BEACON Researchers at Work: Providing computational methods for biological research

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin research scientist Dennis Wylie.

Chimera of goat and lionI’ve always been intrigued by the combination of seemingly incongruous things. As a child I loved stories about strange chimerical creatures composed of one part this animal, two parts that, and so on — not just for the fantastic nature of the imagery but also because I couldn’t stop asking myself questions like “how does the goat head feel when the lion head eats a goat?” As an adult I’ve therefore found it particularly interesting to be working in the field of computational biology: the goals and methods of one discipline often seem bizarre from the perspective of another, but sometimes the contrast helps to bring out something new and unexpected.

Since January I’ve worked at the Center for Computational Biology and Bioinformatics (CCBB) at the University of Texas as part of the bioinformatics consulting group. We work with faculty members across several departments pursuing a large variety of different projects which in one way or another involve large-scale data sets and complex computational analyses, largely (though certainly not exclusively) based around next-generation sequencing (NGS). In the few months since I have joined the group, I have worked on projects applying computational approaches for NGS variant calling, methylation profiling via bisulfite sequencing, RNA-Seq differential expression analysis, RIP-Seq analysis, and metagenomics, among other methods.


Probabilities of proneural vs. mesenchymal subtype (blue indicates higher probability of proneural, black of mesenchymal) predicted by toy SVM model designed to demonstrate overfitting and trained on RNA expression levels of two genes as measured by sequencing (GSE57872). Hollow inverted triangles are specimens which were assigned mesenchymal subtype according to Patel et al 2014 (Science 344: 1396-1401), while filled-in triangles represent specimens assigned to proneural subtype. Contours represent probability levels approximately equal to 50% or 100%; in each of the primarily mesenchymal and primarily proneural regions of the plot, a few samples of the opposite subtype create a “hole” region in which the prediction is flipped.

The project with which I have been most heavily involved here at UT is the ongoing study of the proteasome in Andreas Matouschek’s lab. While it has been well established for some time that the attachment of polyubiquitin chains to proteins targets them for degradation by the proteasome, some proteins are degraded with much less efficiency than others even when polyubiquitinated. Matouschek’s group has shown that the breakdown of polyubiquitinated proteins is accelerated by the presence of an unstructured region which can serve as an initiation site for the proteasome to begin the degradation process. Moreover, they have demonstrated through experiments varying the length and complexity of peptide tail sequences appended to fluorescent proteins that there are observable patterns in the types of peptides which serve as more or less effective initiation sites. The rich complexity of the space of relevant peptide structural features, combined with the increasingly large data sets the Matouschek lab is now generating, makes this a promising meeting ground for biophysical, computational, and statistical methodologies. In this context I am currently providing consultation applying algorithmic pattern recognition methods to help tease out in more detail which features determine proteasomal degradation efficiency.

Dennis WylieAt the CCBB I also have the opportunity to teach the methods I help apply in various research projects to students, postdocs, and anyone else who might be interested. At the end of every May we offer a slate of courses composing our “Summer School for Big Data in Biology,” for which this year I put together a course in machine learning methods for gene expression analysis (based both on my experience at UT and my prior work as a bioinformatician in industry developing molecular diagnostic tests). In planning the syllabus, assembling examples, teaching the techniques, and discussing the many potential applications with students, I had plenty of time to contemplate the interplay of ideas (and, more prosaically, of different jargons) from biology, mathematics, computer science, and many other fields. For example, the idea of model overfitting (e.g., see contour plot of overfit SVM classification model), whether done by human or by machine, lurks in the background of pretty much every scientific field (though not always known by that name).

It is my hope that in providing both research consultation and educational services at the CCBB we are assisting in the ongoing synthesis of scientific theory such that future generations will more easily appreciate the harmonies between disciplines. Combinations that today seem bizarre chimeras may one day be appreciated as natural fauna inhabiting the scientific landscape.

For more information about Dennis’ work, you can contact him at denniswylie at austin dot utexas dot edu.

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Misnomers and Mixed Intentions: Communicating Science is Hard

Reposted from the Teaching Evolution in Action blog

By Chris Symons

Chris SymonsThe route of information between the raw data of scientific experimentation to the public’s understanding is convoluted. The murky water of scientific communication is problematic at best, if anyone ever hopes to get the public united behind a scientific issue, they will need to learn to navigate these problems with science communication they can expect to encounter.

There are a couple key factors, notably term misuse and biased/sensational media, to be blamed for many communication misconceptions.

Term misuse causes inaccurate understandings of definitions. For example, pop culture and video games are sources of evolution being used incorrectly. In the Pokémon series the ‘evolution’ that transpires in the game is actually a metamorphosis, and while this mix up is certainly innocent enough, it is term misuse that reinforces evolution as being a deliberate, linear, immediate process. In other words, that two chimpanzees suddenly birthed a human child, for example. This is a common misunderstanding, that evolution occurs in a single glorious moment and a new species is born. Evolution is a process that occurs over generations, with no specific direction, resulting in very gradual changes to the gene pool.

Pictured: Not evolution

Another word that is under constant contention is ‘theory’, which suffers from different use by scientists, as opposed to the general public. People attach the uncertainty that the common understanding of ‘theory’ has, to the way scientists use it, and this is highly confusing. When scientists use the term ‘theory’, it means an idea that is heavily tested, and heavily reinforced and supported by evidence. Gravity, evolution, continental drift, heavy bombardment, and relativity are all theories- yet we do not see the heavy doubt and denial with all of them.

There is another great divide in communication, due to differing goals of all the people the information must get through to get to the public ear. The scientist may want to convey his data neutrally, and make sure she is not making any assertions her colleagues and fellow scientists will question too harshly. That information could be picked up by a researcher, who wants to use particular points from the dense and technical write up of the findings for a specific purpose. The researcher will emphasize these specific points to suit their purpose. If the media gets involved, their prerogative is getting as many viewers or page views as possible, so they will often lean towards sensationalism by exaggerating points further. Even though the information is still the same, the way it is presented and viewed changes the way it is received and understood. This leads to misunderstanding.

Both of these processes occur commonly, and warp the public’s understanding of scientific information. Communication is critical for re-establishing a higher degree of trust and understanding between the public and the people who do science for their careers. The potential oversight of these roadblocks can be nothing short of disastrous to the relationship between citizens and science. My advice to the general public would be to look hard at your sources of news, and stay engaged and curious about the world. My advice to scientists is to avoid highbrow scientific jargon, or writing too dryly and complexly, when dealing with the public. I also highly recommend every scientist engage actively in social media. What better way to relay information directly to the public than to access them directly? For citizens to obtain information from scientists directly, would be a fantastic step towards a more educated public body- and I have no doubt this educated public would be more united to act on issues like climate change, vaccinations, and evolution.

I have recently discovered a wonderfully inspiring TEDx talk delivered by Sheril Kirshenbaum, all about communicating science. Check it out here-

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Passing of Prof. John Holland, father of genetic algorithms and pioneer in complex systems

August 10, 2015

Dear BEACONites,

It is with great sadness that I report the death yesterday of Prof. John H. Holland, Professor of Psychology and of Computer Science and Engineering at the University of Michigan. John, 86, succumbed to an illness he had been dealing with for a few years, although he did manage to publish two books in the last three years in spite of it. Until very recently, he was as full of ideas and as animated as ever.

John HollandJohn invented genetic algorithms in the 1960’s, proving a number of theorems about them before they had been so named. He also pioneered then what are now called “learning classifier systems,” although he called them only “classifier systems.” John led the development of a marvelous multi-disciplinary Ph.D. program at UM, in “Computer and Communication Sciences.” He was the reason I went to UM for my Ph.D., and was my teacher and mentor in learning about genetic algorithms, although his former student Bernie Zeigler served as my academic advisor, so I am formally Holland’s “academic grandchild.” John founded the Logic of Computers Group with the late Prof. Art Burks, another brilliant thinker (logician and automata theorist) and co-inventor of the ENIAC Computer at Penn. John was largely responsible for making that research group an inspiring place to work and a fun place to spend as many hours as possible each week!

After publication of John’s book “Analysis of Natural and Artificial Systems” in 1975, he became widely recognized and lauded as the father of GA’s. He received many awards, including the MacArthur Fellowship, the Louis Levy Medal, and UM’s highest award for a faculty member.

John was also a co-founder and still a Trustee and External Professor of the Santa Fe Institute, which has been for many years a leading place where economists, physicists, computer scientists, biologists and others formulated the concepts of complex systems that are so important in science and economics today. Many of these leaders were Nobel Laureates, and John was highly esteemed among them. He wrote many books on complex systems, many of which were inspired by his study of the basic requirements for spawning of living organisms.

UM’s Center for the Study of Complex Systems was the brain-child of the highly multidisciplinary BACH group, of which John Holland was a founder–Burks (Art), Axelrod (Bob), Cohen (Michael), and Holland (John). Others joined the group after the 1980’s.

I am greatly saddened by John’s passing–he has always been a great inspiration to me–but I take consolation from knowing that his memory and his legacy will survive long beyond our generation of scientists.

Erik Goodman, BEACON Director

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BEACON Researchers at Work: Partnerships between plants and bacteria

This week’s BEACON Researchers at Work blog post is by MSU graduate student Colleen Friel.

Colleen Friel at the lab benchMy foray into the world of science started back when I was a high school student dead set on becoming a large animal veterinarian. To pursue that goal, I attended the Pennsylvania Governor’s School for Agricultural Sciences the summer before I graduated high school. While there, I performed my first rectal exam on an obviously displeased dairy cow and realized that veterinary medicine was not the career for me. After this rude awakening, I unexpectedly found myself fascinated by my classes in plant science and agronomy – here was a way to engage in science that fed the world (and, as an added benefit, did not involve orifices of any kind!). As an undergraduate, I performed research in microbiology, forestry, and plant biology, a combination that led to my interest in my current field of research, plant-microbe interactions. I was fascinated by how interactions between plants and organisms too small for the naked eye to see were so important on so many scales–they are vital to the functioning of natural ecosystems and goals of feeding the growing population sustainably.

Here in the Friesen lab at Michigan State, my research focuses on how plant and soil microbes exchange resources. One system I study is the mutualism between legumes (plants such as peas, beans, and clover) and soil bacteria called rhizobia. Rhizobia induce some plants to form specialized organs on their roots, called nodules. The rhizobia live in the nodules and fix nitrogen gas from the atmosphere into a form that the plants are able to use. In return for fixing nitrogen, the plant supplies the rhizobia with photosynthetically fixed carbon.

Nodules on a Trifolium plant (courtesy of Maren Friesen)

Nodules on a Trifolium plant (courtesy of Maren Friesen)

This interaction is like a biological market, where the plant and rhizobia trade carbon for nitrogen vice versa. Each organism must decide how much effort they will put into independently acquiring resources (e.g., the plant deciding how much effort to put into root growth to directly take up nitrogen from the soil) and how much effort they will put into trading for resources (e.g., the plant deciding how much carbon to supply to its rhizobia in exchange for nitrogen). Using economic theory about how markets work, it is possible to create mathematical models that describe the costs and benefits legumes and rhizobia incur through this trading agreement. In a collaborative project with the Yair-Shachar Hill lab at MSU and Emily Grman at Eastern Michigan University, we are currently working experimentally validating an existing mathematical model to describe the outcome of the legume-rhizobia mutualism in different environmental conditions. We are using photosynthesis, biomass, and carbon and nitrogen content measurements to estimate parameter values for a previously written model that we adapted to the legume-rhizobia symbiosis. We will test the model by comparing its predicted outcomes to those observed in nature or a novel set of experiments. This work is making important connections between theoretical ecological models and empirical physiological studies, while helping us understand the context-dependent effects of mutualisms on community structure.

Another economic theory that can be applied to the legume-rhizobia mutualism is the tragedy of the commons, where an individual acts against the common interest of the group by depleting a resource for its own benefit. The tragedy of the commons is a situation faced by farmers who are able to graze livestock on village commons: each farmer is motivated to graze as many animals as possible to maximize his or her personal benefit, but this will lead to overgrazing of the commons and depletion of the resource for everyone. In the context of legume-rhizobia interactions, one would imagine that rhizobia would often become “cheaters” in this system-that is, that they would direct their resources towards their own growth and reproduction rather than toward fixing nitrogen for their host plant. Thus, we would expect rhizobial populations to be dominated by these cheaters who fix little or no nitrogen while still acquiring carbon from the plant. However, this is not the case: rhizobia are very diverse and are not dominated by cheating strains.

One possible mechanism that could be stabilizing the mutualism is sanctioning, or the plant “punishing” cheaters for being less cooperative. Plants may punish rhizobia by decreasing nodule carbon or oxygen supply or by promoting early nodule death, but we do not currently understand the exact mechanism of these sanctions. I am making a mutant strain of rhizobia that is unable to fix nitrogen because of a missing protein. Once I finish generating this mutant, I hope to use it for a number of experiments where I will use techniques such as RNA sequencing to determine how the plant’s response differs between cheating and fixing rhizobia at the molecular level. This project will help us identify the genes and proteins that plants use to sanction cheaters, which could help us to understand how legumes and rhizobia co-evolved.

My research seeks to better understand how legumes negotiate interactions with their rhizobial partners. With it, I hope to provide insights into important questions in ecology and evolution about the stabilization of mutualisms, their context-dependency, and their effects on community structure. 

For more information about Colleen’s work, you can follow her on Twitter (@colleen_friel) or email her at frielcol at gmail dot com.

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Natural Rejection: Addressing Student Resistance to Evolution Education

Reposted from the Teaching Evolution in Action blog

Israel_Day 2By Ian Zaback

It’s a moment that we’ve all dreaded in one way or another. A student approaches you at the end of class clutching a note, and as the paper changes hands the phrase leaves their lips: “I’m opting out of learning evolution.”

If you found yourself at the other end of this conversation, what would you say? Would your first move be to silence them, and use the curriculum to reinforce your decision? Or would you send an email to their parents, taking a diplomatic tone to explain why this is such an important concept to learn? This is a situation that all science educators worry about in the back of their minds, but so few have given thought to what they might say if they actually landed in the hot seat. The truth is, this is a multifaceted issue with no one simple answer.

Despite 95% of all scientists accepting the validity of evolution, the United States remains among the lowest in its public acceptance of evolution. This distrust of scientific theory is often deeply rooted in religious and spiritual beliefs, and so trying to challenge such misconceptions only drives students further into the shells constructed for them by their families and communities.

So how do you approach such a sensitive situation without it quickly turning sour? One strategy is to try and preempt the fallout before students fixate on the controversy. Evolution is a fact of life, and so much of what we know about the world makes a great deal more sense in the light of evolutionary theory. By easing into the subject just like you would with any other topic, you can present this important overarching concept as simply another thing to be learned. The advantage of this strategy is that some students may put up less resistance to the evidence behind it, gradually coming to realize just how perfectly many other ecological concepts that they’ve learned up to this point make sense in context. Alternatively, if you come into class one day and say to your students: “Alright, there’s a lot of controversy around this topic, but today we’re getting close and personal with Charles Darwin!” …you may be digging your own grave. To sensationalize the topic is to rob it of the status of peer-reviewed, highly accepted scientific theory, and instead make it seem like a political issue. Even if you don’t encounter immediate resistance, some of your students may very well grow to consider evolution to be a controversy rather than well-grounded science for the rest of their academic careers.

The important thing to understand is that it is not the science teacher’s job to challenge a students’ deeply rooted religious beliefs. Rather, the important thing is that we communicate the underlying concepts as something of value and ensure that the students develop a thorough understanding of scientific theory. For example, by stressing the scientific definition of a theory as something supported by multiple lines of evidence, we can provide students with an opportunity to understand the school of thought behind scientific theory and discovery. This carries with it the potential to broaden their horizons; in contrast, vocalizing a defined, partial stance favoring evolution as an absolute (whether you accept the theory or not) may serve only to shut them down.

When you get down to it, one of the most important lessons a science teacher can impart to their students is to ask questions. One word epitomizes scientific thought: “Why?” Everything we know about the world around us, from gravity to evolution, we know because somebody observed something that they could not explain and wondered why it was so. Science class shouldn’t just be about learning the facts; rather, the expectation should be that every student walks out of the classroom at the end of the year prepared to make discoveries of their own. So how do you mitigate unpleasant situations when teaching about evolution? You need to work with the student to show evolution in a different context. Shutting resistant students down only hinders the learning process and pushes students away from a lifestyle of scientific discovery. However, if you inspire them to adopt a more open worldview and accept them in turn, they may yet be inspired to embrace their own scientific curiosity.

About Ian: Hello, fellow scientists! My name is Ian Zaback. Hailing from Farmington Hills, MI, I’m a secondary biology education student from Michigan State, a proud Briggsie and RISE student. Officially, my role this summer is as the KBS Science Education Intern, but I am thrilled to be joining all of you in Teaching Evolution in Action as well. For as long as I can remember, I’ve been incredibly passionate about the natural sciences. From running environmental advocacy campaigns in high school to serving as an outdoor education specialist at sleepaway camp for the last two years, deep down I’ve always understood that the greatest good that I can do is in sharing my passion with others, engaging my students in active learning and inspiring them to ask questions about the world around them. I can hardly wait to grow with all of you throughout the summer!

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