” he says. “It was a lot of really small chance occurrences that put me where I am now.”

As an undergraduate, Zaman studied computer science at a small liberal arts college that allowed him to choose which science classes he’d take to fulfill his major. “I ended up taking psychology classes to avoid biology. I had a typical high school biology experience where I was forced to memorize stuff, and it felt like a lot of busy work. I didn’t want any more biology after that.”

But during his last semester, Zaman took an elective about genetic programming and algorithms that explored how the idea of evolution could serve as a way to solve computational problems. “So instead of studying evolution directly, we examined a population of potential solutions to some technological problem we were trying to solve. The solutions that actually solved the problem got to reproduce and create offspring, and there was some chance of mutation. You try out the current best solution but let the random process of evolution explore possible better solutions.”

Most of all, Zaman was fascinated by how well evolution actually worked. “People were using the process of evolution to solve really hard, complicated problems,” Zaman says. Many engineering advances—like antennas on space crafts, front ends for high-speed bullet trains, and even some airplanes—were designed using this evolutionary approach. To understand evolutionary computing better, he dug into research on evolutionary biology. “And I became really excited about all the fundamental questions about evolution that were unanswered, and how this computational way of trying to solve problems could be used to ask questions about biology.

Both/And

Zaman is drawn to the element of surprise in his research, which played a part in the story of how he became a scientist, too. “I hated biology in high school and avoided it in college,” he says. “It was a lot of really small chance occurrences that put me where I am now.”

As an undergraduate, Zaman studied computer science at a small liberal arts college that allowed him to choose which science classes he’d take to fulfill his major. “I ended up taking psychology classes to avoid biology. I had a typical high school biology experience where I was forced to memorize stuff, and it felt like a lot of busy work. I didn’t want any more biology after that.”

But during his last semester, Zaman took an elective about genetic programming and algorithms that explored how the idea of evolution could serve as a way to solve computational problems. “So instead of studying evolution directly, we examined a population of potential solutions to some technological problem we were trying to solve. The solutions that actually solved the problem got to reproduce and create offspring, and there was some chance of mutation. You try out the current best solution but let the random process of evolution explore possible better solutions.”

Most of all, Zaman was fascinated by how well evolution actually worked. “People were using the process of evolution to solve really hard, complicated problems,” Zaman says. Many engineering advances—like antennas on space crafts, front ends for high-speed bullet trains, and even some airplanes—were designed using this evolutionary approach. To understand evolutionary computing better, he dug into research on evolutionary biology. “And I became really excited about all the fundamental questions about evolution that were unanswered, and how this computational way of trying to solve problems could be used to ask questions about biology.


From the Fringe to the Center

Zaman went on to get a Ph.D. in computer science and ecology, evolution, and behavior at Michigan State University, and became an LSA Collegiate Fellow in the Center for the Study of Complex Systems in 2017. In 2020, he became assistant professor in the Department of Ecology and Evolutionary Biology (EEB) and in Complex Systems. Now he splits his research time between computational systems and a wet lab that uses live viruses and microbes.

Zaman’s interdisciplinary approach to research is still considered unique in his field. Similar research practices have been around for a while, he says, but they haven’t often been embraced by a core biology group: because they were often more grounded in computational sciences, or perhaps they asked biological questions but lacked the appropriate context, they were relegated to the fringe.

“But now we’re taking what used to be considered artificial intelligence, artificial life, and abstract computational ideas, and putting them into a traditional biological framework to push our understanding of the natural world with systems that we never would have found in nature,” Zaman says.

He stresses the importance of being open-minded and searching for connections between fields to advance a scientific understanding of how the world works.


From the Fringe to the Center

Zaman went on to get a Ph.D. in computer science and ecology, evolution, and behavior at Michigan State University, and became an LSA Collegiate Fellow in the Center for the Study of Complex Systems in 2017. In 2020, he became assistant professor in the Department of Ecology and Evolutionary Biology (EEB) and in Complex Systems. Now he splits his research time between computational systems and a wet lab that uses live viruses and microbes.

Zaman’s interdisciplinary approach to research is still considered unique in his field. Similar research practices have been around for a while, he says, but they haven’t often been embraced by a core biology group: because they were often more grounded in computational sciences, or perhaps they asked biological questions but lacked the appropriate context, they were relegated to the fringe.

“But now we’re taking what used to be considered artificial intelligence, artificial life, and abstract computational ideas, and putting them into a traditional biological framework to push our understanding of the natural world with systems that we never would have found in nature,” Zaman says.

He stresses the importance of being open-minded and searching for connections between fields to advance a scientific understanding of how the world works.

 

 

For Zaman, this is valuable on a personal level too. “We have to be open to the weird trajectories that lead people to where they are,” he continues. “I specifically avoided taking any bio classes and am now a professor in EEB. It’s not something I would have ever guessed would happen.”

 

 

Illustrations by Ravi Teja Bandaru

 

 


 


 

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Starting college looks a lot different this year for first-year students like J.J., with many courses and activities meeting online. The LSA Annual Fund provides support for tuition, room, and board, as well as the technology and tools necessary to connect to classes and campus. Your support means LSA students won’t miss a beat.


 

 

 

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Release Date: 10/26/2020
Category: Faculty; Research; Meet The Moment
Tags: LSA; Ecology and Evolutionary Biology; Natural Sciences; Complex Systems; LSA Magazine; LSA Collegiate Fellows; Anna Megdell; Luis Zaman; Ravi Teja Bandaru