Travel Award – Undergraduate Diversity at Evolution (UDE) 2017

We are pleased to announce an undergraduate travel award to bring talented and diverse undergraduates to the Evolution meetings this June 23-27 in Portland, Oregon.

For the 14th year the UDE program will send undergraduates to the annual joint meeting to (1) present a poster at the meetings, (2) receive mentoring from graduate students, postdocs and faculty, and (3) participate in a career-oriented undergraduate workshop. The program is sponsored by SSE/BEACON and covers the costs of travel, registration, food and accommodation at the meeting. The application deadline is Friday, March 31st, and decisions will be announced by Friday, April 7th.

Applications are welcomed from all undergraduates (domestic and international), and the admissions goal is to create a diverse pool of students. An overview of the program and the online application can be found at Applications consist of a short statement of interest, a letter of recommendation, and the title and abstract of the poster to be presented.

For inquires, please contact one of the organizers:
Alexa Warwick –
Richard Kliman –
Scott Edwards –

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Individual and Population Variation Pop-Up Institute at UT Austin

This post is written by UT Austin grad student Rayna Harris and postdoc Tessa Solomon-Lane

Image created by Nicole Elmer

Innovative science is increasingly interdisciplinary. With our Pop-Up Institute in May 2017, we aim to expand beyond the traditional scope of interdisciplinary collaboration to make meaningful progress on questions critical to biology, medicine, and society. Pop-Up Institutes are a novel framework for collaboration being funded for the first time this year by the Vice President for Research at the University of Texas at Austin. Designed to be longer than a conference and less permanent than a research center, these Institutes will bring diverse experts together, into the same physical space, to work together. Our Institute “Seeing the Tree and the Forest: Understanding Individual and Population Variation in Biology, Medicine, and Society” focuses on the causes and consequences of individual and population variation. Variation is fundamental to such a wide range of disciplines that we have researchers coming together from biology, statistics, medicine, nutrition, public health, athletics, classics, anthropology, and more.

Technological advances have led to a dramatic increase in the amount of data collected, from the level of the genome to large social networks. But it cannot be assumed that more information equates to a greater understanding or improved outcomes. Integrative approaches and interdisciplinary collaborations will be critical to accomplish this work. Image by Tessa Solomon-Lane.

Individuals differ from each other in a myriad of ways, from their DNA to their behavior and lifetime health. Understanding the underlying causes of this variation across individuals and populations is critical to the success of both the individual and the population within which they live. However, the directionality of cause and consequence is complex, and the pertinent factors that underlie why individuals are the way that they are span disciplines, crossing traditional research boundaries. Human health, for example, is investigated by clinical researchers and health-care professionals, basic and applied biologists, sociologists, statisticians, and more. While genetics clearly influence individual health outcomes, health cannot be fully understood without uncovering the cognitive processes of decision-making. Decision-making is mechanistically based in the structure and signaling of the brain, but family and friends, education, and socioeconomic status all play important and overlapping roles in health-related decisions and, therefore, health outcomes. Different populations—based in ethnic and racial background, gender, sexual orientation, socioeconomic status, geographic location, and more—have differential access to education, work, and health care. These population-level metrics affect individual mental and physical health, thus shifting the state of the population. The interactions between individual and population are reciprocal, dynamic, and not well understood.

The recent explosion of interest in “Personalized Medicine” (also Precision Medicine) has underscored the complexity of the causes and consequences individual and population variation. But healthcare is not the only field facing the challenges of complexity. In fact, many of the problems currently limiting progress are shared across disciplines. For example, little is known about how phenotypic variation develops or is maintained—and at which mechanistic level (e.g., genomic, neural, physiological)—within and across populations and species. A promising and popular approach to understanding the causes of variation at the individual and population levels relies on technological advances in the collection, management, and analysis of large amounts of data (i.e., “Big Data”). For example, it is now feasible to sequence all of the genes expressed in a cancerous tumor or in the brain of a social fish. Big Data also exists at the levels of behavior, social networks, population genetics, and more. But it is not straightforward how information about tens of thousands of genes from a single individual (n=1) can be used to optimize treatment and care. Using conventional models, there is no statistical power in predicting outcomes for a single individual. Furthermore, it is a fallacy to assume that more data necessarily results in a more complete understanding or improved care. Patterns within the data may or may not be meaningful, and choices surrounding analysis and interpretation have consequences for individuals and populations. Unless the right questions can be asked of these data, sheer volume does not guarantee understanding.

For our Institute, we aim to 1) identify fundamental similarities across disciplines and the most promising, central research questions related to individual and population variation, 2) establish a unique and comprehensive research plan, and 3) develop solutions to shared problems that currently limit progress. Together, this work will launch ongoing collaborations to conduct groundbreaking research with real-world, positive outcomes for humans and society.

Over 40 trainees and faculty from across the University of Texas at Austin are already committed to working to accomplish these aims over a period of three weeks. While the Institute will officially take place in May, this diverse group has already come together for town halls and focus group meetings to hone in on our specific themes. The Big Data in Biology Symposium, now in its 5th year, will serve as the launch event for the Institute. This Opening Symposium will bring together researchers from across the University to share their research and synthesize new ideas.

In addition to the support received by the Vice President for Research at The University of Texas at Austin, this Pop-Up Institute is also supported by the Center for Computational Biology and Bioinformatics and several other units and departments, along with the BEACON Center.

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Marvelous microbes: Embracing our beneficial neighbors

This post is written by MSU grad student Shawna Rowe

450 million years ago, plants began to colonize land and grow into the wonderful forms we see today. During this process, the picked up a partner in crime: the mycorrhizal symbiosis— a fungal association many terrestrial plants use to acquire phosphorous. Fast forward to 60 million years ago and we see the emergence of the rhizobial symbiosis— a bacterial association used to aid in the uptake of nitrogen in leguminous (like beans, peas, and clovers) plants. These associations represent extreme examples of beneficial bacterial partners known as mutualists. As a PhD student, I am working to improve our understanding of molecular mechanisms that regulate the legume-rhizobia symbiosis. My attention is focused on understanding the careful balance between enabling the mutualistic relationship and preventing pathogenesis.

What’s so special about rhizobia?

Nitrogen is an essential element for proteins and many other molecules in living organisms. most of the nitrogen we interact with (i.e. ~80% of the air we breathe) is in the form of N2. Instead of getting nitrogen from the air, many organisms like plants and humans, will simply take it from alternative sources that exist in the soil (figure 1). However, to get from existing as N2 to existing as a bioavailable source, nitrogen must be “fixed” at some stages. The two natural ways this occurs are lightening strikes and microbial nitrogen fixation.

Figure 1: Nitrogen cycle

Rhizobia are nitrogen-fixing powerhouses. Nitrogen fixers are organisms that are able to break the incredibly strong triple bond between two nitrogen atoms. In addition to rhizobia, there are many “free-living” organisms that fix nitrogen. Rhizobia are particularly special because they form a relationship which allows them to live inside the root systems of the host leguminous plants. This means that legumes, unlike most other host plants, have the ability to thrive on nitrogen limited land that other plants would be unable to colonize without the help of exogenously applied fertilizers.

Which brings us to 1913— the year ammonia was first artificially manufactured using the Haber-Bosch process. This process was a transformative moment for modern agriculture. Gaining the ability to artificially fix nitrogen into a bioavailable form meant that previously unusable land could be more readily converted into farm land. Even more, adding extra fertilizer to already fertile land made crop yields skyrocket…. A win for everyone! Or so we thought. After years and years of applying synthetic fertilizers to our crop lands we are beginning to see the negative effects: increased use of fossil fuels, nitrogen run-off resulting in algal blooms, and the reshaping of microbial and plant communities. Studying the legume-rhizobia symbiosis will hopefully reveal answers to questions that could lessen our global dependency on nitrogen fertilizers.

What kind of questions need to be answered?

Although this ancient relationship has been studied for many years, many questions remain unanswered. My selected question deals with elucidating how leguminous plants differentially regulate resources to rhizobial partners of varying nitrogen-fixing abilities. Like most organisms, rhizobia of the same species can have varying traits due to varying genetic makeups or other influential factors. One such “trait” is the extent to which a given organism fixes nitrogen. Some fixers, the most beneficial partners, lead to the greatest host fitness and thus are preferentially offered resources from the host plant. Others exist at the opposite end of the spectrum. My thesis work is aimed at understanding what mechanisms regulate this.

In addition to this work, I am also interested in how a host’s ability to limit growth of the least beneficial partners may be impaired by certain environmental factors. For instance, I am currently investigating how other members of the microbial community alter the host plant’s ability to differentiate between partners of varying effectiveness. As I mentioned above, the world is swimming (both literally and figuratively) in microbes of all different types— the soil is no different! In addition to understanding the mechanism behind this regulatory phenomenon, it is helpful to understand how various environmental inputs also effect these mechanisms.

ShawnaRowe_LabWhy does this matter to me?

Hailing from the countryside of Missouri, I grew up surrounded by agriculture. Upon graduating high school, I entered college as a Biochemistry major with no clear idea of what my scientific interests were. I was fortunate enough to land a job in a plant biochemistry research lab. There, they focused on understanding basic mechanisms of plant immune responses to pathogenic bacteria. I discovered the complex world of molecular signaling events and microbial associations. I learned about the co-evolution of organisms that commonly associate and how these associations drive the development and establishment of complex features of host-microbe interactions. I fell in love with our microbial neighbors.


Rodgers, Wiley. “Nitrogen Fixation for Dummies.” City Sown. WordPress, 13 Mar. 2010. Web. 19 Feb. 2017.



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Richard Lenski wins 2017 Friend of Darwin award

Richard Lenski pulls frozen bacteria cultures out of a freezer in the lab on Thursday October 15, 2009. poses in the lab on Thursday October 15, 2009.

Richard Lenski pulls frozen bacteria cultures out of a freezer in the lab on Thursday October 15, 2009. 

Richard E. Lenski, the Michigan State University John Hannah Distinguished Professor and evolutionary biologist renowned for his E. coli Long-Term Experimental Evolution Project, has received a 2017 Friend of Darwin award from the National Center for Science Education (NCSE). Lenski is one of only three scientists nationally to receive the award this year.

The Friend of Darwin award is presented annually to a select few whose efforts to support NCSE and advance its goal of defending the teaching of evolution and climate science have been truly outstanding.

“It would be hard to think of anybody who has done as much to show that evolution is among the experimental sciences than Rich Lenski,” said NCSE’s executive director Ann Reid.

In 1988, Lenski began an experiment with 12 populations of E. coli-all started from the same ancestral strain and all living in identical environments-to see how similarly or differently they would evolve. He initially wanted to keep the experiment going for at least a year and about 2,000 bacterial generations, maybe longer. The long-term experiment with E. coli is now past 66,000 generations and will have its 29th birthday later this month.

“I feel so honored to get this recognition from an organization that fights to make sure students have the opportunity to learn about science and the evidence it can provide,” said Lenski, who is also a founding member of the BEACON Center for the Study of Evolution in Action at MSU.

“Lenski’s work shows explicitly how genotypic and phenotypic variation arises in separated populations,” said Stephen Hsu, vice president for research and graduate studies at MSU. “It provides a direct experimental demonstration of evolution and adaptation at work, which can be queried at the level of DNA itself.” Lenski holds joint appointments in the Departments of Integrative Biology and Microbiology and Molecular Genetics in the College of Natural Science, and the Department of Plant, Soil and Microbial Sciences in the College of Agriculture and Natural Resources. He is also an MSU AgBioResearch scientist.

In addition to Lenski, Edward J. Larson, the Pepperdine University historian and legal scholar who won a Pulitzer Prize for his 1997 book about the Scopes trial, Summer for the Gods; and Daniel J. Phelps, a geologist and critic of a young-earth creationist organization headquartered in his native Kentucky, were also 2017 Friends of Darwin award winners.

Robert Pennock, an MSU professor in Lyman Briggs College, the Department of Philosophy in the College of Arts and Letters, the Department of Computer Science and Engineering in the College of Engineering, and the Ecology, Evolutionary Biology and Behavior Program in the College of Natural Science, and another co-founder of BEACON, also won a Friend of Darwin Award from NCSE in 2003.

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Making lemonade out of lemons: the genetics of yeast cell clumping in continuous culture experiments

This post is written by UW postdoc Elyse Hope.

There is a lot of genetic complexity that can contribute to what an organism looks like. Far from a single gene controlling a single trait (e.g. a gene for height or eye color), many traits are determined by the contributions of hundreds of individual genes acting in a variety of pathways. Trying to figure out what genes are involved is a hard problem, but important to pursue using both classic and novel methods because the majority of important traits across all organisms are complex. On the front lines of agricultural research, geneticists are working to better understand complex traits like drought tolerance, pest resistance, and root depth that will assist in intelligent breeding and genetic engineering decisions in response to climate change. In the microscopic world, bacteria and other microbes exhibit a variety of complex traits that enable them to colonize new nutrient sources and resist harmful chemical treatments, contributing to pathogenicity and the development of drug resistance. It is in this microscopic world that I do my work, adapting the methods in the Dunham Lab to ask how a complex trait evolves.

Figure 1: Continuous culture chemostat vessels in a heat block, with media input and effluent output lines

In Maitreya Dunham’s lab at the University of Washington in Seattle we investigate (among many topics) how the single-celled microbe yeast evolves in real time. The yeast we use is the same species that is used to make beer and bread and as such is not typically considered a pathogen itself, but we can use it as a model system to ask questions about yeast in general. Most of our questions have to do with how yeast cells respond (at the level of both their genomes and their phenotypes, or what they look like) to selective pressures, particularly limitation of an essential nutrient. The system that we use to ask these questions is a device called a continuous culture chemostat, which is a small glass growth chamber that allows the microbes to grow at steady state (Figure 1, multiplexed chemostats), adapting only to the selective pressure we’ve chosen — or at least that’s the idea.

Figure 2: A flocculent yeast strain settles in liquid media in a culture tube

Just before I arrived in the lab, another graduate student conducted 95 of these small evolution experiments under three different nutrient limitations and found that about 35% were also demonstrating an adaptation to the physical constraints of the growth chamber itself. They evolved a trait in which the cells formed big clumps while growing in the chemostat — big enough to be visible to the naked eye, which is an accomplishment for cells as small as yeast (Figure 2, cells clumping in liquid culture). This trait increases how long the yeast is able to remain in the growth chamber without being diluted out, which gives them a different type of adaptive advantage. It’s also a nuisance in our experiments (the “lemon” of my title), because it confounds the selective pressure from the nutrient limitation. A well-known credo in biology is “you get what you screen for” (Frances Arnold, Trends in Biotechnology, 1999). While we were screening for responsiveness to the nutrient limitation, we were also screening for responsiveness to the growth environment itself. This collection of clumping strains gave us a unique opportunity to make lemonade and ask questions about the clumping trait instead, like which genes contribute to clumping and how often we see mutations in the same genes. These were the primary questions I explored in our most recent paper, available on bioRxiv while it is in review.


Figure 3: How many ways can we get to flocculation? Possible causal mutations in clones are represented as colored ellipses, that might be different in every clone (A), arranged into pathways (B), or acting on the same downstream gene (C)

Clumping, or “flocculation” as it’s technically called, is a known complex trait with many genetic contributors, and has shared features and genetics with other traits related to biofilm formation, which have hundreds of known contributing genes. What would we find in the 23 clones we selected to study? Would there be too many contributing mutations for us to resolve? Would each clone harbor a different causal mutation, or would they all be in the same gene or acting on the same gene? (Figure 3, possible routes to flocculation) I want to step back for a moment and say that this project was a geneticist’s dream. We had a remarkable puzzle to solve, of unknown complexity, and the entire toolkit of classic yeast genetics and modern genomics techniques at our disposal. We also had no idea what we would find at the end, but we knew it would be interesting and possibly quite useful. The project also even had components, like the Bulk Segregant experiment I’ll describe below, that were suitable teaching tools, and some of the experiments and analyses were conducted as part of the Yeast Genetics & Genomics course at Cold Spring Harbor in the summers of 2014-2016.

We started our search for the causal mutations using Whole Genome Sequencing, comparing the genome sequence of the evolved flocculent clones to the sequence of the ancestral yeast strain from which they evolved. We detected many mutations per clone this way, and only some of those were in candidate genes we expected might be involved in flocculation based on the literature. To narrow it down to which mutations were actually causing the flocculation, we employed a classic yeast genetics technique called Bulk Segregant Analysis, which uses yeast crosses to follow which mutation co-segregates with a trait (e.g. flocculation/clumping). By examining co-segregation patterns for the mutations we identified in our sequencing data, we were ultimately able to identify the causal mutations in all of our clones. At first, we thought there were a few different mechanisms at work. We found a common mutation affecting the regulation of a known flocculation gene called FLO1 in over half of our clones, but the rest seemed to have a mix of different mutations. When we got further into the literature about flocculation, however, we discovered that almost all of the mutations we found were in genes that also had something to do with the regulation of FLO1.

This could have gone very differently. Since we started this work by agnostically asking how many different adaptive routes lead to flocculation, we could have gotten 23 different answers. Instead, we got one primary route. Because of this outcome, we could take the work a step further: if we deleted this causal gene, would the yeast evolve flocculation more slowly? Our final experiment evolving many replicates of a strain with FLO1 deleted revealed that this single deletion reduced flocculation occurrences to 3%, and demonstrated the efficacy of using experimental evolution as a tool to identify and eliminate the primary adaptive routes for undesirable traits.

This work applied experimental evolution to better understand a complex trait — but for me, flocculation isn’t just any complex trait. It’s a useful trait, the genetics of which is incredibly important to understand. Flocculation is one of several traits associated with biofilm formation, a community-building trait that is a key contributor to brewing but a nuisance in other industrial processes and in laboratory and clinical settings. Pathogenic yeast that live in such protective communities are harder to kill with antimicrobial agents, and industrial yeast that clump are easier to filter without expensive machinery or chemical treatments. I’m hopeful that the evolution-and-engineering approach we took will enable us and others to better serve the needs of the medical, academic, and industrial communities who face biofilm-related issues daily.

The approach underlying this work also exemplifies the main reasons I chose not just my graduate lab but also my graduate department. Both understand that classic genetics techniques and modern genomics techniques can answer a lot of questions on their own, but combined they can do even more. I think scientific endeavors are most successful when they are rooted in both the contributions of the past and of the present, and I’m pleased that we had an opportunity to showcase how well that can work with these experiments.


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Judi Brown Clarke receives Excellence in Diversity Award

We are proud to congratulate BEACON’s Diversity Director Judi Brown Clarke for receiving the Individual Sustained Effort Toward Excellence in Diversity. The Excellence in Diversity Award program recognizes outstanding efforts of faculty, students, and staff at MSU who are committed to the principles of diversity and inclusion and who actively engage in activities demonstrating a sustained commitment to these principles.

The award ceremony will be held at The Kellogg Hotel and Conference Center, Big Ten A, on the MSU campus Monday, February 13, 2017 at 4:00pm.

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Evolving Evacuation Plans for Urban Areas

This post is written by UI grad student Keith Drew

University of Idaho Evacuation Planning Research Team

The team at University of Idaho currently consists of four people, Dr. Robert B. Heckendorn, Keith Drew, Homaja Marisetty, and Madhav Pandey. Our team also includes researchers at the Michigan State University, led by Dr. Kalyan Deb. Our work is focused on evacuation planning and emergency management, using evolution strategies to evolve traffic assignments, or probability-based instructions, for intersections in urban areas. The problem we want to solve regarding evacuation is the issue of traffic congestion, while also being able to adapt to changes in the environment. Our goal is to evolve real-time traffic distributions in the face of disasters and other evacuation events, however, the group at Michigan State University is seeking to solve the same problem using a different approach. So far, we at the University of Idaho have created an implementation of our model and began running experiments.

I am a graduate student in Computer Science at the University of Idaho and I also completed my undergraduate here as well. I was introduced to the project by Dr. Heckendorn, and have found it to be engaging work so far. Personally, I am interested in this work because I feel it is important. A real-world application, leveraging evolution strategies, appeals to me in two significant ways. First, the work could lead to a life-saving evacuation management system, which appeals to my practical side and second, working with evolution heuristics is always compelling for the sake of watching solutions to difficult problems form.

Our research goal is to find a way to provide real-time solutions to evacuation problems, which are constrained by congestion, time, and dynamic events. In the past, evacuations have led to large congestion problems in the traffic systems being evacuated. Our work seeks to provide traffic distributions that tell vehicles which way to go at intersections, adapt to changes in the environment, and optimize routing for the safety of the population being evacuated. So far, our specific approach is untested and looks promising. Our current approach is to evolve probability distributions for each intersection throughout the area being evacuated. These probabilities serve to route traffic by breaking the traffic assignment up and sending the appropriate amount each way at an intersection. We judge each set of such probabilities based on how safe every member in the population is at a given time. For example, if a hurricane will move over the evacuation area in one hour, we simulate one hour and evaluate safety at that point. Evacuation plans vary by the type of event calling for an evacuation, such as a flood or a hurricane. In some cases, elevation is a key component of safety, in other cases distance is the key focus; by evaluating the safety of the population we hope to find non-intuitive solutions for any type of evacuation event.

Another aspect driving our research is the dynamic nature of real disaster and evacuation events. Our model focuses on these changes as constraints. For example, we want to be able to handle a change in the availability of certain routes, during a disaster. Such a change might be a bridge washing out or collapsing, or perhaps a road becoming partially or fully blocked due to a traffic accident. Another type of change involves the way that people behave while driving. If people are told to evacuate a certain way, yet they choose to follow their own instincts, issues of congestion can arise, and our model needs to be able to handle those types of changes. Another more obvious type of change is the disaster or danger that people are evacuating from. Consider a hurricane, which moves along. Over time, safe areas become unsafe, and unsafe areas become safe once again. In the face of such changes evacuation plans can be undone, and we want to provide a solution that can adapt to any such change.

An example of our model output

Once an algorithm or model is created that works well and can create robust traffic assignments for large areas, communicating that information to the evacuees becomes a new challenge. Methods for communicating such information include possibilities such as vehicle-to-vehicle communications, self-driving cars, and more. Self-driving cars are of particular interest, as studies have shown that automation can improve traffic conditions significantly, and some work has shown that self-driving cars can work together to navigate intersections without stopping. They simply speed up or slow down on approach to the intersection. With such technology, our model might be able to provide the directions needed to such cars.
My contribution to the project so far has been helping to develop the model we are using, as well as implementing it, using a combination of a lexical analyzer, parser, and C++. Currently our system includes a simulation, for running traffic through a graph that represents the area being evacuated, an evolution strategy algorithm, which evolves the traffic distributions, and a grammar which is used to specify evacuation areas and tests for our model. Another key component created by Homaja, is a visualization tool, written in Processing. The tool takes output from our model and creates visualizations of traffic moving through the grid, as well as creating graphs that allow us to ensure the model is working as intended. It’s very nice to be able to watch vehicles traverse our little city, and escape some unseen danger.

In the end, we are leveraging knowledge and experience from the realms of evolutionary computation, emergency management, civil engineering, and traffic analysis and behavior, all to create a system which can, ideally, minimize any loss of life during large emergency events. So far, preliminary results look very promising, and I am definitely enjoying this work.

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Charles Ofria named one of the 2017 William J. Beal Outstanding Faculty

Charles Ofria, 2017 William J. Beal Outstanding Faculty Award Recipient

Charles Ofria, 2017 William J. Beal Outstanding Faculty Award Recipient

We are proud to congratulate BEACON’s Deputy Director Charles Ofria for being named a 2017 William J. Beal Outstanding Faculty Award winner.

Charles Ofria is recognized internationally for his research at the interface of computer science and evolutionary biology. He developed the Avida Digital Evolution Research Platform, wherein self-replicating computer programs are subject to mutations and selective pressures resulting in an open-ended evolutionary process. Because these digital organisms exist inside a computer, Ofria can easily study long-term evolutionary processes and, in turn, apply what he learns toward solving computational problems

Ofria is one of the founders of the $50 million BEACON Center for the Study of Evolution in Action at MSU, an NSF-supported center that allows engineers with an applied evolutionary focus to work with evolutionary biologists to create a theoretical foundation for both computational and biological research.

As part of his role in founding the BEACON Center for the Study of Evolution in Action, Ofria developed a multidisciplinary course on Multidisciplinary Research Methods for the Study of Evolution, for the purpose of mentoring students on the research process at the intersection of fields. He covers communicating across fields, developing interesting research questions, performing literature searches, formulating and testing hypotheses (along with using simple statistics) and presenting research results so they are accessible to a range of audiences.

Each year, multiple groups in the class publish their work after the end of the semester, with students universally agreeing that they have gained tremendous insight into the research process. Teaching reviews from other classes note his ability to convey abstract concepts in a practical manner, often through live-coding demonstrations, real-world examples and instructional technologies that provide students with instantaneous feedback.

His teaching has been recognized with the MSU Teacher–Scholar and the Withrow Teaching Awards.

Ofria is the president of the International Society for Artificial Life and a member of the editorial board for “PeerJ Computer Science.” He is active in reviewing articles for a number of prestigious journals, including “Nature,” “Communications of the ACM: IEEE Proceedings of Artificial Intelligence” and “Journal of Theoretical Biology.”

MSU will celebrate the accomplishments of 10 William J. Beal Outstanding Faculty Award winners and other recipients of all-university awards during the Michigan State University’s annual Awards Convocation on Tuesday, Feb. 7 at 3:30 p.m. at Wharton Center’s Pasant Theatre. Two faculty from the College of Engineering — Bradley Marks and Charles Ofria — will be honored. The 2017 honorees bring the number of MSU faculty honored to 541 since the award was established in 1952.

Colleagues, friends and family are invited to share the event with the awardees. MSU President Lou Anna K. Simon will salute their contributions to the university’s excellence. Simon also will take a few minutes to acknowledge MSU’s Founders Day as well as deliver the 2017 State of the University address.

Watch the celebration at
There will be a link to the live stream of the celebration at and The William J. Beal Outstanding Faculty Awards are supported by the Office of University Development.

William James Beal (March 11, 1833 – May 12, 1924) was an American botanist, who was professor of botany (1871-1910) and curator of the museum (1882-1903) at the Michigan Agricultural College (MAC), now MSU. He was a pioneer in the development of hybrid corn and the founder of MSU’s renowned W. J. Beal Botanical Garden.

Related Website:
Story courtesy of MSUToday

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Indigenous Evolutionary Knowledge Survey

This post is written by MSU postdoc Wendy Smythe

For the purpose of this survey we are asking Native American, Alaska Native, Pacific Islanders, and Hispanic individuals to take this 15-minute survey as a means to give voice to the views and opinions of Indigenous people.

The goal is to take an active role in helping bridge the gap of knowledge between non-Native scientists and Indigenous communities by utilizing traditional knowledge to more effectively employ science instruction. Indigenous people possess Traditional Ecological Knowledge (TEK) that has led to the sustainability of many ecological resources—where these resources are responsible for the sustainability of these communities since time immemorial. However, the emerging climate crisis and increased anthropogenic activities have begun to threaten and deplete these resources. To address these concerns, the science, technology, engineering, and math (STEM) fields have begun to collaborate with NA/AN communities in an effort to monitor, manage, and protect natural resources.

Currently there is a disconnect between the STEM fields and TEK with regard to ways of knowing and how to ethically and respectfully use TEK. A disconnect that is often reflected in science instruction, where few educators completely understanding Indigenous worldviews and their effects on student learning and world view. Kimmerer provides a more descriptive definition of TEK, where he states that TEK is;

“knowledge, practice, and belief concerning the relationships of living beings to one another and to the physical environment, held by peoples in relatively nontechnical societies with a direct dependence upon local resources…it is born of long intimacy and attentiveness to a homeland and can arise wherever people are materially and spiritually integrated within their landscape. TEK is rational and reliable knowledge that has been developed through generations of intimate contact by native peoples with their land”.

Below is a link to a survey of Indigenous Knowledge in order to collect data on opinions in Indian country about TEK/STEM and Evolution. We are trying to get 300 responses and the first 50 will get a $10 Amazon gift card, followed by a random drawing of 25 for a $5 Amazon gift card. We want any age group, all education levels, and anyone Indigenous (Native American, Alaska Native, Mexican, Pacific Islander).

The survey takes about 12 minutes to complete (and must be completed to get the Amazon gift card).

The survey will be open until February 16, 2017

The best way to effect change of Indigenous Education is by listening to the voices of the people.

Here is the link.

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Studying the Evolutionary Dynamics of Emergent Phenotypes

This post is written by MSU faculty Mark Reimers and Arend Hintze

Let us marvel about the complexity of life for a moment. We have DNA transcribed into mRNA, just to get that translated into proteins, which metabolize, catabolize, or process many other molecules and are responsible for the form and function of each cell. But it doesn’t end there, cells form aggregates and make tissues, which make organs, which form organisms, which are controlled by a complex neural network, just so that we can have social interactions and form communities. And again it doesn’t end there. Each of these steps, in themselves dependent on all other layers of interactions. You see an attractive mate, your brain processes this information, it secretes hormones, which trigger signal transduction cascades, which express genes, regulate other functions, and as usual, the story told much later was: “Well, one thing led to each other, and Mommy and Daddy fell in love, and then you were born!” – evolution in action!

But it also makes us wonder if our computational models keep up with this nested complexity. In pretty much every system we use to study evolution, we have some form of genome that gets translated into something who’s performance we evaluate, and maybe we have agents interacting, but these at most three steps of modeling is far from the apparent complexity of nature – but does it matter?

We think it is more than fair to say that we learned very much about evolutionary processes and evolution in general from using computational models, and quite frankly, that was possible because they were simple. Every time you add another complication to your model, you potentially open a can of worms, and you need to control for yet another factor. Simplicity is the key to successful research.

Most simulation studies at BEACON assume a one-to-one correspondence between ‘genes’ and traits. This strategy makes sense for simulating evolution of bacteria, whose business is biochemistry, and where many phenotypes depend on one gene (or one operon); the E coli studies and single objective evolutionary algorithms were the two key inspirations for BEACON. However we argue that this approach is insufficient for studying metazoan evolution, because animals are constructed through interaction of many components specified by genes. Each trait or body feature is then affected by many genes; and most genes affect several distinct traits, although some of these may be revealed only under a life stress not typically encountered or hard to test in the lab.

At this point we want to broaden our understanding, and use computational models to understand more complex biological processes, in particular those where the system itself changes over time, typically: neuronal or developmental plasticity. In both cases, the genome isn’t translated into the final structure, but the genome encodes a process that controls an ever changing system. In terms of developing a computational model, it becomes less about specifying a form, but about specifying the rules that control a dynamic system. But even that would not really capture natures complexity, the challenge becomes to specify rules that specify rules, which control rules, which are all codependent and interacting. Or maybe this isn’t necessary, and simple models are already sufficient and there is no additional effect on evolutionary dynamics or adaptation. Quite frankly, we don’t believe that. What controls rate of adaptation, and at what fixed point one arrives depends strictly on how the fitness landscape is explored, and it is mutations that facilitate that. If mutations all have a direct effect on how they move the organism around in the FLS then you will have a local exploration. If the effects are random, the organisms would jump around randomly in the FLS. Consequently, a complex nested system that has neural or developmental plasticity will not only move around the fitness landscape in strange ways, it will also start at one point in the landscape, and due to it’s lifetime adaptability move a different point over it’s lifetime, and the genes control how this lifetime movement happens.

If we want to study how animals and their traits evolve, we need to consider how genes affect developmental processes, and model such processes in our simulations. Development proceeds by signals between cells (or other components); the strength of these signals is specified by genes, but the consequences for traits depend on the interaction of any modified signal with all the others active in the same place at the same time. Of course we need to abstract from the complexity of nature.

Other considerations suggest that we should explore this. Evolution typically selects via very many criteria simultaneously. Although research over the past few decades has shown how evolutionary algorithms work for a single criterion and to some extent for two, we have little idea about how to select for many criteria simultaneously. However this is exactly what happens in animal evolution. As Gerhart and Kirschner argue in The Plausibility of Life, summarizing the work of many evo-devo labs, the flexibility of a metazoan to adapt simultaneously to many different criteria and changing selective forces depends on indirect and emergent mechanisms generated by exploratory processes, and ‘weak linkages’, both specified by genes. If we want our simulations to be relevant to the relation between molecules and animal forms or behavior, we should simulate such mechanisms.

We think this approach will likely also shed light on one of the major issues in human molecular genetics. Despite the promise of the Human Genome Project to identify the genetic variants that contribute to complex human diseases and thus to clarify the molecular processes that drive such diseases, little actionable knowledge has been accumulated nearly 20 years on. What evidence we have about haplotype blocks and purifying selection suggests that many disease-related variants have actually been selected for, rather than against. Such observations cannot be explained by the kind of ‘one gene/one trait’ models often studied, but they are entirely consistent with the many and varied selective pressures that are brought to bear on a complex long-lived animal in changing circumstances.

Similar considerations hold for evolving behavioral traits. It is easier in simulations to specify discrete behaviors through distinct genes rather than to simulate the processes that produce behavior, and so such simulations are a natural first step. But as we want our simulations to be more relevant to animal behavior, then we need to consider the development of the nervous system, and the history of learning, both whose processes, but not outcomes, are specified by genes.

In the last few years we at BEACON have made considerable progress in understanding how complexity can emerge through the evolution of simple traits; we have expanded our repertoire of computational tools; and we learned to work closer and better across disciplines. Now we contemplate the prospect of researching the layers of complexity possible through evolving emergent systems; it is mind boggling, as we open a door to glimpse at what nature holds in store for us. We look forward to being able to open this door even further with the support of BEACON and critically discussing insights with our community!

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