Systems biologist Arjun Raman looks at the big picture to build microbiomes that adapt

Like many smart, science-minded kids, Arjun Raman thought he wanted to be a doctor when he grew up.

“There was the romantic idea of helping people, which is nice, but I never allowed my imagination to go anywhere else,” he said. “It was always about thinking that the best way science can be used is to practice medicine.”

“Man, how wrong I was.”

After majoring in chemistry and physics as an undergraduate at the University of Chicago, Raman joined the MD/PhD program at the University of Texas at Southwestern, where he worked in the lab of Rama Ranganathan, MD, PhD, a bioengineer who is now the Joseph Regenstein Professor of Biochemistry and Molecular Biology at UChicago, with a joint appointment in the Pritzker School of Molecular Engineering (PME). Ranganathan studies the design principles and functional properties that build biological systems and heads the Center for Physics of Evolving Systems, a joint effort of the Biological Sciences Division and PME. Raman credits him with opening his eyes to an entire world of science beyond treating patients.

“I like to tell people my experience in his lab allowed me to dream,” he said. “Science allows you to think about what could be, and I found that to be addicting.”

Raman followed Ranganathan’s lead, drawn to the study of systems in the natural world. Sometimes these systems are incredibly complex. Sometimes they are remarkably simple. Sometimes they do things in curiously convoluted ways that defy explanation. But they often rival or exceed the abilities of any kind of system that humans could engineer.

The scientific challenge in studying such systems lies not just in breaking them down to see what each piece does, because the whole of these systems is almost always greater than the sum of its parts. What scientists like Raman want to do instead is learn the rules that helped the system evolve, so researchers could potentially engineer their own systems to solve biological problems and treat disease.

The key to understanding natural systems, Raman says, is that they grow and adapt. They aren’t static machines set into motion that execute preprogrammed instructions. They change in response to different inputs or environments, much like individual organisms.

“If we want to ultimately engineer biological systems to do things that we know engineered systems can do, we have to embrace the idea that there's an entirely new science that needs to focus specifically on systems that have come through the process of evolution, rather than engineering,” he said. “If you understand how principles of design and biology are manifest, then you should be able to go into the lab and create systems that are adaptive and robust too.”

Complex systems, core components

In its most abstract sense, a system is just a bunch of parts that come together to serve a purpose. So, biological systems can be described at many different levels of complexity. Within multicellular organisms, like animals, the systems are different organs and tissues that work with each other to breathe and think and move. At the protein level, amino acids interact with each other to create the folding and function of a protein.

While working with Ranganathan, Raman started using statistics and mathematical formulas to understand systems of proteins. He noticed that out of hundreds of amino acids that may form a protein, just a small number were responsible for its actual functions. What’s more, that small number of amino acids weren’t the obvious candidates based on what he might have guessed from its three-dimensional structure or usual rules of biochemistry. But there were signals that mathematical formulas could predict the important players, and he was intrigued by the idea that those formulas could apply in other contexts too.

During his postdoctoral work at Washington University in St. Louis, Raman had his chance to test this idea. He worked in the lab of Jeff Gordon, a UChicago alumnus who is known as “the Father of the Microbiome” for his groundbreaking studies on the links between gut bacteria and obesity. Gordon’s work renewed interest in the links between the world of microorganisms that inhabit the digestive tract and human health and inspired a cottage industry of scientists sequencing and comparing every gut microbe they could find. But a microbiome, whether in the human gut or anywhere else in the natural world, is an exponentially more complex system of independent organisms that interact with each other, their hosts, and their environment—that is, an ecosystem.

Raman applied the mathematical rules that he developed in simple protein systems to human gut microbiomes and found that the same remarkably small number of components of the system could account for their behavior. For example, he found that out of thousands of species of bacteria in samples from acute malnourished children, just 15 were out of balance compared to healthy control samples. Using that data, they also created a supplement to restore those crucial 15 species that helped the children develop and thrive much better than their cohorts. They saw the same results in animal models as well: The loss of the same 15 species of gut microbes led to malnourishment.

“The same dynamics recapitulated themselves over and over again,” Raman said. “So, there was reason to believe that going through the process of evolution actually reduces the dimensionality of those systems down to core components, and those core components are conserved across contexts.”

Engineering microbiomes from scratch

Now as an Assistant Professor of Pathology in the Duchossois Family Institute (DFI) at UChicago, Raman and his team are using the mathematical rules that reduce complex systems like gut microbiomes down to their core components, and then using that data to engineer new microbiomes from scratch with a specific function in mind. But instead of going about it like an artisanal chef, selecting ingredients based on their distinct properties, they take a systems approach.

Using the DFI’s bank of more than 2,000 gut bacterial strains, they created 100 different custom microbiomes to see how well they could kill off a common pathogen called Klebsiella pneumoniae. Then, using adaptive machine learning algorithms to analyze the results of which combinations did better or worse, they created new microbiomes to test again, refining the process over and over until they had a 24-member, synthetic microbiome that depresses the pathogen in vitro and in mouse models. While the repetitive work for this proof of concept was carried out by a sore-armed and weary postdoc, Raman believes automation can scale up the process to test thousands of synthetic microbiomes for desired functions at a time.

Designing microbial communities with a specific goal in mind is nice, but Raman believes the true goal is learning how to build systems that can adapt as well. Science is littered with promising drug treatments that work perfectly in a petri dish and fail in mice, or cure a disease in rodents but never pass human clinical trials. It’s easy to chalk that up to differences in basic biology between a cell and a mouse and a person. But approaching it from a systems perspective, are there components beyond those responsible for basic functionality that allow a system to adapt and function in different contexts?

Raman believes there are, and they may have to do with the variability we see in different systems throughout nature. If the same 15 species of gut bacteria can help provide a child with proper nutrition, or the same 24 can keep a pesky bug from making you sick, what are the hundreds or thousands of other ones doing? Of course, there are other roles to play, but they may also play a part in helping the system as a whole work in its environmental niche.

“If you look at biological systems that ascribe by decentralized regulation, they're comprised of hundreds to thousands of parts. You don't need that many parts to instantiate function, but what you might need those parts for is adaptability to new functions,” Raman said.

He points out that you can often find the same proteins in many different types of organisms. Whether they’re in bacteria or mice or humans, they fold the same way, they function the same way, they have the same structure. But if you look at their amino acid sequences, they have variations depending on where they came from.

“Those differences are directly related to whether it’s from a mouse, a human, or bacteria. Depending on that the protein is slightly different,” he said. “It could very well be that that slight difference is actually necessary for the protein to function in a human versus a mouse versus bacteria. So, what you think is noise under a microscope actually becomes signal in the wild.”

‘Synthetic ecology’

Using the same mathematical and machine learning tools, he hopes to discover more rules about how to build this kind of adaptability into a synthetic system without compromising its core functionality. This could have huge benefits toward translating findings from the lab more reliably into patients, not just for microbiome-based treatments but any medical advances that struggle to make the leap from bench to bedside.

“We think this kind of synthetic ecology is where synthetic biology can really flourish,” he said. “We have access to the parts, and if we understand how to put them together, we can start solving arbitrary problems in ways that are completely new.”

While Raman may not be the medical doctor he once envisioned, laying hands on patients to heal them, he has found a way to help potentially more people in an unexpected and perhaps unintuitive way. An individual actor, using his unique talents, drawing from the resources at hand and interacting with other members of the community to produce a result greater than the sum of its parts.