Technograph Summer 2016

VOLUME 131: SUMMER 2016

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TECHNOSTAFF Technograph editor Isabella Jackson Assistant Technograph editor Brooks Berish Editor-in-cheif Masaki Sugimoto Managing editor for reporting Michal Dwojak Creative director Hannah Auten Designers Juliany Nakazato Michelle Tam Jacob Singleton Copy chiefs Samantha Skipper Caitlin Bremner Writers Emily Scott Vivienne Henning Lilly Mashayek Page transmission Kit Donahue Publisher Lilyan Levant Email technograph@dailyillini.com Mail Technograph 512 E. Green St, 3rd floor Champaign, IL 61820 Phone (217) 337-8350 AN ILLINI MEDIA MEDIA PUBLICATION COPYRIGHT 2016

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TABLE OF CONTENTS

ANCESTRAL TREES

SPEECH TECH

BIO BOTS

Researchers work to uncover genetic source of disease

Beckman Institute fellows develop new language software

Tiny organic machines move in response to exposure to light

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COMPGEN INITIATIVE LINKING DISEASE

TRAITS THROUGH ANCESTRAL TREES

By Vivienne Henning | Staff Writer

T

raits are common aspects of our identities that we often use to differentiate ourselves from one another. Whether it is eye or hair color, each trait is passed down to an individual. The question of how the origin of a trait is tracked is under examination at the Institute for Genomic Biology (IGB) labs. Understanding how ancestral trees are generated can help with disease identification.

The project is being undertaken by different members of the CompGen Initiative, which enables multi-disciplinary work focused on computational genetics. People involved in sociology, biology, psychiatry, engineering, and the High Performance Computing (HPC) bio group have all come together in the pursuit of a common goal: to better understand the relation between human health and disease.

The CompGen Initiative’s online home page says that they “seek to combine the collective strengths of Illinois’ genomic research with its prowess in large-scale parallel systems and big data to develop new technology that enables future genomic breakthroughs. The new technology could enable a better understanding of the basic processes of life, illumination on how evolution works, and custom treatments for disease, among other discoveries.”

“We think that to make strides in understanding health and disease it’s time to break down disciplinary boundaries and start talking with folks who are not just interested in human medical approaches, but take novel ways of looking at problems,” said Derek Wildman, professor of molecular and integrative physiology. “That’s what this institute, the IGB, is all about: multi-interdisciplinary big science.”

Wildman specializes in comparative genomics and phylogenetics – the study of how species are related to one another. There are many different phylogenetic techniques that can be used to help detect human disease traits and that can help us gain a better understanding of the ancestral history of humans.

A vast majority of these kinds of studies are referred to as ‘case controls,’ where the single nucleotide polymorphism (SNP) – a variation in a base pair of a person’s DNA – of individuals is monitored in the context of a person’s particular phenotype, or the observable characteristics of a person.

However, the overall uniqueness and differences amongst human genomes can make this difficult.

Thanks to the Thousands of Genomes project, human genomes from central Europe, Africa and parts of Asia are now easily accessible for study. The team at Illinois can now sequence different human genomes and examine the entire set of SNP’s, or variants, that people in a certain region may have.

Don Armstrong, an IGB research scientist specializing in computational genetics, is using bioinformatics to help focus in on the 2,500 individual human genomes at his disposal to help determine how changes in someone’s genetic code can lead to phenotypes.

In his proposal to have access to Blue Waters – the largest non-classified super computer in the U.S. – he wrote, “Many non-communicable human diseases - such as diabetes, cancer, cardiovascular disease, and mental illness - are caused in part by a complex network of interacting genetic variants. Multiple projects are currently underway to sequence the whole genomes of hundreds of thousands of cases and controls necessary to identify theses variants and their effect upon cellular processes which lead to disease.”

Armstrong received a preliminary grant that enabled him to have 50,000 node hours on Blue Water to aid him in tackling the comparison of 2,500 individual humans.

“Imagine the size of that data set, all the possible (ancestral) trees that you could draw. If you just had you and your parents, there’s just one possible branch that can be drawn.

Now if you took four people, the number of trees and how they can be arranged increases. Now if you imagine having

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7 exploding factorially. It’s a non-polynomial – explodes in terms of its complexity – problem,” Armstrong said.

“And because of that the solutions are not exact, so we have to try a bunch of different solutions and hope we come up with the best model that models the tree. It’s only because of the existence of Blue Waters that we’re even able to contemplate working on this problem,” Armstrong said.

Multiple sequence alignments will allow comparison of singular differences of all of the individuals’ genomes lined up on top of each other. That information can then be used to make phylogenetic trees – depictions of relationships amongst humans.

To find the best phylogenetic tree, they’re put through a series of simulations using the Bayesian method of statistical inference and the Markov chain Monte Carlo (MCMC) technique of expectations of statistics helping to determine an estimate.

“We want to find the best (phylogenetic tree). The simulations are models of sequence

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evolution and you can simulate the data and you get a tree returned, and that tree will have a score,” Wildman said. “It crudely represents how many mutations occurred to describe that data. We do these simulations millions of times and the goal is to get it so we find the pinnacle of the best scores for those trees. At the end of the day we’ll have our best view of how all these genomes are all related to one another” Once the best phenotypic ancestral tree is determined, the team is aiming for it to be applicable to help determine trait value of any diseases that contain genetic components.

“A lot of the more pervasive diseases that affect a large number of people - heart disease, cancer, lupus - we’re only in the infancy of understanding the molecular mechanisms that predispose people to getting those diseases,” Armstrong said. “So understanding the genetic changes that had happened will hopefully give us a better understanding of how the disease progresses and give us tools so we can stop the disease in an earlier stage and also make more accurate diagnoses of it.”

With continuously evolving efforts to understand the human genome, the results hold the potential to have a positive effect on human disease treatment and overall health.

“At IGB our slogan is ‘where science meets society’ and our University is a land-grant institution and our mission is to improve people’s lives,” Wildman said. “So we think genomics can be used to improve people’s lives, specifically by looking at our health and disease.”

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Emily Scott | Staff writer

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reethi Jyothi thinks that in 50 years, the term “under-resourced language” should not exist.

To help make that happen, Jyothi, a postdoctoral fellow at Beckman Institute, and her colleague Mark HasegawaJohnson, faculty member of the artificial intelligence group at Beckman Institute, started a research project that aims to transcribe under-resourced languages in a way no one has ever attempted before.

They hope their efforts will help under-resourced languages have access to automatic speech recognition technology that is used on electronic devices, among other applications.

Hasegawa-Johnson said that while there is good speech recognition technology available for about 10 languages, the majority of languages spoken are still incompatible with automatic speech recognition. “(For) the other 6,990 languages, there’s no reasonable audio technology that you can use in those languages because it’s really hard to recruit people to create the labeled speech data that you need in order to create it,” he said.

Jyothi explained that other mainstream approaches to this problem have created language-specific models, where they collect audio of a spoken language and corresponding transcriptions from native speakers of the language.

“It’s a very expensive resource, especially if you’re trying to recognize languages which are minority languages or languages that are very hard to reach native speakers online,” Jyothi said.

In order to collect data about under-resourced languages inexpensively, but still effectively, Jyothi and Hasegawa-Johnson considered the fact that there are many common sounds across languages.

“Even if you play sound in a new language to someone who doesn’t speak the language, there is some useful information that is perceived by this non-native speaker of the language,” Jyothi said.

Their research involves playing audio files to nonnative speakers of a language and asking them to write down English text that most closely matches what they heard. This information is then refined with algorithms and acts as a substitute for transcriptions from a native speaker. So far, they have worked with 10 different languages.

Their approach could solve the commercial problem involved with building automatic speech recognition technology for under-resourced languages — a problem that Hasegawa-Johnson described as simple supply and demand.

He said some minority languages could have only 2,000 native speakers, and it’s possible that of those native speakers, very few of them have Internet access.

“Maybe there are a couple of people, but they’re busy. They’re doctors, bankers, lawyers, or whatever — they don’t have time to sit down and transcribe 100 hours of speech,” Hasegawa-Johnson said.

The smaller the market, the more expensive it becomes to develop automatic speech recognition technology in that language.

“Which means that the people who might be able to use it are left out in the cold,” Hasegawa-Johnson said. “There’s nothing to help them.”

Jyothi and Hasegawa-Johnson’s approach could solve this problem, but the idea is so novel in itself that they said it’s hard to convince others it will work.

It was an idea that came out of Jyothi’s frustration when she was trying to develop automatic speech recognition technology in Hindi and couldn’t find a reliable collection of text in order to do so.

“And I said, well, why don’t we just have people write down what they hear, even if they don’t speak Hindi?” Hasegawa-Johnson said. “If you listen carefully to another language, you can hear consonants versus vowels, you can hear things that sound more ‘e’ like versus more ‘ah’ like — you can hear some distinctions that do carry across languages.”

It’s the type of approach that some have proposed as a joke, Hasegawa-Johnson said. As a researcher,

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“It’s really nice to introduce the problem because it’s kind of really out there - and then show convincing results.”

PREETHI JYOTHI POSTDOCTORAL FELLOW AT BECKMAN INSTITUTE

Jyothi said that poses a problem when it comes to convincing others of the approach’s validity, but makes it even more rewarding when they show that it works.

“It’s nice to introduce the problem — because it’s kind of really out there — and then show convincing results,” Jyothi said. “Especially when you compare those results with baselines which people trust . . . then it allows us to

justify our techniques better.”

Hasegawa-Johnson said it’s the “wait-and-see phenomenon” that commonly occurs in scientific research — where others aren’t willing to jump into the research until they see that it works.

“I think the only people besides us who have tried this now are people who are working with us,” he said.

Moving forward, Jyothi and Hasegawa-Johnson said their biggest challenges will be scaling their approach so that it can be competitive with systems based on transcriptions from native speakers.

Ultimately, they would like to see their work reduce the cost of entry into under-resourced languages, and make speech technology available to those who may not want to learn a language such as English or Mandarin.

It all comes back to Jyothi and Hasegawa-Johnson’s belief that this technology will allow people to communicate and live their lives comfortably, despite the fact that they speak an under-resourced language. With their novel approach, they hope to make this term a thing of the past.

“This mismatched crowdsourcing has been a really effective new tool that nobody else has done,” Hasegawa-Johnson said. “But our ultimate goal, really, is to make it possible to create a speech technology in any language.”

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Graduate Students Develop Biological Robots that can be Controlled by Light

By Lily Mashayek | Staff Writer

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raduate students at the University have developed biological machines that can be controlled using light.

The body of the six- to 18- millimeter robot is made from skeletal muscle, protein and a biocompatible polymer.

The bio bot contracts and relaxes, allowing the robot to move.

“A lot of our ideas for the design come from bio-inspiration,” said graduate student Ritu Raman, the first author of the paper. “Our bio bots — the way they walk is very similar to the way an inchworm walks and that was done deliberately because it’s an easy form of

walking to imitate.”

In order to move, however, the bots require some type of stimulus. Caroline Cvetkovic, a graduate student, said they initially used electrical signals to stimulate the bots, but it became harmful to the muscle tissue over time.

“The first thing we did was use electrical signals, so basically sending an electric field or zapping the cells and getting them all to contract at one time,” Cvetkovic said. “But, if you do that a lot, just like if you could imagine electrocuting someone, over time it becomes harmful to the cells … and they gradually weaken.”

Have a

So, the team of engineers turned to a non-invasive form of stimulus: flashes of LED light.

“If we can use something like light, that’s really non-invasive,” Cvetkovic said.“And it’s great because you can apply the light at a certain distance away so you don’t have to touch the cells, and you’re not introducing anything that’s really harmful to them over time.”

“We basically used genetic engineering (and) genetic modification to put a different kind of channel in the membrane,” Raman said. “So, they have these light-gated ion channels rather than voltage-gated.”

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The skeletal muscle bot is actually the second version of the biological robot. Initially, cardiac muscle was used instead of skeletal muscle. However, cardiac muscle contracts on its own, so it was not possible to control it was to control its movement.

“In the first generation, we used cardiac cells … and these were really great for the first device we designed because heart cells will contract or beat spontaneously,” Cvetkovic said. “But that didn’t allow us to have any way of actually controlling the muscle.”

“Skeletal muscle is really good for that because, in the body, muscle only contracts when neurons fire, so you can very directly control (that),” Raman said.

Raman said the robots are also being worked on by dozens of people outside of the University who are all a part of an National Science Foundation, NSF, center involving multiple universities. Each team works on a different aspect of the bots, with the University of Illinois team working on the actual building of the bots.

“This project and this lab is part of an NSF center between MIT, Illinois and Georgia Tech,” Raman said. “So, there’s dozens of different people working on things kind of related to bio bots at different stages of bio bots.”

Sarah Frier, sophomore in Engineering, said that while she hadn’t heard of these biological machines before, she thinks they are an “amazing” idea.

“I think that’s amazing, I think it’s really exciting that something that sounds so advanced and almost, like, futuristic, came out of our graduates,” she said.

Raman said that they are also very aware of the ethical implications of their work.

“People start thinking, ‘oh, you’re making these biological machines: are they alive, are they living, are you considering the ethics of building something that could potentially have some sort of agency?” Raman said. “We like to say, currently we are very far from anything that you would consider a living, or really any kind of, machine.”

Cvetkovic said they are still not sure what they might use the bio bots for in the future.

“So, if you think about them as these kinds of biological legos … if we had building blocks of different biological materials and then man-made materials, how do we arrange them in different ways to get machines that can do different things?’” Cvetkovic said.

Raman said they are currently focusing more on the fundamentals of the bots and on understanding the basics of how they work.

“What we’re really trying to do as a lab and with this project is to establish very fundamental scientific principles of building with biology,” Raman said.

“We are still far out from a more therapeutic or clinical application,” Cvetkovic said. “But we like to think about (the bots) in terms of developing different systems and platforms that can then be developed further down the line.”

Raman said their lab has been primarily health-focused.

Cvetkovic said they have worked on some medical applications, such as a glucose sensor.

“I think there are a lot of future applications for this type of technology, primarily in health but also in other types of environmental or security things based on the kind of cells that you use,” Raman said.

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