BEACON Researchers at Work: The Invisible Hand of Evolution

This week’s BEACON Researchers at Work blog post is by MSU postdoc Jeff Morris.

IMG_0270As the 18th century dawned over Europe, pretty much everybody believed the world was as it was because of a mysterious divine plan. But during the period known as the Enlightenment, thinkers began working out the mechanics that structured both nature and human society. By century’s end many of the fundamental processes of physics and chemistry had been elucidated. But the greatest leap ahead in the history of biology – the discovery by Charles Darwin of evolution by natural selection – wouldn’t happen until the middle of the 19th century, and it would owe as much to the study of economics as to anything in the preceeding century’s life sciences.

Adam Smith
, an 18th century Scottish philosopher, was struck by how economics obeyed natural-like laws despite the often capricious and irrational behavior of economic actors. Smith envisioned an intrinsic interrelation between producers and consumers that pulled economic behavior toward certain norms as if “led by an invisible hand.” The net result of many financial actors behaving selfishly is a well-regulated, self-organized system: the parts don’t have any intention of working together, but screw up and do it anyway.

Young Charles Darwin was influenced by many, many Enlightenment thinkers, but it’s hard not to notice how similar the self-organization of nature by natural selection is to Smith’s ideas about economics. This Spectator article from 2009 covers Darwin’s economic influences in depth. It also uses one of my favorite weird words, “defenestrate”:

“Ideas evolve by descent with modification, just as bodies do, and Darwin at least partly got this idea from economists, who got it from empirical philosophers. Locke and Newton begat Hume and Voltaire who begat Hutcheson and Smith who begat Malthus and Ricardo who begat Darwin and Wallace. Before Darwin, the supreme example of an undesigned system was Adam Smith’s economy, spontaneously self-ordered through the actions of individuals, rather than ordained by a monarch or a parliament. Where Darwin defenestrated God, Smith had defenestrated government.” – Matt Ridley

Marketplace analogies are common in biology: here are two dealing with microbial evolution. And when applied to human social organization, evolution often draws the same criticisms as free market policies: can “nature red in tooth and claw” produce cooperation and charity, or must we rely on a benevolent dictator to give us those happy institutions?

The chief problem with evolving cooperation is the tragedy of the commons. Briefly, if cooperation has a cost, then a non-cooperator that can still get the benefits of cooperation will always have a fitness advantage over cooperators. Theoretically, this advantage will always exist even if the breakdown of cooperation totally trashes the environment. We know the tragedy doesn’t always happen because we see organisms in nature working together – but how does evolution escape it?

Bill Hamilton proved that self-sacrifice could evolve by natural selection if the recipients of the sacrifice were related to the sacrificer – something we’ve come to call kin selection. In the microbial world, kin selection can happen when microbes live in close physical association with each other. Since microbes reproduce by simple division, as long as they don’t move around they’re likely to be surrounded by close relatives. Therefore, if a cell spends resources to make a product that leaks into the environment, the cells most likely to benefit are also close relatives.

Bacteria living in a spatially structured environment like a seabed (left) are more likely to be related to their neighbors than the same organisms living in open, well-mixed water (right). Classic kin selection can happen in the scenario on the left, but not in the one on the right.

Bacteria living in a spatially structured environment like a seabed (left) are more likely to be related to their neighbors than the same organisms living in open, well-mixed water (right). Classic kin selection can happen in the scenario on the left, but not in the one on the right.

But kin selection can’t explain all the cooperation we observe in the microbial world. In fact, we see tons of it in the open ocean, whose turbulent waters are about as close as you’re likely to get to no spatial structure at all. The oceans are full of cells that are dependent on unrelated cells to make crucial metabolites, or in some cases to clean up environmental toxins. In order to understand how this arrangement evolved, we need to consider two theories:

  1. Streamlining theory arose from work with the omnipresent marine bacterium SAR11 done mostly by Steven Giovannoni’s group at Oregon State. It maintains that there is intense selection pressure on microbes to reduce the sizes of their genomes when nutrients are limiting and populations are large. During this process of gene loss, many cells lose the ability to perform vital functions and become dependent on neighboring cells that have retained those functions.
  2. The Black Queen Hypothesis, or BQH, which I originally proposed in my PhD thesis and then worked out more rigorously in a 2012 mBio paper with Erik Zinser and Richard Lenski, proposes that streamlined cells can get away with losing important functions as long as those functions leak their products into the environment. Like players of the card game Hearts want to get rid of the black Queen of Spades because of her high point cost, streamlining cells want to get rid of leaky functions. However, some cells have to end up holding the Black Queen, because once the leaked products are rare enough, additional streamlining moochers won’t have an advantage. BQH evolution thus produces communities of function-performers, or helpers, and moochers, or beneficiaries. These communities have the appearance of cooperation/altruism, but they arise by normal selfish Darwinian natural selection.

We originally proposed the BQH to explain how the marine photosynthetic bacterium Prochlorococcus had become dependent on its neighbors for protection from hydrogen peroxide, a toxin that is constantly produced in natural waters exposed to sunlight. Because peroxides move freely across cellular membranes, any cell protecting itself by breaking down the peroxides also lowers the environmental concentration of peroxide, unavoidably helping any mooching neighbors. This leakiness makes peroxide detoxification a Black Queen function and explains how Prochlorococcus can get away with not protecting itself.

In order to test the predictions of the BQH, we used a wimpy Prochlorococcus-like E. coli mutant that had all of its anti-peroxide defenses knocked out. We then gave this mutant a plasmid – an accessory piece of DNA – that allows the cells to make the peroxide-destroying enzyme KatG and evolved the resulting strain under strong, peroxide-generating light for 1,200 generations, or about 150 daily transfers of the cultures into fresh batches of growth medium.

Percentage of the evolving E. coli populations retaining the KatG plasmid. Values are means of 3 replicately evolved populations; error bars are standard deviations.

Percentage of the evolving E. coli populations retaining the KatG plasmid. Values are means of 3 replicately evolved populations; error bars are standard deviations.

Even though plasmid-free cells could barely survive on their own under the lights, they nevertheless evolved and stably co-existed with their plasmid-containing ancestors throughout the 1,200 generations (see above). Moreover, while the helpers underwent a number of evolutionary changes, there was no evidence that they were trying to be “stingy” with their production of KatG in order to outcompete the beneficiaries. Just the opposite – the evolved helpers made more KatG than their ancestors.

Family tree of genes that mutated in more than one of the three evolved populations.

Family tree of genes that mutated in more than one of the three evolved populations.

Not only did the two forms coexist, there’s also evidence that they diverged into different “species.” When we sequenced the genomes of helper and beneficiary clones taken from three replicate evolved lines, we found two mutated genes common to all helper lines, and a completely different set of two mutated genes in all beneficiary lines (see above). This indicates that the early choice to become a beneficiary fundamentally changed the adaptive landscape of these cells, meaning different mutations are adaptive for helpers than for beneficiaries. This is a barrier to gene flow, and based on the ecological species concept, these two types represent different species (or more properly for bacteria, ecotypes) – evolved in 150 days under a simple BQH regime!

Starting with a single, clonal population, Black Queen evolution produced an ecosystem with 2 distinct types, one of which was apparently altruistically helping the other. Of course, we know that they aren’t helping because they care about the beneficiaries; they just can’t avoid doing so. Each player is maximizing its own selfish advantage at the other’s expense, but the nature of the leaky function prevents either from winning the game and taking sole control of the evolutionary medium.

The BQH thus acts as an invisible hand stablizing the ecosystem and forcing the two types to play nice with each other. Are there any important differences between this kind of inadvertant cooperation and what we might think of as “intentional” cooperation? Maybe, maybe not. Either way, though, it’s clear that the BQH equilibrium can keep varieties of closely related organisms co-existing for a long time, and, unable to get rid of their pesky partner, it’s possible that other forms of cooperation might evolve in time.

Human attempts to manipulate complex systems, be they economies or ecosystems, are often studies in disaster. The invisible hand of the Black Queen shows us one of many ways that blind nature is often a better engineer than any intelligent designer.

References:

Morris, JJ, SE Papoulis, and RE Lenski. 2014. Coexistence of evolving bacteria stabilized by a shared Black Queen function. Evolution.

Morris, JJ, RE Lenski, and ER Zinser. 2012. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio 3:e00036-12.

Giovannoni, SJ, JC Thrash, and B Temperton. 2014. Implications of streamlining theory for microbial ecology. ISME J 8:1553-1565.

Jeff Morris is a NASA Astrobiology Institute postdoctoral fellow working in Richard Lenski’s lab at Michigan State. He will be starting as an assistant professor in the Biology Department at the University of Alabama in Birmingham in January. Jeff (@ASDarwinist) blogs about science, politics, and heavy metal at antisocialdarwinist.com.

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BEACON Researchers at Work: Genetic and Environmental Basis of Trait Loss, or, How to Lose a Trait: Organismal Spring Cleaning Edition

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

Samuel PerezThe study of morphological traits, physical features that are directly observable and measurable, is important for the study of evolution, and play a central role in Darwin’s theory of natural selection. Organisms interact with their environment using their morphological traits to do tasks like obtain energy from limited resources, avoid predation, maintain homeostasis, and reproduce. The traits involved in these tasks often have differences between individuals (variation) in a population, and some traits have more variation than others. If those morphological differences lead to differences among individuals in the number of offspring, then natural selection results and evolution at the population level can occur; this process is known as adaptation.

However, if a formerly-adaptive trait no longer contributes to the fitness of a population, perhaps due to changing environmental conditions, then what happens to that trait? In that case, there can be two possibilities: either the trait will become reduced and lost as it accumulates mutations and resources are invested in other, more essential features that can contribute to adaptation, or the trait can be modified towards a new function. A striking example of trait loss in animals is the loss of flight, which has occurred in two major groups of animals that have developed flight (insects and birds). Some insects have lost their wings altogether, while penguin wings have been co-opted for swimming.

A.thaliana flower. Note that pollen from the mature short stamen does not reach the pistil (center) part of the flower.  Pic: Jürgen Berger/Max Planck Institute for Developmental Biology, Tübingen, Germany

A.thaliana flower. Note that pollen from the mature short stamen does not reach the pistil (center) part of the flower.
Pic: Jürgen Berger/Max Planck Institute for Developmental Biology, Tübingen, Germany

I’m interested in knowing how much variation is observed in a trait undergoing loss at the population level, the genetic basis of trait loss, why nonfunctional traits are sometimes maintained, and how formerly adaptive traits can be shifted towards new functions (co-option). I study trait loss in the model plant Arabidopsis thaliana (thale cress), of the Brassicaceae family, which has almost 4,000 species and includes species like mustard, cabbage, canola, and radishes. A conserved feature in Brassicaceae is that flowers have four long stamens (male reproductive parts) and two short stamens.

 

However, A. thaliana produces more than 98% of its seeds through self-pollination, and the short stamen anthers, where the pollen is produced, never come near the stigma, where the pollen needs to be deposited to fertilize the seeds (see picture above). Previous experiments by Anne Royer in which short stamens and long stamens were removed showed that the short stamens do not contribute significantly to self seed production, making it an example of a nonfunctional trait. Anne also showed that the short stamens have begun to be lost in some populations of A. thaliana, and this loss exhibits a latitudinal cline in the native European range: stamen loss is common in the Mediterranean but rare in Scandinavia.

A. thaliana flowers with six stamens (left), five stamens (center), and four stamens (right).

A. thaliana flowers with six stamens (left), five stamens (center), and four stamens (right).

Many animal and plant traits that exhibit a latitudinal cline also show an altitudinal cline as well; lower latitudes correspond to low altitudes, and higher latitudes correspond to lower altitudes due to similarities in climate. To see if this is true for stamen loss in A. thaliana, I counted short stamens in 15 natural populations from N.E. Spain that ranged from 109-1688 m in altitude. I found that, in parallel to the latitudinal cline, populations at lower altitudes had more short stamen loss than populations at higher altitudes (see graph below). These parallel latitudinal and altitudinal clines suggest that there is a common source of natural selection at work, either directly on short stamens or through a correlated trait.

Mean short stamen number (±1 SEM) from 15 populations in N.E. Spain. Quadratic fit in red is provided.

Mean short stamen number (±1 SEM) from 15 populations in N.E. Spain. Quadratic fit is provided in red.

One of the advantages of studying lines from multiple populations of Arabidopsis thaliana is more potential for phenotypic and genetic variation across floral traits. Green circle diameter = 1.9 cm.

One of the advantages of studying lines from multiple populations of Arabidopsis thaliana is more potential for phenotypic and genetic variation across floral traits. Green circle diameter = 1.9 cm.

I also measured stamen loss from a series of inbred line crosses between two populations from Sweden and two populations from Spain to look at the genetic basis of stamen loss across the whole genome. We found that there was genetic variation in stamen loss within one of the Italian populations. The F1 generation showed evidence of recessive genes underlying stamen loss, the F2 and backcrosses suggested that there were epistatic interactions between the genes responsible for stamen loss. The gene interactions may be slowing the evolution of stamen loss even when genes have additive effects.

My thesis research will focus on furthering the understanding of the selective and genetic mechanisms underlying stamen loss. I will examine the fitness effects of stamen loss, both direct effects through costs of production of short stamens, as well as correlated traits that may be involved with adaptation to high altitude and latitude. I will conduct genome-wide association mapping studies (GWAS) using the whole-genome sequence data to look for the genes responsible for the latitudinal cline in stamen loss.

For more information about Sam’s work, you can contact him at perezsa4 at msu dot edu.

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BEACON Researchers at Work: Discussing evolution is fruitful: Or, Why I don’t shut up about evolution

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

southAs a woman raised in the South, and now returning to it as I finish my dissertation, I am reminded of a gem I have heard come out of more than one Southern mother’s mouth: “If you can’t say anything nice, don’t say anything at all”

As I always interpreted as “Just don’t be mean,” I found wiggle-room in this statement: If it has to be ‘not mean,’ it could also be neutral. I could say things that described a process that were neither mean nor nice, merely factual.

Taking the approach of neutrality, I’ve had a host of fruitful, meaningful discussions. Talking about things has helped me to uncover the framework of my thoughts, my misconceptions, and my assumptions in real-time. I believe we can grow, individually and as a society, through examining our thoughts, and those of others, through such conversation.

However, despite the benefits of discussion in everyday life, do these benefits extend to classroom discussion, particularly for tricky topics, like evolution?

In the case of evolution, some might shy away from discussing it at all; after all, various misconceptions about evolution run rampant, originate often from first encounters with the topic, and can even be instilled by teachers. Given the additional, resistant religious and political climate toward evolution in certain regions, it can be intimidating for teachers to bring up evolution in the classroom. If you find yourself in that position, I urge you: discuss it anyway.

Not only is the teaching of evolution supported by several national science education standards, tons of evidence also shows classroom discussion to have many benefits. Among them, classroom discussion helps expose students to importance of team work and cooperation, foster the inclusion of under-represented groups, and facilitates knowledge exchange between students. These discussions are helpful in exposing common misconceptions which students may later challenge as a first step in gaining new knowledge. And now, we have evidence to support that discussion (*not* debate) of the science of evolution can be used as a tool to increase student understanding of evolution and experimental biology.

I was privileged to work with Dr. Mark Tran (formerly MSU Zoology; now new faculty at Blue Ash College in Cincinnati- go Mark!) and Dr. Gail Richmond(Teacher Education, MSU) on a project that addressed how upper-level college biology student’s evolution knowledge and misconceptions changed after a discussion-based course. The overall theme of this particular course was physiological adaptation to the environment, thus evolution was critical for students fully understand the course topics.

As a baseline, first we tested students to see what they knew about evolution prior to the discussion course. We asked open-ended questions of varying complexity to test their understanding of evolutionary processes both generally and with respect to the specific topics to be covered in the course.

Then, for the duration of the semester, the class met weekly for 50 minutes. Each class session was designed to foster peer-to-peer dialogue about course topics and related weekly readings. Students met in small groups and convened as a class answer previously constructed discussion questions as led by graduate teaching assistants (TAs) and fellow students (under TA guidance). Finally, at the end of the semester, we re-tested the students.

peacock

An overused, but classic, example

Here’s what we found: Students consistently struggled with adaptation and how it is connected to evolution (namely, adaptation and evolution do not occur in a single organism over its lifetime, and evolution is not always adaptive). These misconceptions were widespread, even among students who *had* previously taken an evolution course (talk about a let-down!). However, improvement after discussion was apparent for certain topics, in particular for students who had yet to take an evolution course.

In short, students increased in their ability to define evolution, as well as to distinguish that populations can evolve without adapting, but not the reverse. Students also showed a greater ability to distinguish between observational and manipulative research methods.

Thus, discussing evolution can be used as an effective tool in evolution education. Our results stress the need for instructors to address their students’ preconceived ideas on evolution and dispel misconceptions at the start of courses, if not at the start of student’s classroom exposure to evolution. Furthermore, our results also show that students found discussions to be intellectually stimulating, and increased their interest levels in science. And, given that students who had not taken an evolution course made greater gains in knowledge (compared to students previously enrolled in an evolution course), discussions on evolution may actually work best for early-career students before they take courses specifically covering evolution.

So, I beg you, discuss evolution with students. Discuss evolution with friends, family—anyone that wants to learn about the science that connects us all. And if you need help, see some of the resources below or ask a friendly BEACONite to help you out. Just drop us a line, and start the discussion. 

For more, see our paper here:

Tran, M.V., Weigel, E.G., and Richmond, G. (2014). Analyzing upper-level undergraduate knowledge of evolutionary processes: Can class discussions help? Journal of College Science Teaching 43(5): 80-90.

Link: http://www.cirtl.net/files/Tran,%20Weigel,%20and%20Richmond%20JCST%202014.pdf

See also these fantastic evolution education materials, including games, comics, cool videos designed to engage students: 

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

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BEACON Researchers at Work: Of Moths and Math

Octavio CamposThis week’s BEACON Researchers at Work blog post is by University of Washington graduate student Octavio Campos.

We can all appreciate the beauty and diversity of flowers. After all, they come in so many different shapes and sizes – not to mention colors – that there’s bound to be something that appeals to everyone’s taste. Even for the dark and broody among us there is the Black bat flower! But although flowers hold strong symbolic meanings for people all over the world, their vast variation in appearance is thought to have been greatly influenced by their need to attract particular species of animals for the purpose of efficient pollination, and therefore efficient reproduction. Moths, bats, bees, and hummingbirds (and even some small rodents), for example, benefit from consuming the energy-rich nectar that many flowers produce while inadvertently transferring pollen between flowers as it sticks to their fuzzy bodies. 

Manduca sexta, a crepuscular hovering hawkmoth with long proboscisIf you really stop to think about it, drinking the nectar from a flower isn’t exactly the easiest way to making a living out there. Some animals can only feed from flowers by hovering in front of them. That by itself is a hugely complicated process that demands an impressive amount of coordination and control. On top of that, some hovering animals such as hawkmoths must be able to insert their proboscis, which is a hollow drinking straw-like appendage that serves as their mouthparts, into a very narrow opening in the flower in order to reach the nectar reservoir. Bear in mind, the proboscis can be from one and a half to three times the length of the animal itself! Imagine holding a rubbery, flexible pole that was 15 feet long and trying to precisely touch the bullseye of a dartboard, and you might begin to appreciate the scope of this challenge. And to make things even worse, many (but not all) hawkmoths do their foraging at night or dusk and dawn, when light levels are extremely low.

Some of the variation in flower shape in nature.  Adapted from http://theseedsite.co.uk/flowershapes.html

Some of the variation in flower shape in nature. Adapted from http://theseedsite.co.uk/flowershapes.html

Given the challenge that hovering animals must face when attempting to feed from flowers, I was interested by whether certain shapes of flower might be able to help the moth find the nectar source. For example, some plant species have flowers whose petals more or less resemble a flat disk, while others have petals that form the shape of a trumpet, funnel, or bowl, and anywhere in between. Intuitively, it might make sense flowers that are more trumpet and funnel-shaped might be able to better guide the long proboscis of hovering hawkmoths toward the nectar reservoir of a flower. After all, the military seems to have come to a similar solution in the context of mid-air refueling of military aircraft. During this process, a fuel hose with a cone-shaped tip is presented by a fuel tanker to another aircraft trailing behind it. The trailing aircraft, seeking to be refueled, will attempt to dock with the fuel hose via a long-thin probe. Having a cone at the tip of the fuel hose effectively makes the tip of the fuel hose bigger, thus providing a larger target for the refueling aircraft to aim for.

Flat disk flowers should be the most difficult to exploit while more "trumpet-shaped" flowers should be easier for moths to exploit

Flat disk flowers should be the most difficult to exploit while more “trumpet-shaped” flowers should be easier for moths to exploit

A trumpet-shaped 3D-printed flower (ABS plastic), with shape parameters specified by a mathematical equation

A trumpet-shaped 3D-printed flower (ABS plastic), with shape parameters specified by a mathematical equation

The trouble is that, as a scientist, I would like a quantitative way in which to investigate flower shape and its supposed affect on pollinator foraging ability. In other words, how can I describe flower shape using numbers instead of phrases such as, “funnel-like” and “disk-like?” The solution that my collaborators and I settle upon was to reduce the vast complexity of floral 3-dimensional shape into as few key “traits” as possible and then describe those traits using a mathematical equation. If you can do that, then you essentially have an equation for numerically specifying any imaginable flower shape that you can think of. And the beauty of such an equation is that 3D printers can be used to make a real-life sculpture of any particular combination of shape “traits” specified by the equation. For example, my flower shape equation can specify four aspects of floral shape: flower length, width at the outer edge of the petals, width of the central nectar reservoir, and, most crucially, the degree of curvature of the petals. If you give me any four numbers, one for each of the four flower traits, then I can use my shape equation and a 3D printer to make a plastic prototype of that hypothetical flower!

Now we’re getting somewhere… 

Image from infrared video of a hawkmoth (lower left) foraging on one of the 16 artificial flowers in this 16-flower array.  There are two distinct flower shapes in this array, 8 of each.

Image from infrared video of a hawkmoth (lower left) foraging on one of the 16 artificial flowers in this 16-flower array. There are two distinct flower shapes in this array, 8 of each.

What all of this allows me to do is to design artificial flowers of varying but precisely defined shapes and then make them using a 3D printer. I then take these artificial flowers and attach tubes filled with sugar water (which is all that flower nectar actually is), and then I expose these flowers to visitation by real pollinators, hawkmoths in this case. By carefully documenting the order in which these flowers are visited and for how long, I can gain insights into how floral shape influences pollinator foraging ability! Ultimately, I hope that my data can be used down the line to investigate how (if at all) animal visitation has influenced the evolution of flower shape diversity throughout the millions of years of flowering plant history, bringing my research back full-circle. Flowers have captivated human senses for m

illennia. We are quite capable of altering many aspects of floral appearance through careful selective breeding. But we aren’t the only “choosy” ones out there. Thanks to my flower shape equation, we can begin to take some initial steps in figuring out how influential the “birds and the bees” are at determining the evolution of floral shape… Time will tell!

For more information about Octavio’s work, you can contact him at eocampos at uw dot edu.

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BEACON Researchers at Work: Notes from the field

This week’s BEACON Researchers at Work post is by MSU graduate student Kenna Lehmann. 

Kenya2Month041It never ceases to amaze me how returning to place after years away results in this dizzying contradiction: so much has changed, but everything feels the same. Four years ago, I was a Research Assistant for the MSU Hyena Project. I lived in a tent and collected behavioral and demographic data every morning and evening for ten months. The data I collected became a small piece of the dataset Professor Holekamp and her research assistants and graduate students have been gathering for 26 years. Four years later, I have returned to Kenya as one of those graduate students and so much has changed. 

I have been mentally preparing for my return to Kenya since I started graduate school in the fall of 2012. With all of that advance thinking, going back to Fisi Camp (fisi is Swahili for hyena) didn’t start feeling real until my supplies started arriving a couple of weeks ago. I will be studying hyena vocal communication and unfortunately, this means I need a LOT of equipment. I need recording equipment to record the hyena’s calls. I need speakers to play the recorded sounds back to the hyenas. Plus, I need all the memory cards, hard drives, and batteries to keep all this equipment running (and I won’t bore you with all the underwear, personal field gear, and charging cords that are necessary for life in camp). 

The three speakers I tested, with a cantelope for scale.

The three speakers I tested, with a cantelope for scale.

The speakers were an adventure all by themselves. I have a lot of requirements and received advice ranging from “Anything will work” to “Nothing will work except custom speakers made by an expert” and “There is no way you will find speakers like that without having it plugged into external power.” I had a few brief panic attacks in the midst of this fiasco. In the end, I purchased three different portable, battery operated speakers and tested all of them out.

The Klipsch speakers (the medium-sized ones in the picture above) ended up being the perfect combination of battery powered, amplitude, and sound clarity. With my back turned to them, it was easy to believe I had a hyena whooping behind me. If I can fool myself, then the hyenas should be fooled too (at least for a little while). 

Once you have devices that run on batteries, you need batteries to go with it. Suddenly, you feel as if you’ve given a mouse a cookie. Now that you have rechargeable batteries, you need a battery charger, and then you need to something to run those chargers. Our solar power in camp isn’t always reliable and we always have a ton of people using it. This made a solar set-up necessary. The last thing I want is to have good weather, no mud, great hyena cooperation, and no background noise, only to find that the recorder batteries have died. I ended up getting a lovely, compact set-up that includes a solar-powered battery and a rugged solar panel. 

On top of my own supplies, there were some other things we needed for camp. Add all this together and you get the craziness that ensued in my living room for a week:

Just half of the boxes that arrived at my house.

Just half of the boxes that arrived at my house.

Two sets of recording equipment, plus their cases.

Two sets of recording equipment, plus their cases.

This is only a portion of the mess. After this I got too embarrassed to take pictures.

This is only a portion of the mess. After this I got too embarrassed to take pictures.

Luckily my roommates are also researchers so they tolerated the mess. Eventually, I packed everything into five very full, very heavy bags. 

And I was ready to fly out!!

Now, I find myself in Kenya, this place I called home for ten months. It still feels like home, but so much has changed! The hyenas are different (although I was surprised to find there were a few I still recognize), the park itself has transformed, and camp and the nearby town have grown considerably. But, the most important difference is the data I am collecting will be critical for my dissertation. I will succeed or fail based on their quality and quantity. 

As I mentioned, my research here focuses on the vocal communication of the spotted hyena (Crocuta crocuta). I hope to help us begin to understand the evolution and the function of vocal communication in the complex social world that hyenas must navigate. No pressure, there. But, in the end, even if the stakes are higher and that crossing I used every day four years ago is now impassable, this tent I am typing from still feels like home. Not all that much has changed.

If you’re interested in reading more blog posts from Kenna and the MSU Hyena Project, check out their blog at msuhyenas.blogspot.com!

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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?

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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 Pl

eiotropy.
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 broad

ly, 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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