Gateway Archive Archives

BTI-NAIST Exchange Marks 15 Years

Tim Montgomery

Following a visit to Minnesota by three Japanese graduate students from the Nara Institute of Science and Technology (NAIST), a group of four Minnesota graduate students from the BioTechnology Institute (BTI) visited Japan in mid-October. Chris Flynn, Grayson Wawrzyn, Jessica Eichmiller and Maria Rebolleda-Gomez were graciously hosted by their NAIST counterparts on a 3-week trip that completed the 15th exchange in a program organized by former BTI Director, Ken Valentas in 1996.

The 15th BTI-NAIST exchange featured a symposium on progress in microbial biotechnology, enzyme engineering and systems biology – and a five-year renewal of the agreement that created the program.

Since its conception, the exchange has successfully connected graduate students from one institution to research groups in the other based on common interests with the intent of learning new skills and techniques. Students from the host laboratory also become cultural mentors for the visitors. Lasting professional and personal bonds are forged in the process, sometimes resulting in collaborative research initiatives.

“I think that my favorite part of Japan,” commented Grayson Wawrzyn, “was learning to be part of a culture so strikingly different from our own.”

Wawrzyn, a graduate student researcher in the lab of Claudia Schmidt-Dannert, was assigned to Takashi Hashimoto’s laboratory and worked with one of his students to help characterize some of the enzymes involved in nicotine biosynthesis in tobacco plants.

Other members of the BTI group participated in equally compelling genomic research projects. Eichmiller studied novel intracellular proline transporters and tested the stress tolerance of mutant yeast strains in the lab of Hiroshi Takagi. Rebolleda-Gomez was introduced to systems biology in the study of bacterial genomics while in the lab of Hirotada Mori. And Flynn learned how cells repair damaged DNA while in the Maki lab.

For Japanese lab members who are required to present their lab work in English each week, working with the exchange group from BTI was an opportunity to practice speaking scientific English.

Living and working together, lab groups also had fun together. Several of the Japanese labs had their own baseball teams, and the last week of the exchange featured ‘lab Olympics day’ where Japanese lab members dressed in super hero outfits competed for fun in a series of relay races.

In addition to experiencing traditional Japanese foods like sushi and okinomiyaki, BTI exchange members also experienced the cultural environment in trips to local shrines around Nara and the old hilltop estates in Arishiyama near Kyoto. The highlight of their cultural experience was a 4-day holiday break that brought most of the group to Tokyo before members went their separate ways. Wawrzyn and Rebolleda-Gomez explored Tokyo further while Eichmiller visited a Japanese friend and Flynn and his wife toured a world heritage shrine and marveled at the beauty of the ponds and cascading waterfalls of Chuzenji in Nikko.

“The hospitality of our hosts was superb,” concluded Flynn. “Everyone was super friendly.”

Added Eichmiller, “an unexpected benefit of the trip to Japan is that I can better relate to my Japanese colleagues at the University.”

A Rewarding Experience in Japan

by Tim Montgomery

Janice Frias, Katherine Volzing, Chad Satori and Josh Ochocki visited Japan this past November as part of the BioTechnology Institute’s ongoing exchange program with the Nara Institute of Science and Technology (NAIST). They travelled to Japan with returning exchange students from NAIST whom they had previously hosted at BTI.

Exciting cultural experiences complemented the students’ lab work at NAIST, beginning with an informal welcome party where dried squid “candy” was served. The BTI group stayed on-campus in guest houses, were chaperoned to various tourist and cultural attractions and experienced new food and pastimes – from a hearty noodle meal of hiroshimayaki to a traditional foot bath where fish nibbled the dirt particles off their toes.

Katherine Volzing visited the ginza, a high style shopping district in Tokyo, and was invited to breakfast with a family in the Tsukiji Fish Market where they served her raw tuna on a stick.

“It looked yucky,” she confessed, “but it was the best thing I ever ate.”

Cultural excursions included time spent at the Todai-ji Temple in Nara; the Kiyomizu Temple, Sanjeusangen-do Temple Garden and Ginkaku-ji Gardens in Kyoto; the Floating Torii at Miyajima; Himeji Castle, and aboard the 200 mph Shinkansen or “bullet train” while in transit. But the exchange group from BTI also accomplished quite a bit in their lab work.

“Professor Takagi said we were the best working group ever,” exclaimed Chad Satori proudly. Satori was excited to work with cell transfections and binding assays in the Sato lab in a change of pace from his mostly analytical work under Edgar Arriaga at BTI.

Janice Frias, who has worked to synthesize biohydrocarbons in the Wackett lab at BTI, was assigned to the lab of NAIST exchange coordinator Hiroshi Takagi, Professor of Cell Biotechnology specializing in applied microbiology and protein engineering. Frias assisted in Takagi’s work with stress tolerance in yeast as an element in improving industrial fermentation in the production of bioethanol.

Frias, Satori and the other members of the exchange group from BTI each found their assignments while at NAIST to be rewarding. Distefano lab member Josh Ochocki was introduced to the work of Professor Kinichi Nakashima exploring neuron stem cells and how they develop into different types of brain cells. And Katherine Volzing found her experience in the lab of Ko Kato examining differentiation in gene expression in stem cells extracted from mice to be a change of pace from her statistical and computational modeling in the Kaznessis lab at BTI. All were impressed with the professionalism as well as the aggressive English requirements of their Japanese counterparts.

“They’re required to put together and present their lab work and plate results in English each week,” explained Janice Frias in amazement.

“The labs were impeccably clean,” concluded Chad Satori. “And everyone was very professional and kind.”

Advancing Biotech Byte by Byte

How computational biology is solving the big data dilemma, one question at a time.

Plus Q&A’s with Dan Knights and Chad Myers

When you log onto Facebook, your profile provides the company with a truckload of data about you — where you hang out, what you “Like”, and who your friends are. What’s more, the computational algorithms used by social media sites are getting better and better at identifying whom you should befriend, or what you should “Like.” Surprisingly, Computational Biologists in the University of Minnesota’s Biotechnology Institute (BTI) are using some of these same algorithmic techniques to power new paradigms in data acquisition and analysis for biology.

In the case of Facebook data, an “edge”is defined by connections to Friends or
actions that a user takes, such as a “Like” or status update. The company stores
all of this information and uses it to personalize your experience on the site. Consider Friend recommendations, for example. “The concept is simple,” says Chad Myers, a BTI member with appointments in both the College of Biological Science (CBS) and the College of Science and Engineering (CSE). “Facebook looks at your set of edges, and someone else’s set of edges, and it says, you share this many edges in common, so you are probably Friends, too. That approach can be really accurate in biological data, as well.”

An “edge” in biological data could fall into a multitude of categories, but the core concept is the same. Biological molecules that perform similar functions often exhibit similar
patterns in large genome-scale datasets. Computational algorithms can then identify and analyze these patterns in the data.

“We can measure similarity in data, but we don’t necessarily have to understand every aspect of it,” says Myers. “Based on how closely associated certain unknown genes or proteins are to more thoroughly annotated genes or proteins, we can guess what functions the unknown molecules might serve.”

With the advent of genomics and other next-generation technologies, the biotech sector is collecting unprecedented quantities of high-dimensional biological data. The increasing complexity and volume of these data could provide significant insights in biology, yet they also introduce unprecedented computational challenges.

For example, think of a simple organism like yeast. One researcher might study a cellular process by using a high-powered microscope to generate video with thousands of frames, each containing of millions of pixels. Another researcher might use state-of-the-art spectrometry to catalogue hundreds or thousands of protein interactions. Yet another might apply genome sequencing to measure quantitative gene expression levels across the genome for hundreds or thousands of samples. Each of these data collection endeavors is a hard problem to solve individually, yet perhaps the ultimate challenge is to understand how these data sets, taken together, tell an integrated story about biology.

Simply put, the human brain cannot process such vast collections of data points. Furthermore, Excel spreadsheets no longer suffice to make sense of these interrelated pools of information. “Computational biologists develop new methods, and define new paradigms to look at data with computational techniques,” says Myers, whose research maps out millions of genetic interactions in organisms all the way from yeast to human cells.

One of the techniques Myers has developed relies — like Facebook — on similarities between clusters of genes. If certain genes display similar expression patterns under a variety of controlled circumstances, it can be inferred that their functions might be similar.

Dan Knights, a member of BTI and the Department of Computer Science and Engineering works in a different sector of Computational Biology. Knights’ research investigates the rapidly evolving microbiomes of human and non-human primates. Both Myers and Knights use primarily genomic sequencing information and advanced computational techniques to pave the way from raw data to an integrated understanding of biology.

“We’re interested in how you can define a healthy gut microbiome,” says Knights, who looks at not just one genome, but at all the genomes present within a subject’s gastrointestinal tract at a given time. “This turns out to be quite challenging because a diverse gut microbiome with hundreds or thousands of different species living in it is actually more healthy than one with only 100 species or 50 species.”

Computational biologists fall along a spectrum. “Some researchers focus more on the biology, others focus entirely on developing new algorithms,” says Knights. Both Myers’ and Knights’ labs do a bit of both. Some of their work involves designing and executing wet lab experiments; the rest involves building tools to process data from those experiments, and other experiments by collaborators across the world.

“Researchers developing computational approaches often have an abstract perspective of biological systems,” says Myers. “While specific biological questions motivate our work, when we develop a method, we rarely only develop it for a specific biological system or even species. The problems that are most exciting are the ones where, if we solve it here, it will also solve someone’s problem out there.”

By deriving solutions that cut across disciplines, faculty in BTI work on a broad range of problems. The same technologies enabling the genomics revolution are also being applied to precision agriculture, sensors in manufacturing, bioremediation, and other environmental concerns like climate change.

Ultimately, researchers in the biology sphere aim to construct models with strong
predictive power. For instance, Myers hopes to understand how genetic interactions influence phenotypes, either in normal or disease states. Likewise, Knights hopes to create diagnostic tests that can predict health consequences based on the community of microbes living within a person’s gastrointestinal tract.

Computational biology is an excellent tool for organizing, identifying and analyzing trends in data sets. Computers won’t, however, eliminate the need for deeply experienced experts. “You can never automate human intuition, especially that of biologists,” says Myers. “There might be thousands of hypotheses that are consistent with the data you’ve measured, but a good geneticist or molecular biologist can really narrow that hypothesis space quickly based on intuition.”

Rather than computers replacing people, computers are changing how humans interact with data. Computational processes are becoming increasingly iterative, meaning that data analysis is not a simple one-off affair. Instead, one set of computations could
reveal a pattern that a human would need to detect and/or interpret. Then the next set of computations would be designed to further investigate this particular pattern, in a cycle that repeats indefinitely.

It’s no longer just that one human detects such patterns. “Projects are getting bigger and more multi-disciplinary,” says Knights, who leans heavily on both local and global collaborators. “Disciplines are converging. Everyone’s trying to put the pieces together right now, because it’s all one giant system.”

At the intersection of computer science and biology lie exquisite opportunities to build new technologies and solve major problems. Visit gateway.bti.umn.edu to read more about the exciting work being completed by BTI faculty.

Insights from our Insides, a Q&A with Dan Knights

Microbiomes are rapidly evolving, and this computational biologist is out to debug the mystery of why.

Colleen Smith

We are what we eat but there’s also a host of microbes living in our guts that help us make the most of all that food. Computational Biologist, Dan Knights investigates the dynamic and rapidly evolving relationship between humans and the bugs living within.

Just like macro-organisms, bacteria have specific environmental needs — and in the case of gut bugs, that environment is the gastrointestinal tract. Central to the concept of the ‘microbiome’ is that a symbiotic relationship exists between this community of microbes and their human hosts. Yet when conditions change, so do microbiomes, and a lot is changing in our world right now.

Knights’ work focuses on understanding what makes a microbiome healthy, and what drives it towards illness. Read more about how he melds biology with bytes in the Q&A below.

Since microbiomes can contain so many different species at once, how do you collect meaningful information about them?

“The main way that we study microbiomes is to grind up their DNA and sequence it. If you try to grow the bugs, you get an enormous bias towards bugs that grow easily in the lab. For a long time, people thought one of the main gut bugs was E. coli. It turns out E. coli just grows really well in a petri dish. E. coli is in most people, but in a healthy individual, it only takes up about one tenth of a percent. We’ve found that instead, if you grind up the DNA and sequence it, you can capture all of the diversity that’s there.”

As a computational biologist, how does computer science affect your work?

“We spend about half of our time designing experiments, carrying out experiments, and analyzing the data. The other half of the time is spent developing new algorithms. The most exciting parts are when we’re developing new tools to support the experiments we’re running, so we really get synergy between the two disciplines of Biology and Computer Science.”

How do computer algorithms aid your research?

“The first way in which we use algorithms is to go from the raw DNA to understanding which bugs they come from, what they might be doing, or which types of genes they are. After we’ve done that raw interpretation, there’s another set of algorithms that we use to interpret the biological significance of the bugs we find. We want to know which are the good bugs and which are the bad ones, and we want to build a new test that will tell you how healthy your microbiome is, based on the species present within it.”

What does a healthy microbiome look like?

“A healthy gut microbiome has hundreds or thousands of different species living in it, and that’s actually more healthy than one that has 50 or 100 species. The diversity makes our data sets high dimensional, but there’s also high variation in bugs between healthy people, in a certain person over time, or between people with a given disease.

It’s not something like blood pressure, where you have a simple ratio. Instead, there are hundreds of numbers, so it presents a very interesting computational problem. What we’re doing primarily is trying to enable precision medicine with the microbiome. This means being able to tell as precisely as possible which species and which strains are in a person, and then to be able to classify them as being healthy or unhealthy. If a person’s microbiome is unhealthy, we say that it is in a state of dysbiosis.”

Does dysbiosis happen on a case-by-case basis, or is something broader occurring?

“Something broader is happening with modernization. We have cross-sectional data where we can see that people who are in a developing country tend to have significantly more diverse microbiota than, say, people living in the USA. If you can find very rural peoples living in developing countries, their guts are even more diverse. We don’t know what benefits those missing bugs are conferring, but we do have epidemic levels of obesity, diabetes, Crohn’s disease, colitis, asthma, and allergies — all of which are linked to a shift in your gut bugs, and all of which are also linked to increased exposure to antibiotics in kids.

It’s likely that we’re missing some of the bugs that we evolved to have. Which exact ones are crucial, we don’t know, but that’s one of the things that we’re studying in our primate microbiome project.”

What patterns have you found through these projects?

“We found that when we have samples from wild monkeys, two different species of monkey will have completely different gut microbiota. But as soon as they move into captivity, they lose most of their native bugs and acquire modern human bugs.

It seems as though there is an axis of dysbiosis. If you start with wild monkeys and continue along to captive monkeys, the next group you get to is non-westernized humans, and then all the way at the end, the farthest ones from the wild, are Americans. And that’s not where you want to be. Our studies of recent immigrants in Minnesota have confirmed this pattern.”

How could biotechnology help those suffering from dysbiosis?

“One way is to give someone new bugs — i.e. a probiotic — although what people typically think of as probiotics is a very limited set of bugs. You can find thousands of probiotics on Amazon, but if you look at the ingredients, it’s the same small set of strains over and over. We’re talking about a much broader set, so that would include bugs like Faecalibacterium prausnitzii. That’s a good bug. Everybody who’s healthy has it. Every time we study a disease, it’s depleted in people who have the disease or are at risk, and yet you can’t buy it.
It’s not a probiotic yet, but that’s an example of a bug we could potentially use as a therapeutic.

Another way is to give someone prebiotics, which are food specifically for your bugs rather than the bugs themselves. You could supplement with a particular chemical that you know encourages certain types of bugs to grow.

Or, there could be targeted antibiotics that would protect certain members of the microbiome without being as comprehensive or broadly destructive.”

Why is this dysbiosis happening, and how can computational biologists contribute to solutions?

“Everyone’s trying to put the pieces together right now, because it’s all one giant system. You can’t really study human endocrinology without considering the bugs, and you can’t study how the bugs are affecting the human without endocrinology and immunology, so research teams are converging, and projects are getting bigger, more multidisciplinary, and more reliant on computation.

It took decades to build a functioning model of a complete cell, in a very simple organism. To do that for multicellular organisms and bacterial communities is still quite far beyond reach. The golden apple would be to have a full computational model of everything in the human body. Everything you have to do in between where we are now and that point of
the comprehensive model is fair game for computational biology.

Some people are modeling low level physical processes between interacting molecules. Others are a level up from that, measuring the activity of enzymes and how different expression levels change in response to environmental conditions. Then there are people studying communities of cells and cell-cell heterogeneity, and so on up the line. All of those problems require computational biology.”

Life? There’s a Map for That. Q&A with Chad Myers

How computational approaches are carving out new ways of understanding biological networks.

Colleen Smith

Computational Biologist Chad Myers applies his expertise in leading edge techniques in computer science to the latest genome-scale technologies in order to understand the genetic architecture of life.

Despite an overabundance of genomic data, researchers remain stumped by many basic questions about how genes interact with each other and the environment to create such astonishingly diverse organisms — and to present such perplexing and difficult obstacles to curing disease states.

Myers’ lab tackles these fundamental challenges by developing new algorithms and computational tools to both map genetic interactions and understand what those interactions mean in model organisms like yeast and worms all the way across the tree of life to humans.

How do you define a genetic interaction?

“If you make mutations in two genes at the same time, and there is an interesting effect on the organism that you couldn’t predict, that’s called a genetic interaction (GI). It’s like you’re flipping switches in the organism in a precise way, turning off certain components one by one.

The interesting thing is that flipping most of those switches off individually doesn’t have an effect. It’s been 15 years since researchers first knocked out every single one of yeast’s 6,000 genes. They found that yeast only needs 20% of those genes to grow.”

Does that mean most of yeasts’ genes are redundant?

“There are relatively few genes that are redundant in sequence, but they’re functionally redundant. By flipping combinations of switches, you start to see that you need one of two systems, but you don’t need both of them. Our lab has been heavily involved in mapping those types of interactions.”

What types of questions can you answer by mapping out GIs?

“There’s been a decade and a half of mapping projects in different contexts, and this yeast project is one of those. Everyone decided, since we can measure this on a large scale, let’s just measure it, and understand how much noise is in our measurements, with the expectation that it should be valuable. It was kind of like, build it, and they will come.”

After almost ten years, we’ve now measured interactions for about 15 million combinations of genes in yeast, but what exactly does all this data tell us? Well, we’ve learned quite a bit and much of it generalizes to other species.

For example, even though there are only ~1000 ways to kill a yeast cell by deleting single genes, there are ~100,000 ways to kill a yeast cell by introducing combinations of two mutations. This has major implications beyond yeast because it reveals how complex genetic networks are, and helps to put a measure on the expected complexity of these networks.

By mapping GIs on the scale we have in yeast, we now understand many of the rules so that we can map them more efficiently in other species. For instance, genes that tend to have large numbers of GIs also tend to be enriched for certain functional roles, evolve more slowly, and be more conserved across the tree of life.”

What’s the end goal?

“Computational techniques play big roles in the modern era of data-driven biology. First, they are very important in simply collecting reliable data, which often involves a number of computational steps to process and transform the data. After that, ultimately, what we want to do is squeeze some kind of information with predictive value out of the collected data, where again, computational methods are critical. For example, the goal of techniques like machine learning and data mining is to take a huge amount of data and distill it down to the important nuggets.”

What types of data are computational biologists trying to distill right now?

“The biggest developments in data are driven by genome sequencing technologies, which have been applied in hundreds of ways to measure various aspects of molecular biology in recent years. There are already several thousand species that have their complete genomes sequenced. In humans, for example, there are probably at least a half million genome sequence profiles from a variety of different normal or diseased tissues, many of them public. Despite having all of this data, we still don’t know what half of the genes do, even when they function normally.”

The new way to measure gene expression quantitatively is RNAseq, a sequencing based technique that provides a snapshot of which genes are expressed in which tissues. That data is also huge, with thousands of different expression profiles available for humans or any model organisms, including plants, like maize. This data is really powerful if you want to understand how genes drive the development of tissues, or which genes differentiate between this phenotype or that phenotype.

A lot of questions in biology are related to which genes work together to accomplish things, or which genes are involved in common processes that support life. So, a type of data my lab cares a lot about are network data that can tell us which genes are related. Gene expression information, especially when it’s collected across a variety of different tissues or individuals can be a valuable source for extracting relationships among genes.”

How do you visualize network data?

“Picture a number of nodes, with lines between them. The nodes are genes, and an edge means that when I delete these two genes in combination, it kills the cell.

What’s interesting is that in most cases where that happens, it happens within a larger structure, where there are multiple interactions crossing the two pathways. It’s actually a very highly structured bundle of genes.

It’s hard to look for individual edges because computationally, it’s difficult or impossible to interpret these data at the level of the individual gene pair, but when you integrate the data with our existing knowledge of pathways, the structure emerges. We’ve designed algorithms to find these local structures in the data.”

Does this only work in yeast?

“No, this is not yeast specific — we’ve applied this computational approach in the context of human Genome-Wide Association Studies and found really interesting results.

For example, you might look at two patients and think they have nothing in common because they have mutations in totally different sets of genes. But if you take a step back and say, wait a second, these different mutations are actually affecting the same two pathways, now you can group together patients that otherwise look different. It’s a heterogeneity problem. There are a lot of different paths to the same exact phenotype.”

Is that only true of disease states?

“A fundamental principle is that biological systems are modular. Very rarely do genes work in isolation. They work as part of larger sets of genes that together accomplish something.”

So, these techniques are enabling you to address deeply cross-cutting questions?

“Yes, we can solve one problem here, but by doing so we might also solve a thousand problems like it in different contexts. We might think about maize today, and how we might improve our ability to feed the world, and then tomorrow think about how to treat cancer. A lot of the same concepts are being used and reapplied across diverse biological settings where the computational approach is the bridge.”

Q&A with Michael Freeman

New BTI faculty member translates unknown microbial languages into novel possibilities for biotech.

By Colleen Smith

Michael Freeman joins the Biotechnology Institute this Spring as a new faculty in the College of Biological Sciences. Hired in the Synthetic Biology Cluster, Freeman specializes in the biosynthetic pathways that produce small molecules called natural products.

Natural products are molecules often manufactured by microbes, and they come in many different shapes and sizes. In human health, these biomolecules are of interest for their potential uses as new antibiotics or anticancer drugs. In microbial ecosystems, they also serve many other functions — most of which are presently undefined.

At the University of Minnesota, Freeman will target intriguing bacteria that have not previously been cultured or easily manipulated in the laboratory setting. By studying unknown — and often remarkable — microorganisms, Freeman’s work could lead down new avenues in biotech.

What fundamental question motivates your research?

“Microbes are vitally linked to human health. They live in the soil, affecting how our food grows. They live in our guts, affecting how we digest our food. Yet despite their importance, we still don’t know much about which bacteria are present in these different environments, what they are doing, or how they communicate. My main motivation is to learn how bacteria communicate with each other and the outside world.

With all the genomic sequencing data that is coming out now, my research builds on the idea that we now have a new window into microbial metabolisms. In particular, I’m interested in how microbes communicate with each other through the language of natural products. I focus on the way bacteria construct that language.”

When you say language, what do you mean?

“Well, I’m trying to convey a simple definition of language as a series of words that are built by a sequence of letters. One type of bacterial language is constructed of ‘words’ called peptides, a class of natural products. Peptides are made with amino acid ‘letters’ — each of which must be synthesized by the microbe in a very specific way. Understanding this language, and how to manipulate it, is really the holy grail.”

Do all microbes or bacteria use the same type of communication?

“Bacteria speak many different languages. Some are constructed with novel ‘letters,’ which imply new functions and new chemistry as a product of evolution. Others share common letters or words, even if they’re from completely different environments. However, we don’t always know which bacteria is speaking which language — and even if we do, we don’t know anything about how they are doing it. That’s the puzzle we’re trying to solve.”

Enzymes are specialized proteins that catalyze chemical reactions within living organisms. Why does it matter that uncultured bacteria can use strange enzymes to produce new letters?

“Most organisms use 20 letters for speaking the language of peptides. New enzyme functions essentially expand this number to create drastically more complex words and thus, a richer language. Functionally, these enzyme modifications affect the shape and behavior of peptides, and in turn the microorganisms, in unique and important ways.

How do you detect and decipher the products of novel enzymes?

“Mass spectrometry has played a vital role in my research. I use this extremely sensitive technique because the quantities of the molecules available to me are very, very low sometimes, and the modifications are very, very subtle. If you think of amino acids as letters, then mass spectrometry describes each letter present in a peptide, and in which order, so that you can accurately read the word. ”

Synthetic biology is an emergent and multidisciplinary field that involves the engineering of biological molecules. How does your research fit into this discipline?

“I define myself as a Natural Product Biochemist who uses Synthetic Biology to aid my research. It’s my tool rather than my primary research focus.

Synthetic biology has come a long way for DNA or RNA, but for natural products, it’s still in its infancy. You cannot yet easily piece together letters to make any word you would like to have. The biggest problem is being able to systematically build on the information that you do receive, and have it feed back into your understanding, so that you can actually build more and more complex molecules.”

What new information do you want to feed back into the field?

“If you want to study and produce new compounds, and figure out the different languages microbes use, you need to know not only how to manipulate and work with bacteria, but also how to pair particular natural products with particular bacterial models.

I’m interested in developing expression hosts from different genera, orders, and possibly even phyla in order to standardize how we approach bacterial model systems, under a very limited set of conditions, and using specific vectors. With the backdrop of symbiosis, the coup d’état would be to grow a macro-organism essentially as an incubator for other bacteria.”

How could a greater understanding of microbes and the languages they use contribute to new biotechnologies?

“Oh, in many, many ways. For instance, if we learn how microbes talk to each other, then we give ourselves the opportunity to make them listen, so to speak. This could be in the context of our own gut bacteria to improve health or in bioremediation processes, to become more responsible stewards of the planet. Finding new molecules may lead to new pharmaceutical drugs, and new enzymes can always lead to new opportunities in modifying those drugs or other materials. It is an incredibly exciting time for my field of science and I feel very lucky to be a part of it.”

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