BEACON | An NSF Center for the Study of Evolution in Action

BEACON Researchers at Work: Mock Interviews are Nothing to Mock

Posted on March 24, 2014 by Danielle Whittaker

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

As a result of my work as a former Future Academic Scholars in Teaching (FAST) Fellow at Michigan State University, I was invited to participate in the CIRTL (Center for the Integration of Research, Teaching, and Learning) Network Exchange program. This program funds students to conduct campus visits, arranged like mock interviews, at one of the 22 CIRTL institutions across the US. For my exchange, I traveled down to the University of Georgia to give presentations on both my Teaching-As-Research (TAR) project and dissertation research.

Preparing for the mock-interview visit took more work than I’d anticipated. I’d thought that planning the visit during Spring Break was a good idea, however, the weeks leading up to my visit were packed full of pre-break exam grading and a conference—both of which delayed my preparations. I now know that I’m not going to be able to do everything full-speed when I go to interview for jobs, and that if I intend to still accomplish many things prior to a visit, I will need to spread my schedule out and leave about twice as much time as I’d allotted. This is necessary not just in constructing the talks, but in leaving adequate time to practice and revise them based on feedback.

Traveling in right after a conference was tough, but my hosts made me feel more than welcome. I was glad that, although my visit was 3 days long, it began at 11am on the first day with lunch with my TAR host. We had a great conversation about the state of evolution education and what her experiences have been as a new professor, and she was also nice enough to share a few details about the venue where I’d be presenting my TAR research. This helped put me at ease and allowed me to focus on the moment, rather than the stress of the talks.

After a few individual meetings with professors and the opportunity to observe some of the classes taught by biology education faculty, it was finally time to present. My TAR talk was modified from presentations I’d given previously at MSU, with a few additions which described my future directions and changes based on feedback from my lab members and the FAST group. The talk was centered on a model for how we currently teach the genetic basis of evolution and how we might be able to modify it for greater student gains.

I was pleased to be slotted into the normal meeting time for the Biology Education group, which meant I had plenty of people to give quality feedback. Furthermore, a few guests showed up to the group that week, so I was excited to receive feedback from them directly, in addition to the feedback I later got in follow-up, one-on-one meetings and over dinner with faculty and postdocs.

I was somewhat elated that day two involved class observations and meeting with lab groups. I was glad to relax after day one, yet still be able to observe and talk to many people from which I learned a lot. There are many kinds of reforms and research taking place in biology education at UGA, and I hope to bring back some of these to the courses I teach at MSU as early as this semester.

I also had the pleasure of meeting with the graduate students and postdocs in many of the biology labs. They were very hospitable and open with me about their departments, their research, and living in the area. It was nice to be able to compare notes across labs and institutions on the graduate student and postdoc experience.

Finally, I reached day three with just a few meetings and two talks to round out my visit. It was challenging to treat the entire experience like an interview, mostly because I didn’t want to believe interviewing would be this exhausting (Note: snack bars are your *best* friend to keep up!). Nonetheless, on day three, I met with a few faculty and gave two talks: one on my disciplinary research of how stickleback males change how they court with age, and one to the CIRTL leaders meeting at UGA on why these types of exchanges are valuable. I was glad once these talks were over to be able to chat, get feedback, and finally head home from UGA in the late evening.

The CIRTL exchange was a wonderful experience and the preparation required and lessons learned will be invaluable for future job interviews. It was a great exercise in planning, practicing, presenting (both my work and myself) well, and pursuing an academic career. After the effort I spent to prepare my talks and familiarize myself with the work of the people with whom I was meeting, I have a glimpse at what it’s going to be like on the job market, and I’m sure I’ve learned far more than I can even reflect on now.

Thanks so much to everyone who helped to organize, facilitate, and participate in my network exchange, and know I hope to make you proud when job interviews become a reality!

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, careers, job interviews, professional development, teaching | Leave a comment

BEACON Researchers at Work: Gene-phenotype interactions affect speed of adaptation

Posted on March 17, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Idaho graduate student Tyler Hether.

Evolutionary biology has historically approached the genetics of adaptation from two perspectives. From the genetic perspective, focus has been given to population dynamics and functional roles of single genes. From the phenotypic (trait) perspective, quantitative genetics has provided us with a theoretical base and empirical framework to study adaptation of suites of traits simultaneously. At the interface between these two lie interaction networks among genes that affect phenotype—the genotype to phenotype map—and it is the incorporation of a network-level functional view of genetic interactions into models of multivariate phenotypic evolution that represents a new synthesis in biology.

An initial step toward this synthesis is to explore the consequences of simple network motifs on patterns of pleiotropy and epistasis. Imagine a situation where many genes up and down regulate one another. If a mutation were to occur in one of these genes what effect would that mutation have on the entire network? Could the network structure facilitate or impede adaptation?

Recently, Paul Hohenlohe and I set out to identify how network structure can affect multivariate adaptation. Generally, the amount and rate of adaptation is proportional to the amount of existing additive genetic variation in a population. In other words, with more additive genetic variation a population can respond quicker to selection than with less of this variation. When talking about two or more traits we have a matrix of variation and covariation instead of a single metric—the G-matrix—and the structure of this G-matrix is used to predict phenotypic trajectories while populations adapt. The G-matrix is useful in the context of adaptation because it reveals the amount of genetic variation and covariation that can respond to selection. For more detail overview of how the G-matrix can affect adaptation see this useful website. Critical to the G-matrix is new phenotypic variation and covariation that is brought about by new mutations. This phenotypic variation and covariance that arises from new mutation is the “mutational effects” matrix, M-matrix.

So how can network structure affect multivariate adaptation? One of our hypotheses was that mutations that arise in gene networks create predictable levels pleiotropy and epistasis that can affect the rate of adaptation. To test this we looked at six different two gene-two phenotype models that range from each gene regulating itself to both genes regulating the other in a feedback loop. The regulation was modeled using Michaelis-Menten-like dynamics and the phenotypes we were interested in were the rate of gene expression at equilibrium for each gene (see Figure 1A and D, below). Depending on the network structure a mutation that occurs in one gene could have a cascading effect across the network, affecting the expression of both genes and, hence, change both phenotypes.

Figure 1: Pleiotropy and functional epistasis in gene regulatory networks directly affects rates and trajectories of adaptation. For comparison we show two types of interactions: top row (A-C) shows a negative interaction where the upstream blue gene down regulates the downstream red gene; bottom row (D-F) shows a negative feed-back loop between blue and red genes. Panels A & D) The resulting map from genotype to phenotype (equilibrium expression rates) highlights differences between these two models as a result of gene interactions (blue and red lines show phenotypic values for trait x1 and x2, respectively). Panels B & D) M-matrices across phenotypic space shown as ellipses (scaled 2.5X). At the genotypic level, mutations are random with no covariance between traits; however, mutation at the phenotypic level depends on the network model and the location of individuals in trait space. Panels C & F) This variation in M-matrices affects the shape of the G-matrix (ellipses) and adaptation towards new phenotypic optima (blue and red lines track phenotypic means of populations evolving towards blue and red dots, respectively). Parameters: migration rate = 0.001, selection strength = 10000, mutation rate = 0.01, population size = 2000.

For example, Figure 1A shows the genotype to phenotype map for two networks. Blue and red lines show the phenotypic values for the blue and red genes, respectively. Had these genes been independent from one another the blue lines would be perfectly perpendicular to the red lines since each gene only would affect the expression of itself. In the top row the blue gene suppresses the expression of the red gene and, as a result, the contour lines are not perpendicular. In this particular network, if a mutation were to occur that increased the allelic value of the blue gene it would simultaneously increase the blue phenotype while decreasing the red phenotype. We can see this mutational effect in the M-matrices (Figure 1B). Here we show M-matrices graphically using ellipses across 9 points in phenotypic space. Figure 1B highlights the first of two interesting results. That is, there is a negative covariance in the M-matrix (the major axis of variation resembles your computer’s backslash key).

The bottom row of Figure 1 (D-F) shows a more complex network, a negative feedback loop. Though still apparent in the previous example this negative feedback loop highlights the second interesting result we found. That is, the shape and orientation of the major axis of mutation (co)variation changes: it depends on where individuals reside in phenotypic space (Figure 1E). For example, if the population was comprised of individuals on the bottom right corner of Figure 1E they would have negative mutational correlation (similar to the previous example) but if the population was comprised of individuals at the top left corner of the plot their mutations would show a positive mutational correlation.

So mutations in a gene networks can affect the shape of M-matrices. Next we hypothesized that the shape of M-matrices affects how quickly populations respond to selection. We tested this hypothesis by simulating divergent selection. Broadly, we simulated two populations where each population was initially genetically similar and had identical average phenotypes for each gene (black dot in the center Figure 1C and F). We then set each population to new phenotypic optimum (blue dot and red dot). In Figure 1C and F, blue and red lines show the phenotypic trajectories of each population as they evolved toward their respective optimum and G-matrices have been superimposed at generations 50, 100, 500, and 1000. In the first example (Figure 1C) both populations evolved close to their optimum relatively quickly but in the second example (Figure 1F) there is a delay in adaptation for the blue population. Though mutations in the blue population are still occurring, the delay in adaptation is because mutations are occurring in the wrong direction selection is favoring. Indeed, Figure 2 shows the phenotypic trajectories for each population when phenotypic space is rescaled by the M-matrices in the case of the negative feedback loop. In this rescaled space, the distances traveled by each population are comparable
even though the red population traveled about 47% farther than the blue population in phenotypic space (Figure 1F).

Figure 2: Single simulation run for the negative feedback loop drawn in “mutational space”. In phenotypic space each polygon would be a square with equal area. Note that the blue and red dots are equidistant from the starting point in phenotypic space (Figure 1F) but the blue dot appears farther than the red in this rescaled mutational landscape.

This research underscores how simple gene interactions can create complex genotype to phenotype maps and the wiring of such networks can influence the speed at which populations can respond to selection. Though the networks considered here are relatively simple they could be thought of has two genes on opposite ends of a larger yet well connected network. We are currently interested in expanding beyond two genes, allowing individual networks to evolve, and looking for evidence of “evolving M-matrices” in empirical systems.

For more information about Tyler’s work, you can contact him at tyler dot hether at gmail dot com.

Posted in BEACON Researchers at Work | Tagged adaptation, BEACON Researchers at Work, epistasis, gene networks, gene-phenotype maps, mutation, phenotype, pleiotropy | Leave a comment

BEACON Researchers at Work: Evolution makes software adaptive

Posted on March 10, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Chad Byers.

Perhaps it is because 91% of US-based data center professionals checked “Yes” in a recent survey for whether their company had experienced an unplanned data outage in the past 24 months. Or, perhaps it is because the average outage roughly cost $7,900 per minute … and lasted for over 119 minutes. Either way, you have just been charged with the task of designing software that can respond to unanticipated conditions and changing user objectives, online at run time, and as quickly as possible because as they say, “time is [truly] money.”

The vision of software systems self-adapting and self-reconfiguring in response to adverse conditions has made steady progress from the realm of wishful thinking to realization in modern-day society as computing continues to become more pervasive. Smart energy grids, telecommunication systems, smart traffic systems and similar emerging applications necessitate the deployment of dynamically adaptive systems (DASs) to cope with the various forms of uncertainty these applications bring with them.

The question is: how does one build these self-* characteristics (self-adaptive, self-managing, self-healing, self-optimizing, etc.) into our software systems? One common approach, albeit not necessarily a good approach, is to try and consider every possible scenario that your system might encounter and design a set of strategies to address them. However, this prescriptive approach is often not responsive enough (e.g., 119+ minutes) and there will likely be scenarios that “slip through the cracks” of the developer’s mind. An alternative approach is to take a cue from a natural process that has produced solutions well-adapted to their environment for billions of years, evolution. Rather than preloading our software system with only a set of static reconfigurations that it can switch between, why not embed the process that is capable of generating new reconfigurations? This is precisely the research I have been focusing on here at MSU with Dr. Betty Cheng, namely, mitigating uncertainty by harnessing evolutionary search within DASs.

Recently, we have been investigating the role that genetic algorithms play in coping with uncertainty in an industrial DAS application for remote data mirroring. Remote data mirroring is a safeguard that many businesses use to protect critical data by storing remote copies at one or more secondary sites (mirrors) across a network. In the case of a site outage or a failed network link, the system must quickly reconfigure so that data can continue to be accessed while minimizing revenue loss. However, there are many competing trade-offs to consider among solutions such as their (1) cost, (2) performance in effectively distributing data, and (3) reliability in the face of new adverse conditions. It is too costly and time-consuming to produce these solutions by hand and easily prone to human error. Instead, we represent the free variables of the network in a digital encoding (“DNA”) and allow the evolutionary processes of crossover and mutation to produce new network configurations. Using current monitoring information about the network, the genetic algorithm aims to return solutions that match the user’s desired network qualities. As new environmental conditions arise or the company’s needs change, this process is repeated and has been demonstrated to return successful solutions to within minutes. Our future collaborative work with Dr. Kalyanmoy Deb aims to explore how to further mitigate uncertainty through the generation of a *diverse* Pareto-optimal suite of solutions.

So back to the initial question: how do you design a system that can dynamically respond to changing conditions and objectives as quickly as possible at run time? Evolve it!

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

Posted in BEACON Researchers at Work | Tagged BEACON Researchers at Work, evolutionary algorithms, Evolutionary Applications, genetic algorithms, industry, software engineering | Leave a comment

BEACON Researchers at Work: Constructing models for gene regulatory networks

Posted on March 3, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by North Carolina A&T graduate student Mina Moradi Kordmahalleh.

The idea of bioinformatics and how an electrical engineer can work on this topic is quite new and interesting to me. In Iran, I received my bachelor and master’s degrees in Electrical Engineering specializing in the control field. At the beginning of my PhD program, I became familiar with a fascinating area of study known as Gene Regulatory Network (GRN) through my investigation of researchers who have backgrounds in the control field and working on bioinformatics. With my background in data mining, control, and bio inspired algorithms, I was motivated to work in GRN.

In biological terms, a gene regulatory network is a collection of DNA segments in a cell, which interact with each other and other substances in the cell. Inferences of GRNs are key to understanding the fundamental cellular processes and revealing the underlying relations among genes.

A general representation of a simple gene regulatory network. The nodes in the network represent genes, with the arrow depicting gene activation, and gene inhibition represented with a blunt arrow. Nodes without links indicate no gene interaction.

The ability to model GRNs more accurately may improve medical diagnoses, disease treatment, and drug design. It also helps to understand how drugs affect gene dynamics and which genes are most involved in a process. With the availability of temporal gene expression data in certain biological conditions, different computational methods aim to reconstruct regulatory roles of genes to address biological problems.

The selection of modeling approaches depends on the type and amount of available data, while taking into consideration challenges like high dimensionality, temporal dynamics and measurement noise. The most common mathematical modeling technique for GRNs are ordinary differential equations, Boolean networks, Petri nets, Bayesian networks, Stochastic, and graphical Gaussian models. Different techniques have been proposed to generate models of GRNs to explain a set of time series observations and to predict the behaviors of the components. However, the methods for design and analysis of GRN are in early stages.

In order to clearly show the complexity of the gene network a living cell has been compared to a complex factory, which employs molecular Nano machines while genes and other components of the factory are changing and evolving over time. Factors for these changes include environmental conditions like pH or temperature, maturity stage of an organism where a younger organism may be different from an older one, response to stress like UV irradiation or chemical toxin, and even a disease such as cancer, which all may change the level of protein expression.

The focus of our current work is to address issues such as time lag embedded in the network, external stimuli, and dynamical functioning of the GRN in designing the models. The Artificial Neural Network (ANN) previously was used in constructing the model of GRN because of its ability to handle noisy data and to cover higher order correlations. In this type of network, each node is associated with a particular gene, where the value of the node is the corresponding gene expression. Connections between nodes represent regulatory interaction between genes under a certain weight, which indicates the strength of the connection. Finally, through the training of the ANN, the dynamic of the GRN is constructed based on nonlinear relations among nodes.

Through the effectiveness of ANN in modeling GRN, our current work involves the prediction of the gene expression levels in a gene regulatory network by a new generation of partially connected ANN with evolvable topology. We assume that the observed measurements of genes may depend on some hidden variables. Through the evolutionary process, these hidden variables are firstly able to play the role of memory in order to determine the necessary non-uniform time lag in modeling; and secondly they can play the role of internal states of the biological system such as regulating proteins, excluded genes in the experiments, external signals, and biological noise to represent the latent variables.

Moreover, we assume that partially connected structures exist between nodes, which is consistent with the biological assumption that the genetic networks are usually sparsely connected. Networks can evolve in different ways such as the addition or subtraction of nodes (genes) or parts of the network or by changing the strength of interactions between nodes. In this scheme, the prediction error between actual gene expression data and the estimated ones during the training process guides the search space to discover the network topology with sufficient accuracy on constructing the GRN. Evolution of network topology continues until determining the best fitting model of the GRN, which represents the underlying dynamic behaviors of the genes.

For more information about Mina’s work, you can contact her at mmoradik at aggies dot ncat dot edu.

Posted in BEACON Researchers at Work | Tagged bioinformatics, data mining, gene regulatory networks, genetic algorithms, neural networks | Leave a comment

BEACON Researchers at Work: Studying drug resistance in bacteria

Posted on February 24, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Idaho postdoc Silvia Smith.

Humans, like any other organism, impact their environment as their natural history unfolds. As the result of selection for increased brain size and improved cognitive abilities, we developed a variety of cultural adaptations, the most remarkable (both negatively or positively) being the domestication of animal and plants, starting about 10,000 years ago in the Fertile Crescent. This set of cultural adaptations spread across the sparse nomadic human populations, actually evolving independently in several locations, and resulted in the availability of a constant food supply, which in turn supported an increase in human population size and density. This major shift in subsistence patterns and our newly imposed selective pressures during the process of domestication have resulted in large-scale changes in the target animal and plant species, but also in our pathogens. In fact, as population density increased, so did the likelihood of sustaining and transmitting infectious disease, a shift described as the first epidemiological transition by Armelagos and colleagues (1996). The adoption of domestication also resulted in an increase in the human life span, which was accompanied by an increased likelihood of developing chronic disease; this is also known as the second epidemiological transition. Since the turn of the century, when antibiotic were first discovered, we have seen the emergence of multi-drug resistant pathogens. This is the result of rapid artificial (i.e., human-imposed) selection on organisms with short generation (or doubling) time like bacteria and viruses, and is referred to as the third epidemiological transition.

As a biological anthropologist interested in various aspects of human health and infectious disease, I conducted my dissertation work on the coevolutionary relationships that exist between mycobacteria (among which are the causative pathogens of tuberculosis) and their human host species. I subsequently took a postdoctoral fellowship at the University of Utah School of Medicine, where I employed evolutionary theory, molecular genetics, and systems biology methods and theory to study differential susceptibility to complex systemic disease affecting the eye.

Agar plate showing a culture of the human pathogen Acinetobacter baumannii ATCC 17978 with fluorescently tagged plasmid pB10::gfp. We use fluorescence as a proxy to detect the presence of multidrug-resistance plasmids. Acinetobacter is a remarkable medical concern as it is responsible for an increasingly high number of multi-drug resistant infections worldwide.

I am currently a Postdoctoral Research Scientist at the University of Idaho, Department of Biological Sciences and Institute for Bioinformatics and Evolutionary Studies, where I am fortunate to work with Dr. Eva Top on a project funded by the Department of Defense. The main goal of this project is to characterize the evolutionary pathways involved in the coevolution of broad-host-range (BHR) multi-drug resistance (MDR) plasmids and some of their human pathogenic bacterial host species. More specifically, we are interested in assessing if and how MDR plasmids can improve their persistence in biofilms formed by various Gram-negative bacteria, and compare their persistence patterns to those characterizing liquid bacterial cultures. It is in fact the spatially structured biofilm environment that characterizes bacterial infections in (war) wounds, and we postulate may promote the maintenance of MDR plasmids, and thus the persistence of drug resistance genes. This project involves both experimental bacterial evolution as well as genomics analyses of coevolved host-plasmid pairs, and is ongoing.

For more information about Silvia’s work, you can contact her at silvia dot anthro at gmail dot com.

Posted in BEACON Researchers at Work | Tagged antibiotic resistance, bacteria, drug resistance, experimental evolution | Leave a comment

BEACON Researchers at Work: Coping with Variable Environments

Posted on February 17, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Colin Kremer.

Imagine for a moment that you are a plant, animal, or microbe. Chances are good that the environment you live in (desert, forest, grassland, lake, even the ocean) regularly changes in ways that are important to your survival. Factors such as the amount of food you have access to, the temperatures and weather you experience, or how many predators are trying to eat you, all typically fluctuate through time. This is particularly common in temperate zones with distinct seasons (such as Michigan, where I live and work – although winter seems endless right now!).

Many organisms have specific traits, or adaptations, that enable them to do well (or maximize their fitness) under particular conditions. If their environment changes, these adaptations may not be beneficial anymore, decreasing their fitness. By creating times when species with different adaptations each do well, environmental variation can allow competing species to coexist, promoting biological diversity.

In my research, I use mathematical models and data to study the strategies species use to deal with variable environments, and how these strategies evolve and affect the ability of species to coexist. These questions fascinate me because they provide insight into how biological diversity is generated and maintained, and why species are active and doing different things at different times (For example, why do flowers bloom at different times during the year?). Understanding the consequences of environmental variation is also important for applied reasons: humans are changing ecosystems dramatically across the world. In some cases these effects reduce variability, as we suppress wildfires or control river flooding using dams. They can also increase variability: climate change is affecting temperature variability in addition to driving increases in average temperatures.

What strategies can you think of for dealing with a changing environment? The process of evolution has solved this problem many times, and a few strategies turn out to be particularly common:

Specialize, and avoid the bad times. When the going gets rough, many species either go dormant, or get out of town, migrating to a better environment. To avoid hard times, many animals hibernate or form resting stages, conserving their energy; plants produce seeds that can survive drought and freezing, sometimes for hundreds of years; deciduous trees lose their leaves over winter, and so on. Other species migrate, tracking favorable conditions, such as geese heading south for the winter. With this approach, species specialize on doing well under particular conditions, and avoid stressful conditions they are not adapted to.

Be a generalist. As an alternative to specializing and doing really well under specific conditions, some species focus on just doing okay over a wide range of conditions. There are two major ways of being a generalist. First, species may maintain many different traits or adaptations for surviving a variety of conditions. Like the boy scouts, this strategies involves ‘being prepared’, except instead of a giant backpack full of supplies and gear, organisms have larger genomes, or more diverse collections of proteins, or a variety of physiological adaptations.

A second way to be generalist is to change or adapt in response to changing conditions. For example, trees can produce different kinds of leaves that are better at capturing light or conserving water, depending on whether they are shaded or experiencing drought. These responses can happen at the level of an individual, without requiring a genetic change, a strategy called plasticity. Alternatively, natural selection can act on variation in important traits, when individuals with beneficial traits pass on those traits to their offspring. This allows species improve their fitness through evolution. Both of these responses take time (although plasticity is faster than evolution) and can be costly. If changes occur quickly or unpredictably, plastic or evolutionary responses may be too slow, leaving organisms in a constant state of mal-adaptation.

To wrap up this post, I’d like to share two examples of how I’ve studied these ideas and strategies in my own research.

Fluctuating resources and phytoplankton competition. Phytoplankton are small, unicellular organisms that live in water and depend on nutrients from their surroundings and light from the sun to grow and photosynthesize. In temperate lakes, with strong seasonal cycles, annual pulses of nutrients are common, as strong mixing in the spring distributes nutrients from the bottom of the lake throughout the water column. Light levels also fluctuate, both seasonally and daily. Variation in the availability of critical resources (nutrients, and light) can allow different phytoplankton species to coexist, even if they compete for the same resources. This can occur if some species are good at growing quickly when resources are plentiful, but bad at competing for resources when they are scare. These species do well right after a pulse of nutrients occurs, only to get out-competed later in the season by slow-growing, highly competitive species. The tradeoff between these specialized strategies, combined with varying nutrient supply, creates times of the year when each kind does well, allowing them to coexist.

Over the next 100 years, as oceans warm due to climate change, the distribution of many phytoplankton species will change; these species will disappear from some regions (in red), expand into new areas (in blue), and persist in others (in gray).

Using mathematical models, I found out that coexistence is only possible given intermediate amounts of variation – when environments are too constant, or too variable, only one specialist’s strategy remains viable. Also, coexisting specialists can lose out to a generalist species, if it is able to change quickly from being a fast grower to a good competitor in response to nutrient.

Ocean temperatures and our changing climate. Phytoplankton also inhabit the oceans, where they play a critical role as the foundation of most food webs, and drive global cycles of carbon, nitrogen, and other elements. In collaboration with another BEACON scientist, Mridul Thomas, I’ve studied how ocean temperature determines which phytoplankton species inhabit different parts of the ocean. We’re particularly interested in exploring how phytoplankton communities will respond to climate change, which is increasing ocean temperatures and changing their variability. A critical piece of this puzzle is figuring out how quickly the thermal preferences of phytoplankton can evolve. You can read more about this work in Mridul’s previous BEACON blog post, or check out a summary of the paper we published on our research.

For more information about my work, you can contact me at kremerco at msu dot edu or visit my website.

Posted in BEACON Researchers at Work | Tagged adaptation, BEACON Researchers at Work, Biological Evolution, climate change, phytoplankton, seasonality | Leave a comment

Sharing your passion for science with the world through reddit: Interview with Unidan

Posted on February 12, 2014 by Danielle Whittaker

Reposted from MSU graduate student Randal Olson’s blog.

Ben Eisenkop, a.k.a. Unidan, is always excited about science
Photo c/o Ben Eisenkop

For the third and final interview in this series of posts about science outreach on reddit, I’m interviewing a “reddit celebrity” who became famous for sharing his passion for science with the rest of the world through reddit. If you missed the previous interviews, I’ve already interviewed a postdoc who gave a reddit IAmA and a professor who runs reddit’s AskScience forum. Hopefully I’ve convinced you by now that it’s more than worth your time to engage in science outreach through reddit, but if not, give this post a read and see if it floats your boat.

Today I’m interviewing reddit user Unidan, affectionately known to reddit users as “The Excited Biologist.” Outside of reddit, Unidan is known as Ben Eisenkop, and works at a public university as a graduate instructor. When he isn’t teaching students at his university or imparting random bits of knowledge to the public on reddit, Ben works as an ecosystem ecologist, primarily working with wild bird populations.

“Biologist here!”

Ben became famous on reddit when he started showing up in random conversation threads and imparting tidbits of scientific knowledge related to the conversation. He’d always start the comment with the phrase, “Biologist here!,” then proceed to go into excessive detail about the most random facts. Here are a few examples: [1] [2] [3].

One of the classic responses that earned Ben the nickname, “The Excited Biologist”

Ben’s enthusiasm and scholarly tone won over the hearts and minds of reddit’s users, who demanded that he run an IAmA session so they could ask him everything they want to know about science. That IAmA ended up becoming one of the most successful IAmAs on reddit to date and ran for over 5 months, reaching millions of users and inspiring them to learn more about science.

Whenever someone has a random science question on reddit, inevitably someone will begin chanting “/u/Unidan, /u/Unidan, /u/Unidan!” in hopes of summoning him to the thread to share his wisdom. In return for his time and knowledge, reddit users shower him with gifts and adulation. There’s even talk of Ben running a nature-focused YouTube series backed by reddit. Doesn’t this sound like the kind of interaction you’d love to have when engaging with the public?

Below is my interview with Ben. My questions are bolded.

Interview with Ben Eisenkop (Unidan)

What motivated you to use reddit as a science outreach tool?

Well, I started using Reddit more as a time-wasting device, actually, like most people do! It wasn’t until I started releasing little tidbits from my day or things that I’ve accumulated in my work that people began to really take interest. Eventually, I was asked to do an AMA which took off while I was asleep, and I woke up to quite a myriad of questions!

How do you use reddit to communicate science to the public?

I try to be very conversational. In my opinion, Reddit’s semi-anonymity is perfect for straining out bad questions, but also letting people ask incredibly honest ones that even my real students would be very reluctant to ask, due to how they may be perceived by others. For me, that lets me answer some very fun questions and look at problems from angles that I’ve never considered before, especially when I get to speak to people outside of the biological sciences.

What are the pros and cons of using reddit as a science outreach tool?

The pros are that I can really respond at my leisure, and I can gather myself up pretty nicely and revise exactly what I want to say and convey instead of having to do it immediately on the spot. I respond, in my own opinion, pretty quickly, regardless, but it’s still nice to be able to fact-check myself before I give someone advice, and it’s also great because I can converse with people for months or sometimes years in sparse little pieces all over the globe! It’s been incredible to talk to people with such a wide variety of experiences and backgrounds.

What was your best experience engaging with the public on reddit? Have you had any negative experiences while engaging with the public on reddit?

My AMA was extremely positive, and went on for six months straight! I’d say most people that I interact with are incredibly positive, and I’ve even managed to change the minds of a few of the bad ones. People’s opinions of me are certainly normally distributed though, as there’s always people on the extreme negative side, too: I’ve gotten death threats, things sent to my actual address, etc. I don’t worry too much, though, I figure most of that type of stuff are just pranks gone a bit too far!

What other science outreach programs have you participated in? How would you compare science outreach on reddit to other science outreach programs you have participated in?

I’ve done talks at schools from time to time, and symposium visits within academia, of course. I was recently invited to go to SUNY ESF and Syracuse University and was able to convince a colleague of mine to help me bring a falcon with us and, together, we gave a talk on the ecology of birds, which was a lot of fun! Here’s a video of the falcon from the talk that day. Comparing the experiences is a big difference, people commend me for answering a wide variety of questions, but honestly I think answering extremely technical questions from members in your own field is much more difficult. I’ve gotten some really intense questions from other biogeochemists in person, and those are the real thinkers, instead of the more trivial type of questions I’m often asked on Reddit. At least online, I

’m not often asked to solve difficult chemistry equations!

What was it like giving a reddit AMA as a scientist?

It was a ton of fun, I had a blast! Near the end, I felt bad because I was often directing people to incredibly long diatribes I had already written as many of the questions were beginning to repeat themselves, but there was a certain sense of accomplishment that came with helping a lot of younger people find direction and solve specific problems. One of the funniest things was the massive outpouring of homework questions that came via PMs on Reddit, I’ve never done private tutoring, but now I feel like I can list it on a resume, haha!

Do you have any advice for scientists interested in engaging with the public through reddit?

Never talk down to people, always give them the benefit of the doubt and talk to the height of your intelligence. That’s not to say be incredibly verbose or overly didactic (like using the words “verbose” and “didactic”), but rather, assume your audience is able to follow along. If people feel like they’re being talked down to by some “knowledgeable person,” they’ll tune out, regardless of the message. And if you’re wrong about something: admit it! It happens to me all the time. I’m probably wrong about something right now! It’s nothing personal, and admitting it doesn’t make you less of anything, in my opinion, if anything, it makes you much more credible.

Want to share your knowledge with the world?

It takes less than a minute to sign up for reddit. Conversations about science happen every day on reddit, and are perfect opportunities to practice how you communicate science and share your knowledge with the world. What are you waiting for? Give it a shot!

Posted in Education | Tagged Outreach, reddit, science communication | Leave a comment

Answering people’s pressing science questions on reddit: Interview with Tobias Landberg

Posted on February 10, 2014 by Danielle Whittaker

Reposted from MSU graduate student Randal Olson’s blog.

Tobias Landberg is one of a handful of scientists that run the million-user subreddit, /r/AskScience
Photo c/o Tobias Landberg

This post is the second in a series of posts where I am interviewing scientists who do science outreach to the public on reddit. My goal here is to discuss these scientist’s experiences to give everyone a taste of what science outreach is like on reddit. If you’re still on the fence about whether reddit is a good place to do science outreach, give these interviews a read and see if it suits your fancy.

My second interview is with Tobias Landberg, an Assistant Professor of Biology at Arcadia University. Tobias holds an undergraduate and master’s degree in Organismal Biology from the University of Massachusetts Amherst, and a Ph.D. from the University of Connecticut. Prior to becoming a professor, Tobias worked at two postdoctoral positions: One at Boston University / the Smithsonian Tropical Research Institute in Panama, and another at the Watershed Studies Institute at Murray State University. Tobias has been moderating and answering questions on reddit’s AskScience forum for over three years now, drawing on his broad research experiences to educate the public about science.

/r/AskScience

AskScience is one of reddit’s default forums, meaning that any new reddit user automatically subscribes to the forum. Currently, there are over 1.3 million users that subscribe to and read posts on AskScience on any given day. AskScience is unlike any of the other default reddit forums because it’s dedicated solely to asking science-related questions.

On any given day, you’ll see a handful of thoughtful questions on the front page of AskScience

Users vote the questions up or down depending on how good they think the question are. Good questions float to the top of the page, while inane questions sink to the bottom. This means that the community conveniently sorts out the questions that they are most eager to hear the answers for.

A typical question on AskScience

In any given thread, scientists and AskScience panelists offer answers to the question and link the readers to scientific articles if they want to read more. Just like with the questions, users also vote up or down on the answers, so the best answers usually float to the top. With just a few minutes of your time, you can teach science to thousands of curious people — and have a permanent record of it, to boot!

Below is my interview with Tobias. My questions are bolded.

Interview with Tobias

What motivated you to use reddit as a science outreach tool?

Reddit is a fun place to goof off, but being a science nerd, I find lots of interesting science articles and images. Three years ago I found /r/AskScience, a subreddit dedicated to answering scientific questions. It is very satisfying to be a panelist there and be able to have dialog with the interested curious people asking questions about the stuff I love to think and talk about. The diverse community of panelists who answer questions, scientists from around the world, have grown to be colleagues. We share a common interest in education and outreach, and we even have lab meetings and discuss science, commiserate and share articles, skills, knowledge and excitement for science. There’s also been a fair amount of collaborations struck up. It’s learning from these colleagues as well as mentoring young scientists that has kept me motivated.

How do you use reddit to communicate science to the public?

I’ve been a moderator of AskScience for three years and we have built an amazing community that is reaching 3 million unique households with over 10 million page views per month! But even with all this traffic, we have a very personal relationship with our readers. People submit questions and we answer them. It’s kind of simple, but what sets AskScience apart is the way we moderate the site with the help of the community. We have a team of dedicated, generous and skilled people who are cleaning up threads and deleting distracting responses. Our community helps tremendously by upvoting the best answers and downvoting or flagging problematic ones. Behind the scenes we have constant discussions about how to best serve our community and developing new ways to improve the site, reach more people, increase the signal to noise ratio.

What are the pros and cons of using reddit as a science outreach tool?

The pros are that it’s fast easy and you can reach a wide audience. For those of us who study the science behind the things that people wonder about a lot, the possibilities are enormous. I like that you can do it whenever you want for as long as you want. Just show up and answer questions and engage in dialog. The down sides are that some panelists get burnt out answering similar questions that come up frequently. Other panelists don’t get enough questions in their field or specialty. And you need to have patience and pedagogy. It can also be frustrating to see answers that sound right and get upvoted but aren’t accurate. The moderators put a lot of energy into solving these problems and keeping up with our tremendous growth and traffic.

What was your best experience engaging with the public on reddit? Have you had any negative experiences while engaging with the public on reddit? If so, please elaborate.

Teaching people how to ask a good question is one of my favorites. Lots of questions come in that are unanswerable because they aren’t scientific. Giving someone an answer to a question is satisfying but teaching them to think scientifically is even better. Learning how people think, about misconceptions and what they find interesting has proven invaluable to me and helps me communicate broadly as well as to connect with my students. It’s just a pleasure to have life-long learners seek us out and learn through dialog, not only what they thought they didn’t understand, but new things. We’ve had teachers post questions from primary school kids and those have been especially memorable.

Honestly I can’t think of a really negative interaction I’ve had with the public. Rarely people get upset because their posts get deleted, but we have an incred

ible amount of support from our community who appreciate the hard work we do answering questions, doing research, and cleaning up the comments. Unlike on many other sites, trolls get weeded out of AskScience very quickly. For example, we don’t have creationists harassing evolutionary biologists because they get no traction on our site; they are outnumbered, downvoted, reported and anti-science comments are quickly removed. Recently sites like Popular Science have shut down their comments section because research has shown that internet trolls yelling negatively impacts learning. But we’ve solved that problem in a way I believe is unique on the internet.

What other science outreach programs have you participated in? How would you compare science outreach on reddit to other science outreach programs you have participated in?

I’m collaborating with the National Geographic Society, the Mystic Aquarium and the Mill River Conservancy to learn about snapping turtles in Connecticut. We have successfully deployed the NGS crittercam on lots of snappers over the past six years and learned things only snappers knew before now. This collaboration has educated lots of kids, allowed me to mentor many students, publish a paper and has garnered a lot of media attention. We even helped get some legislation passed to protect snappers. This work is logistically very expensive and difficult and slow. It’s tremendously rewarding and successful but we haven’t had grant support and can only outfit one turtle at a time with a camera that collects eight hours of footage. It takes dozens of people and dozens of hours of work for every deployment. Reddit is the opposite. Show up any time you want, stay for five minutes or five hours: no commitment, no organization, just reach out to potentially hundreds of thousands of people per day. I get different things out of these two outreach efforts and they are both very valuable to me.

Do you have any advice for scientists interested in engaging with the public through reddit?

It’s easy. Come to /r/AskScience and check out the breadth of questions and the depth of answers and dialog. Sign up for a username and start answering questions. Once you’ve gotten the hang of it, apply to be a panelist and get a fancy colored tag that describes your field and specialty. If you have any questions or concerns, message the moderators and we will help you. It’s a great place to have fun and do good while goofing off.

Could science outreach on reddit help your career?

As someone who just landed a tenure track faculty position, I found having AskScience on my CV was really valuable. Several people asked about it on interviews and this type of education, outreach and broader impacts is increasingly important to granting agencies, hiring, and tenure & promotion committees.

Any parting words?

If you want to challenge yourself to be an educator, learn new things and interact with the public, check out AskScience. We are growing rapidly and need the help of scientists. Big things are happening and we are heading into exciting new territories. We have just announced a new crowdsourced funding initiative open to panelists which will give our readers a new level of interaction with our panelists. Join us!

Have an hour to spare?

If you have some spare time and want to impart your knowledge to thousands of curious people across the world, come see if anyone at /r/AskScience is asking a question you can answer. Make sure to read theirposting guidelines first, though!

Posted in Education | Tagged Outreach, reddit, science communication | Leave a comment

BEACON Researchers at Work: The more things change, the more they stay the same

Posted on February 3, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by Michigan State University graduate student Austin Dreyer.

Variation is one of the most obvious themes in biology. From variation between taxonomic groups, morphological traits, behavioral responses, habitats, and so on, we are constantly bombarded by the presence and importance of variability in the world around us. An oftentimes less obvious component of biology, however, is how populations maintain an optimal trait value in the face of inevitable environmental and genetic disturbance. Answering this question has been the focus of research for decades, and has caught my attention as well. Indeed, the first time I really considered how any sort of constancy in phenotype is achieved through time given how unpredictable nature can be I was surprised I had not recognized this impressive feat sooner.

One of the pioneering ideas to describe how phenotypic constancy occurs comes from C.H. Waddington in the early part of the 20^th century. His conceptualization of phenotypes revolved around developmental ‘channels’ being formed through generations of selection for a consistent trait value. Waddington postulated that the deeper those channels became through repeated selection, the more resistant the trait was to genetic or environmental perturbations. He termed this phenomenon ‘canalization’ in direct reference to the channels of development that helped to ensure beneficial traits were reproduced with a high degree of fidelity. Since Waddington’s introduction of this concept, much research has been done on the topic, and it is likely no surprise that there is considerable contention regarding what the meaning of canalization really is and how it arises. For the purposes of this discussion, however, I stick to a broad definition of canalization as developmental buffering, or, any mechanism that reduces the effect of environmental and genetic variation on a trait.

The interest in canalization over the years encompasses not only how it has evolved, but also its potential as a driver of evolutionary divergence. Given a working definition of canalization as developmental buffering from environmental and genetic effects, it is possible for genetic variation to accumulate under the umbrella of a trait’s development being canalized. This cryptic genetic variation can then be exposed given a large enough environmental perturbation, potentially acting as a source of novel phenotypes. It is a bit counterintuitive that a process defined as reducing phenotypic variance could contribute to the introduction of new variance, but the buildup of genetic variation made possible by canalization is thought to do just that. Additionally, canalization is often divided into two types, environmental and genetic, and there are numerous mechanisms through which canalization is thought to occur including basic genetic principles such as dominance, molecular chaperones, a result of the complexity of gene networks and due to components of developmental pathways. While much has been studied, clearly much research remains to be done.

My research focuses on looking for canalization mechanisms that are intrinsic to the developmental pathways they occur in, that is, canalization of a trait as a result of the composition of the pathways giving rise to that trait. Specifically, I am looking at canalization mechanisms using the Avida system in collaboration with Carlos Anderson and the model organism Drosophila melanogaster.

Genetic canalization test using Avida. Colored lines represent the mutation rate each population evolved under. Populations exposed to higher mutation rates were more fit than the ancestor, a proxy for genetic canalization.

To test for the presence of intrinsic genetic canalization mechanisms in Avida, we first evolved populations of Avidians in environments with various mutation rates. We then measured genetic canalization by generating every single-mutant for each population and measuring their fitness. Mutationally canalized organisms should be robust to these introduced mutations and we did find that populations evolved under high mutation rates were more canalized than populations evolved under low mutation rates (Figure 1). To test for the presence of intrinsic environmental canalization mechanisms we evolved several digital populations in environments with various probabilities of instruction failure (“disturbed environment”). Environmental canalization was measured by subjecting the ancestral and evolved populations to a disturbed environment and calculating their respective fitnesses. We found that the evolved populations were more environmentally canalized than the ancestral population. For both types of canalization, our next step is to determine whether the mutations that helped canalize these populations were also involved in performing tasks, classifying them as intrinsic mechanisms.

The Drosophila portion of my research involves modifying expression of a known developmental pathway, the insulin-signaling pathway, to test for intrinsic genetic canalization. Using the standard genetic tools that make Drosophila such a powerful study system I will be tinkering with the expression levels of an important component of the insulin-signaling pathway. To establish causation of a specific component of the insulin-signaling pathway controlling genetic canalization, I predict that there will be a relationship between expression levels of the pathway and expressed genetic variation. This research is ongoing, but when completed it has the potential to be one of the first demonstrations of a genetic canalization mechanism that is part of the developmental pathway it canalizes.

The far-reaching implications of canalization have sustained its study for decades, and the attention is well deserved. From its enigmatic potential as a source of evolutionary novelty to the range of mechanisms it can act through to its contested explanation for how constancy of phenotype is maintained, the concept of canalization continues to promise great discoveries relating to development and evolution. For me, I am most excited about unpacking another biological concept that explains how life persists in the face of so much change.

For more information about Austin’s work, you can contact him at dreyerau at gmail dot com.

Posted in BEACON Researchers at Work | Tagged Avida, BEACON Researchers at Work, canalization, development, Drosophila | Leave a comment

BEACON Researchers at Work: Making evolution personal

Posted on January 27, 2014 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Texas graduate student Laura Crothers.

I used to have a little sticker in my office that said I ❤ evolution. “You can’t love evolution,” my co-worker once told me, “it’s just a thing that happens. That would be like loving time or something.” Fair enough. But the fact of the matter is that from the moment I first learned about evolution as a kid, I was hooked. Evolution is pretty mind-blowing. For example, your family is one 3.8 billion-year long unbroken line that extends back to everybody’s first grandparent, a single cell born in a primordial pond. This means your cousins include the seaweed wrapped around those sushi rolls we love to eat (contrary to popular belief, seaweed isn’t a plant) and the 2-3 lbs of bacteria that live in your intestines. And by looking through the lens of evolution, we can understand so many patterns in the world, ranging from explanations for why I get a stomachache when I drink milk while you might not, to where the weird dog breed known as the Chihuahua came from.

As many have written, the theory of evolution by natural selection is elegant in a way that few ideas are. It assumes very little, and explains so much. It can be neatly summarized. The world is a difficult place to live in, with disease, limited resources, and stiff competition. Members of a species are each a little different from one another, and some of these differences come from random mistakes in the gene copying process that goes on during growth and reproduction. If an individual inherits traits that help it survive and reproduce a little better than others in the population it can pass these traits onto its children, and over time, the makeup of the population (and the species) can change.

But over 150 years after Charles Darwin and Alfred Wallace first introduced their theory, public acceptance of evolution remains frustratingly low. This is despite the fact that the most common religious denominations in the U.S. have no quarrel with evolution. So when I’m not at my day job studying color pattern evolution in poison dart frogs, I am developing ways to teach evolution to people in interactive, fun, and personally relevant ways.

Evolution can be counterintuitive

Problematically, evolution is both a simple theory and one that goes against some of our natural intuitions. First of all, it is exceptionally difficult to think about time scales in the millions and billions of years (but it’s still fun to try. Check out this excellent comic for some mind-blowing facts like “the T-Rex [lived] closer in time to seeing a Justin Bieber concert than seeing a live Stegosaurus.”).

Second, we tend to make sense of the world using an intuitive framework for thinking that leads us to believe that evolution is purposeful, that species are stable and their members all alike, or that creatures seem too complex to have been produced by such a messy, iterative process as evolution by natural selection.

Finally, many of us haven’t been taught about evolution in a way that is particularly interesting or personally relevant. It is a great injustice to teach such an awesome idea, considered by many to be the pinnacle of human thought, using only the dry, overly simplistic facts that we have been hearing since middle school and high school.

To this effect, I am involved in two projects dedicated to teaching the public about evolution. These projects are based on two observations: people like learning about evolution when it’s presented in ways that are personally relevant, and people like to hear about surprising natural history facts and evolutionary relationships.

Evolution impacts our lives in countless ways

A screenshot from a virtual tour of the “Evolution & You” exhibit. You can watch a short video of the virtual tour here.

The first project, a collaboration between UT Austin’s Texas Natural Science Center, several BEACON researchers, and the MSU Museum, has been the creation of a museum exhibit showcasing the many ways that evolution impacts our lives. You can check out a virtual tour of it here. Some questions we tackle in the exhibit include:

Why can some of us drink milk with no side effects while others’ digestive systems go into revolt after the mere sip of a milkshake? {answer}

How is evolution to blame for why you can choke while you’re eating? {answer}

Where do flu virus strains get their names {answer}, and what was the 1918 Spanish flu pandemic like {answer}?

How can evolution experiments let us predict the ocean ecosystems of the future? {answer}

Addressing Some of the Harder Points About Evolution

A screenshot of the Clickademic! game.

Turning a fairly abstract concept like evolution into a tactile experience is not always easy. So to address some common misconceptions about the theory I’ve been working with other BEACON researchers to create two touchscreen games. One, called Clickademic! (put together by Dr. Tom Hladish), lets people see how pathogens like viruses and bacteria can drive the evolution of the creatures they infect.

A part of the tutorial for the Tree Thinking game.

The other, called Tree Thinking, which I’m working on with Ammon Thompson and a digital design company, lets
players create evolutionary trees using the information encoded in DNA. The game teaches several important but commonly misunderstood points about evolution while also feeding people’s curiosity about natural history. A few points we are trying to drive home with the game include:

1) Contemporary species are cousins of each other. For example, chimpanzees are not the ancestors of humans. Instead, we share a common ancestor (your greatx550,000 grandparent, give or take a few thousand generations), who lived about 6 million years ago and happened to be an ape.

2) Scientists use DNA to infer evolutionary relationships, and physical appearance alone can sometimes mislead us into thinking an animal fits into a certain spot on the evolutionary tree of life when it doesn’t. For example, the coelacanth is a fish, but it is more closely related to you than it is to a tuna.

3) As with wolves, Chihuahuas, and Great Danes, members of a species can have impressively variable physical appearances and DNA.

4) Finally, evolutionary trees can be used to solve problems, ranging from identifying new sources for cancer-fighting drugs, to finding the culprits in criminal investigations, to informing vaccine developers of the best virus strains to package in next year’s flu vaccines.

These projects are all a work in progress, but it is our hope that through them we might get everyone to have a “whoa, that’s crazy” moment and stoke the embers of curiosity in both young scientists-to-be and old scientists-at-heart.

For more information about Laura’s work, you can contact her at crothers at utexas dot edu.

Posted in BEACON Researchers at Work | Tagged Education, evolutionary biology, Evolutionary games, museums, Outreach | Leave a comment