BEACON Researchers at Work: The role of resource mutualisms in plant adaptation to abiotic environments

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

Tomomi inoculating hog peanuts with rhizobia

Tomomi inoculating hog peanuts with rhizobia

When you get thirsty, what do you do? You simply get something to drink, right? Plants don’t have the ability to move like animals, so they had to come up with other strategies to deal with stress like drought, heat stress, and salinity. For example, they can reproduce and disperse seeds to less stressful habitats or they can associate with other organisms, such as symbiotic microbes, that can “help them out” when times get tough. Although the second strategy has received very little attention, there is increasing evidence that interacting species, particularly microbial symbionts, are capable of facilitating plant adaptation to stress.

Ecologically, there is lots of evidence supporting that microbial symbionts can facilitate a plant’s tolerance to abiotic stress. For example, resource mutualists, such as arbuscular mycorrhizal fungi and nitrogen-fixing bacteria, can help plants acquire nutrients and can help mitigate the effects of drought and low pH. Evolutionarily, genetic variation in microbial symbionts may even facilitate plant adaptation to local environments. Given their short generation times, genetic diversity and dispersal ability, rapid evolution of microbial symbionts may facilitate adaptive plant responses to environmental stress.

Can you find nodules in the roots?

Can you find nodules in the roots?

My research focuses on whether soil bacteria make it possible for plants to adapt to and live in different habitats. One type of soil bacteria, called rhizobia, infect the roots of plants from the Fabaceae family (a.k.a legumes). Once inside the root, they form “root bumps,” called nodules. Rhizobia live inside the root nodules and convert nitrogen in the atmosphere into ammonia, in a form that legumes can use (like a natural fertilizer!). In turn, legumes provide photosynthetic carbon to the rhizobia. Rhizobia therefore can help plants grow in areas where they might not live otherwise. But just like human relationships, plants and rhizobia may not be compatible, or one of the partners may not be even available! For example, rhizobia may not survive or convert nitrogen effectively in certain environmental conditions, like dry soil or shade. Using a native legume called the hog peanut (Amphicarpaea bracteata), I study how mutualism between plants and rhizobia are affected by environmental stress.

In particular, I test whether rhizobia mediate plant adaptation to soil moisture, a well-characterized stressor to plants that also is known to influence plant-microbe interactions. I’m interested in three specific questions: 1) Are plants locally adapted to soil moisture conditions? 2) Do resource mutualists contribute to plant adaptation to soil moisture? 3) What plant traits drive adaptation to wet vs. dry environments?

Reciprocal transplant experiment in progress

Reciprocal transplant experiment in progress

I am currently conducting a series of field and greenhouse experiments to test these questions. I don’t have all the answers yet, but so far I have found soil moisture affects nodulation and benefits that rhizobia provide to plants. I also found that there’s genetic variation for symbiosis-related traits (e.g. nodulation, nodule size) among plant genotypes, suggesting the potential for plants and rhizobia to co-evolve in response to soil moisture. My goal of this project is to expand our understanding of the mechanisms behind local adaptation in two ways. First, I will examine whether symbiotic mutualists are contributing to local adaptation to soil moisture. Given the intimate relationships between plants and symbiotic microbes, it is likely that rhizobia play a role in plant adaptation. Second, I will identify environmental factors driving local adaptation and phenotypic traits under selection, which are critically important to understanding the cause of natural selection and variation in selection among local habitats.

High school students from KAMSC conducting an experiment testing the effects of fertilization on soybean-rhizobia interactions.

High school students from KAMSC conducting an experiment testing the effects of fertilization on soybean-rhizobia interactions

Sam Peters (high school student from KAMSC) working on his independent project in winter 2013

Sam Peters (high school student from KAMSC) working on his independent project in winter 2013

Plant-rhizobia as educational tool: Along with a research on plant-rhizobia interactions, I have shared my excitement for this topic with middle school and high school students. For example, through a BEACON education project, I had an opportunity to mentor a motivated high school student from Kalamazoo Math and Science Center on his independent project, testing whether rhizobia from different soil nitrogen have evolved differently to benefit the plants. I also worked with Brad Williamson, a former president of National Biology Teachers, to create a guided inquiry biology lesson, using the plant-rhizobia symbiosis as a model system (in review for The American Biology Teacher). In this lesson, students gain experience in scientific methods by coming up with hypothesis, designing and conducting experiments, to making claims based on the data they collect. We think that the plant-rhizobia interaction is an excellent system to teach inquiry-based science at high school and college levels.

For more information about Tomomi’s work, checkout her website at tomomisuwa.com or contact suwatomo at msu dot edu.

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BEACON Researchers at Work: What makes invasive species successful?

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

Amanda Charbonneau next to one of her tallest plants - she's 5'6"!

Amanda Charbonneau next to one of her tallest plants – she’s 5’6″!

I love to walk through the woods on a quiet quest to see how many woodland creatures I can spot, and to take an inventory of what’s new and blooming. If you spend enough time exploring same woodlot, you begin to notice when new organisms start creeping in. Throughout Michigan, prairies are filling with Autumn Olive, an invasive species from Asia that most of us know only as the silvery green bush growing alongside the expressway. Similarly, some of my favorite woodland paths are now nearly impassable mats of multiflora rose, a thorny Asian species once planted as living fences. The Michigan DNR estimates that there are about 200 invasive plant and animal species in Michigan, most of which accidently (or occasionally purposefully) established here in the last few hundred years.

Two hundred may seem like a large number of organisms, but they aren’t the only non-native species to have found themselves in Michigan over the years. For every stowaway Emerald Ash Borer that successfully establishes itself and becomes a major concern, there must be dozens of other burrowing insects that got here the same way, but didn’t become invasive. There are gardens full of exotic plants in every neighborhood, and yet only a handful, like Garlic Mustard, have escaped to become a pest. There are more than a million catalogued plant and animal species on earth, and yet the number that acts like invasive species is relatively small. One estimate, called the ten’s rule, is that for every thousand species that disperses out of it’s native range, only 100 will survive the dispersal, only 10 of those will establish in a new range, and only one of those will successfully reproduce and become invasive.

So why aren’t all species invasive when given the chance? Or to state it another way: Why are invasive species able to survive in new environments, when most other organisms can’t?

A page from The Herball of Generall Historie of Plantes, by John Norton (1957) - one of the earliest references to weedy radish.

A page from The Herball of Generall Historie of Plantes, by John Norton (1957) – one of the earliest references to weedy radish.

My research is to determine how potentially weedy species adapt to new environments.  It may sound a bit odd to try to learn about invasive species by looking at a weed, but weeds are a good model system for studying invasive species because they tend to invade the places that we care about the most: our yards, gardens and agricultural fields. This makes them disproportionately costly, and the US more than 34 billion dollars a year on weed management. I specifically work on the plants in the genus Raphanus which includes crop radishes, weedy radish, and a number of wild radish plants. Weedy radish, tend to be a problem mostly in wheat, barley and oat fields, where they crowd out desirable crops and contaminate the harvested grains.

One of the coolest things about weedy radish is that they have two close relatives: crop radishes and wild radish. This means that I can compare the physical and genetic characteristics of all three to try to learn more about how the weed evolved. For instance, the weedy and crop radishes grow in fields all over the world, but wild radish only grows around the Mediterranean and mostly in marginal places like beaches, so even though they are closely related, they live in very different environments.

One-month-old radish plants, wild (top) and weedy (bottom)

One-month-old radish plants, wild (top) and weedy (bottom)

Another really interesting way these plants differ is in their growth rate. Farmers only grow wheat for 3 or 4 months before it’s harvested, and everything else gets tilled under, so in order to survive in a field, you have to grow very fast. Weedy radish can go from germination to flowering and starting to produce seed in as little as 30 days, so they can easily reproduce in that time frame. However the wild plants often take more than 100 days to start flowering, and some populations need to grow for an entire year before they can make seeds. This is important, because it suggests that fast growth is a trait that is under intense selection. When wild radish first moved into wheat fields, nearly all of the plants would have gotten tilled under every year without reproducing. However, a very fast growing one might make a few seeds, which would be better able to survive the following season. Since this is an adaptation to tilling, this trait must have evolved since humans started farming.

The difference in growth rate is impressive, but could have just been due to where they were grown. There are, after all, lots of differences between Mediterranean beaches and wheat fields in Kalamazoo, MI. To verify that the differences between the weedy and wild wheat were due to genetics and not environment, I’ve done three common garden experiments with hundreds of plants each. In these experiments, I grew weeds taken from all over the world as well as several wild populations and some crops all in the same large field. However, instead of setting up the plants in orderly groups like you might in your garden, I chose each individual plants’ location randomly. This arrangement tends to drown out all of the small differences in environment across the field, so that all of the differences you see in physical characteristics are based on genetics. In these experiments, there are always dramatic differences between how long it takes the weedy and wild radish to flower.

Now that we’re sure the differences between wild and weedy radish are genetic I’m sequencing several weeds and wild plants to find the places where they differ genetically. Since we know all of the plants are closely related, we expect that most of their genes will be very similar, and the few differences we see in their genomes should correlate with their physical differences. Once I have all the sequencing results back, I should be able to find things like the genes that allow weedy radish to grow so much faster than the wild version, or genes that allow weedy radish to flourish in fields instead of beaches.

If we can find the genomic regions that control things like growth rate, that’s a trait that crop breeders might be interested in exploiting. They might also be give us a place to start looking for important genomic regions in other weeds, and maybe even more typical invasive plants, since fast growth is one of the commonalities many of them share. From an evolutionary point of view, it’s also important just to understand how weeds came about. New weeds and invasive species are evolving all the time, and the more we know about how they occur, the better our chances of slowing them down when they next show up on our doorstep.

For more information about Amanda’s work, you can contact her at charbo24 at msu dot edu.

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Visualizing coevolution in dynamic fitness landscapes

This post and video is by postdoc Bjørn Østman and graduate student Randy Olson, both at Michigan State University.

The fitness landscape is the framework for thinking about evolutionary processes the same way the phylogenetic tree is how we think about evolutionary history. It can guide our thinking and even enable us to predict outcomes of evolution.

Fitness landscapes are usually depicted and thought of as static, i.e., not changing in time or space, but in reality they change in response to environmental changes. Populations have different fitness in different environments, so changes in both time and space can influence the fitness landscape. For example, releasing chicken on the moon will drastically change their chances to reproduce.

Many papers have been published about fitness landscapes, but with very few exceptions they investigate static fitness landscapes. Exceptions are landscapes that change between two or three different environmental conditions, such as microbes in salty or acidic conditions.

A consistent criticism of studies that look at evolutionary dynamics – the study of evolving populations – is that the fitness landscape is static, and that this is not realistic. But no one knows to what extent natural fitness landscapes change over time. Both the frequency and magnitude of such changes are completely unknown. On the time-scale of significant evolutionary change, do real fitness landscapes experience changes that make any serious difference to how populations evolve? Do they change qualitatively, with peaks coming in and out of existence? Or are the changes merely quantitative, keeping the rank order of fitnesses the same? The former is a possible solution to the problem of how populations can cross valleys between peaks in the fitness landscape: if a population is stuck on a local peak, just wait until the environment changes and leaves an uphill path to new genotypes and phenotypes. But it could very well be that in most cases most of the time populations are stuck in an approximately static landscape. We really don’t know.

And yet, for all the criticism of studies of static landscapes, not much research has been done on evolution in dynamic fitness landscapes.

One environmental factor that can change the fitness landscape of a population is a population of another species. If one species is in any way dependent on another, then there is a potential for the fitness landscape to depend on the other species.

In the video above we present three such cases of coevolution. (Read details of the simulations here.)

Moth-orchid coevolution. The moth eats nectar from the bottom of the orchid spur. In order to do that, its proboscis needs to be at least as long the orchid’s spur. In this model, the moth therefore gains some fitness if this is true. The more orchids it can feed on, up to a limit, the more fitness it gains. The orchids have a different agenda. They need to get someone to transport pollen from plant to plant so they can be fertilized. The moths can do this for them: when a moth sucks nectar, it touches the male flower parts and some pollen is deposited on the moth, which it carries to the next orchid, where some pollen is deposited on the female flower parts. However, if the moth’s proboscis is longer than the spur, then the moth can suck nectar without coming into touch with pollen. As a result, orchids gain some fitness if their spurs are longer than some or all of the moth’s proboscises. The orchids therefore affect the fitness landscape of the moths, and the moths affect the fitness landscape of the orchids, driving both of them to have longer and longer proboscises/spurs. We visualize this in a two-dimensional phenotype-fitness landscape, where one axis is the proboscis length in the moth landscape (spur length in the orchid landscape), and the other axis is some arbitrary neutral trait that does not affect fitness.

Rock-paper-scissors. The second dynamic fitness landscape is the familiar rock-paper-scissors system. The phenotypes consist of two arbitrary traits, and the three populations are evolving in sympatry, meaning there is no spatial component in the model. Each of the three populations dominate over one of the other two and is inferior to the third. In this model that means that if an organism has the same phenotype as the some members of the population it dominates, then it gains some fitness. The more individual members it has the same phenotype as, the more fitness it gains (density-dependence). Consequently, if this organism has the same phenotype as a member of the population that it is inferior to, then it loses fitness. This system makes the fitness landscape of each population very dynamic, with peaks and valleys appearing and disappearing over time.
Q: Are there any real systems that work like this?

Host-parasite coevolution. The third dynamic fitness landscape is a system with two populations, where the host loses fitness when it shares a phenotype with parasites, and the parasites gain fitness when their phenotypes are the same. The host organism therefore benefits from being different from the parasite, and the parasite benefits from being similar. This results in a situation where the host population evolves away from the parasite phenotype, and the parasite’s population evolves towards the host phenotype. However, it often happens that the parasite population causes the host population to split into two or more subpopulations centered around dissimilar phenotypes. The parasite population will then evolve to climb only one of those peaks, as is always the case when a population of competing organisms is facing two or more peaks. Climbing that peak will cause the host organisms that make up that peak to die out. As a result, the peak disappears, and the parasite population now finds itself dislocated from the surviving host population. Both the host and the parasite populations now have uniform fitness, and they consequently undergo neutral evolution and drifts about in phenotype space. In order to prevent this situation, we have given the parasite population a per-trait mutation rate that is twice as high as the host population. This makes it much less likely that the hosts can escape, because the parasites can now explore a larger area of phenotype space than the host. They move faster around the fitness landscape.

The last model results in two populations that continue to evolve indefinitely. Given enough time they will explore the whole fitness landscape, obtaining all the possible phenotypes. This is arguably open-ended evolution, in that evolution keeps going and populations do not encounter a stopping point. A definition of open-ended evolution requires that the population never reaches a stable phenotype, which in this case it does not. OEE can also be defined to require that new adaptive traits keep appearing, in which case this coevolving system does not qualify. New traits values keep appearing, but after a while they will not be novel, as they will have been attained and then lost in the past.

Some conclusionary words
While these movies are based on actual simulations of a model with two traits, we haven’t really done any science to speak of. Nothing has been measured and no hypotheses have been tested. However, the visualizations could be used as a tool for hypothesis testing and discovery. We can think of videos just as a modern version of the Cartesian coordinate system that enables us to visualize a temporal component (or another spatial component). When populations are seen evolving right in front of your eyes, we can sometimes observe effects that weren’t apparent by any other means.

If you have comments or questions, please go to Pleiotropy.
http://pleiotropy.fieldofscience.com/2014/06/video-visualizing-coevolution-in.html

More about fitness landscapes

Using fitness landscapes to visualize evolution in action

Evolution 101: Fitness Landscapes

Smooth and rugged fitness landscapes 

Crossing valleys in fitness landscapes 

BEACON Researchers at Work: Holey Fitness Landscapes

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BEACON Researchers at Work: Understanding how males and females grow apart

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

Nick TestaBiology: really, it’s all about sex. In this case though, I’m talking about the actual sexes, males and females, and how they are different. Most people can spot the difference between male and female deer pretty quickly. Just look for the antlers, right? Sexually dimorphic traits, those that differ between males and females (like antlers), are incredibly widespread in nature and can have some pretty extreme variation. Most of the time these traits are sexually dimorphic because they convey some sort of benefit to one sex in particular. In this example, the antlers are not only used by male deer as weapons when competing for females, but they also act as a useful signal to females that the male deer is healthy enough to support such a big rack.

To me the coolest aspect of this is that within every species you have two, potentially very different looking creatures generated from essentially the same genome (differing only in the sex chromosome, if at all). Angler fish, for example, are extremely sexually dimorphic. Most people can identify an angler fish, but what most people don’t know is that they are probably just thinking of the female (remember Finding Nemo?). Males are up to an order of magnitude smaller than females by length (orders smaller by weight) and look like some sort of sad, bulbous minnow (Pietsch 2005). Most will eventually find, mate with, and subsequently melt into the female. Both of these creatures are the same species, but look vastly different. It’s truly amazing that the difference of a chromosome can turn a ghastly predator into a mopey parasite. I really like this example because it not only illustrates the incredible variation of sexual dimorphism of body shape, but also of body size.

Female and male anglerfish

Every multi-cellular organism has some quantifiable size and shape, which are often sexually dimorphic. An organism’s size and shape can also influence its ability to produce offspring, escape predators and even appear attractive to the other sex! These qualities make sexual dimorphism a great trait to study. Much research in this field has examined how natural selection might differ between the sexes, leading to evolutionary conflicts. That is, the ideal body size for a male and female might differ substantially, despite their largely shared genome. As for my own work, I am interested in questions involving the underlying developmental, physiological and genetic mechanisms that generate the differences in size and shape between the sexes. Understanding how these mechanisms work can allow us to further our understanding of how sexual dimorphism evolves.

Sexual size dimorphism in Drosophila melanogaster. Females (left) are larger than males (right)

Sexual size dimorphism in Drosophila melanogaster. Females (left) are larger than males (right)

The fruit fly, Drosophila melanogaster, is like most insects with regard to sexual size dimorphism; females are larger than males. In general, final body size is regulated by a combination of developmental factors, including: initial body size (usually size at hatching/birth), growth rate, growth duration and even weight loss before maturation (Testa et al. 2013). Changing any of these individually or in combination results in an alteration of adult body size. It turns out that all of these factors (except growth rate) contribute to size differences between the sexes in the fruit fly.

Size differences in fruit flies, however, are largely due to differences in their metabolic activity. Females grow faster while on food and lose more weight when they wander around looking for a spot to metamorphose. In fact, it appears that sexual size dimorphism depends on available nutrients. By rearing flies using food that varies in nutritional content we get a clear idea of how these nutrients contribute to sexual size dimorphism. We found that adult flies remained sexually size dimorphic until food quality dipped below a certain amount. Any lower and the dimorphism disappears. What’s more, we’ve also found that flies containing a mutant version of the Insulin-receptor gene not only have trouble detecting nutrients (they are effectively starved), but also develop without any sexual size dimorphism, as if they were starved (Testa et al., 2013). These results are particularly interesting for me because it suggests that sexual size dimorphism might be regulated by genetic pathways that regulate growth based on available nutrients.

Using standard genetic methods, I’ve been taking genes in candidate pathways and increasing or decreasing their functional activity to determine which ones alter sexual dimorphism. By both removing and increasing expression of these genes, I will be determining whether each one is necessary to generate sexual size dimorphism and/or sufficient to change it, respectively. Only by showing that a gene is necessary for dimorphism and sufficient to change it do we actually show that it is a causal agent.

While I am primarily interested in sexual dimorphism of whole body size, not all size determining pathways act equally on all parts of the body. Some of the genes in my candidate genetic pathways are influencing relative sex-specific changes in size, i.e. shape. For example, we know that: 1) dimorphism of overall body size in Drosophila is controlled, in part, by the sex determination pathway, 2) this pathway is also responsible for generating the morphological differences in males and females and 3) that Drosophila wings display sexual dimorphism for both size and shape. Examining how genes in these pathways (and those nearby) influence sexual dimorphism for size and shape allows me to assess the degree to which wings are under similar developmental genetic control. Using this sort of analysis I can visualize the effect each gene has on generating sex-specific size versus shape! To me, this is probably the coolest part.

Sexual shape dimorphism in wings taken from a natural population. Size effects have been removed (and shape slightly exaggerated) to demonstrate shape differences.

Sexual shape dimorphism in wings taken from a natural population. Size effects have been removed (and shape slightly exaggerated) to demonstrate shape differences.

Studying developmental mechanisms allows us to answer questions about the ‘how’ of sexual dimorphism’s evolution. How do selective forces translate into the sexual dimorphism we see in nature? How do developmental processes impact the evolution of sexual dimorphism? We know so very little about the mechanisms used to do this. In studying these mechanisms, I hope to not only learn about sexual dimorphism, but about more generalizable phenomena as well. Studying the developmental mechanisms of sexual dimorphism will inform us as to how it is, and can be, generated. More broadly, however, it can inform us about the generation of distinct phenotypes within nearly identical genomes.

References:

Pietsch, T. W. (2005). Dimorphism, parasitism, and sex revisited: modes of reproduction among deep-sea ceratioid anglerfishes (Teleostei: Lophiiformes). Ichthyological Research, 52(3), 207–236. doi:10.1007/s10228-005-0286-2

Testa, N. D., Ghosh, S. M., & Shingleton, A. W. (2013). Sex-Specific Weight Loss Mediates Sexual Size Dimorphism in Drosophila melanogaster. PLoS ONE, 8(3), e58936. doi:10.1371/journal.pone.0058936

For more information about Nick’s work, you can contact him at testanic at msu dot edu. 

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Recap: 2nd Annual Big Data in Biology Symposium

This blog post is by UT Austin graduate students Rayna Harris and April Wright.

It is our pleasure to report back on the 2nd Annual Big Data in Biology Symposium that was held at UT Austin on May 16, 2014. Hosted by UT’s Center for Computational Biology and Bioinformatics (CCBB), this event showcased the cutting-edge research done at The University of Texas at Austin and neighboring institutions that takes advantage of high throughput approaches, complex data, and/or high performance computing. Twenty three of the more than 120 attendees were BEACONites! We hope the BEACON presence at the Symposium will grow even more in the coming years.

The Talks

After everyone had coffee and breakfast bagels, CCBB Director and BEACONite Dr. Hans Hofmann and Dr. Dan Stanzione (Acting Director, Texas Advanced Computing Center) welcomed everyone to the event and shared their vision for Big Data research here at UT and beyond. Dr. Rosalind “Roz” Eggo, a postdoc in Lauren Meyer’s lab (BEACON Lab), presented her compelling research linking asthma to cold transmission and the academic calendar in school age children. Dr. Claudio Casola from Texas A&M discussed permutation methods for detecting interlocus gene conversion. BEACON member Dr. Clause Wilke discussed his research into the biophysical properties of molecular that hinder or accelerate rates of molecular evolution.

The keynote address was supposed to be given by Dr. Pamela Silver from Harvard Medical School; she is a world leader in synthetic and systems biology, but her flight was cancelled due to inclement weather. Fortunately, Dr. Edward Marcotte stepped in at the last minute and delivered an excellent talk on how his lab using large datasets to study the evolution of gene and protein networks and the biomedical implications of his research. Dr. Vishy Iyer presented ENCODE research that used ChIP-seq to detect functionally important SNPs that are linked to disease. Elizabeth Milano, a graduate student in Tom Juenger’s lab, presented her work using ddRAD to identity genes underlying phenotypics traits in different ecotypes of switch grass. Dr. Matt Cowperthwaite, who oversees medical informatics programs at the Texas Advanced Computing Center (TACC), discussed informatic approaches to estimating the mutation rate in untreated Glioblastoma multiforme. Dr. Scott Hunicke-Smith, who directs UT’s Genome Sequencing and Analysis Facility (GSAF, which has supported many a BEACON project!) concluded the symposium with a thanks to all our sponsors, volunteers, and participants for helping making the event a huge success.

The Lunch Breakout Sessions

This year, the symposium offered researchers an opportunity to have small-group discussions with various big data professionals over lunch. These sessions were aimed at helping attendees network with other like-minded researchers and discover resources for different aspects of and opportunities in data science.

The Big Data in Teaching Panel provided an opportunity, for grads, postdocs, and faculty to discuss the challenges and opportunities for designing undergraduate curricula that gives students hands on training in data analysis, interpretations, and statistics. Andy Ellington (BEACONite), Claus Wilke (BEACONite), and Erin Dolan (the newly appointed Director of the Texas Center for Science Discovery and coordinator of the popular Freshman Research Initiative program) sat on the lunch panel. The lunch discussion centered around how to integrate your science research with teaching, learning, and mentoring; what topics modernized syllabi should include; online resources for teaching programming in the classroom (such as Appsoma and Code Academy); and research projects for undergrads. 

The Big Data in Medicine Panel provide the opportunities for trainees and faculty to discuss challenges and opportunities for high-performance computing for the medical community. This panel consisted of Dr. Robert Messing (Vice Provost for Biomedical Sciences), Dr. Matt Cowperthwaite (Texas Advanced Computing Center), Dr. Peter Mueller (Department of Statistics and Data Sciences), and Dr. Bill Rice (St. David’s Heath Care). Discussion between medical panel members and the audience covered topics such as the evolution of medical research, which emphasized the need to integrate larger data sets into this area of study; common obstacles medical researchers face when attempting to work with these data sets; and modern tools available that may help with big biomedical research.

The Big Data in Industry Careers Panel provided an excellent opportunity for undergrads and grads to gain exposure to the wide world of STEM careers for Big Data scientists. Scott Hunicke-Smith (Director, Genome Sequencing and Analysis Facility), William Honea (T-Systems North America), Dr. Krista Ternus (Signature Science), and Dr. Dennis Wylie (Asuragen) lead the discussion. After the Panelists introduced themselves, each described the types of career options and associated salaries for PhD-level “big data scientists” within their respective companies. The industry panel had strong representation in the life sciences, but also provided insight into data science jobs that do not involve biology. Topics of discussion included desirable software skills for students seeking industry positions, adapting curriculum vitae for industry, corporate culture and compensation, and types of roles a PhD might have within industry, individual control of science, and science support of business objectives. The session concluded with a discussion about how to identify job opportunities and network in the realm of “Big Data in biology.”

The Poster Session

Twenty one trainees presented posters on a wide range of topics in diverse disciplines such as ecology, neuroscience, biochemistry, computer science, and molecular biology. Most posters were presented by UT Austin graduate students and postdocs, but two students from UT San Antonio made the drive north to participate. Six attendees participated as poster judges (including two BEACONites: Dr. Jeffrey Barrick and Dr. Rebecca Young of Hans Hofmann’s Lab). Nathan Abell and Amelia Hall from Vishy Iyer’s lab, Alberto Ghezzi for Nigel Atkinson’s lab, and Carly Kenkel from Misha Matz’s lab all received prizes for best poster presentation. Rayna Harris (BEACONite) organized the poster session, selected the judges, and presented the poster awards. All who stayed to hear the poster announcement were entered into a raffle, and two students each won prizes for a free CCBB short course offered in the fall.

The Feedback

The week after the event, over 50% of participants responded to our online survey, and the responses were overwhelmingly positive. Specifically, 80% of survey respondents agreed that the breadth of talks was Excellent/Very Good, 94% said the same about the poster session, and a staggering 97% considered the lunch breakout sessions to be of Excellent/Very Good value! Regarding demographics, 48% of attendees were female, with 54% self-identified as trainees and 38% as PIs or research staff. Finally, 12% of attendees came from industry or from institutions other than UT Austin. Again, we hope to increase the number of outside participants next year, particularly from BEACON institutions.

For more information about the Big Data in Biology Symposium, visit the website at http://www.ccbb.utexas.edu/dataconference.html, follow April on twitter at WritingApril #bdib, or contact Rayna Harris rayna.harris at utexas.edu or Hans Hofmann at hans at utexas.edu.

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