My research has long focused on understanding how simple processes can produce the amazing levels of complexity and diversity we see in nature. This past week, we had a paper appear in PLoS Biology that I am particularly pleased with. In it, we use digital organisms to explore how the interactions between hosts and parasites can promote the evolution of new complex traits, even when those traits would otherwise be costly.
Researchers have long understood that coevolution produces rapid evolutionary changes: parasites race to find new mechanisms to infect hosts and in turn those hosts are pressed to keep evolving new defenses, just to survive. This effect was dubbed the red-queen hypothesis, based on the Red Queen’s famous line to Alice in Through the Looking Glass: “Now, here, you see, it takes all the running you can do, to keep in the same place.” Continue reading →
I recently came across a cool paper by Newhall et al (2014) summarizing the (highly successful) efforts of the Swarthmore College Computer Science department to build an inclusive departmental community. Full disclosure: I attended Swarthmore for undergrad, majored in CS, took classes from all of the authors on this paper, worked as a student mentor, and was pulled into the field of computer science as a direct result of the efforts described in this paper. I am not remotely objective. But that’s what the data in the paper are for.
At the crux of the Swarthmore CS department’s plan was the student mentor program. Student mentors (colloquially referred to as ninjas) are students selected to provide academic support to students in introductory CS classes. They run multiple evening help sessions each week for their assigned class, ensuring that students in the class always have somewhere to turn if they’re stuck. More informally, these help sessions provide an opportunity for younger students to get to know other CS students, ask questions about the department, and get advice about what classes to take. Additionally, student mentors facilitated the introduction of a partially flipped classroom approach in the classes to which they were assigned. These classes were taught in labs where every student is at a computer, with a heavy emphasis on in-class exercises and coding along with professor demonstrations. Student mentors were present in class to assist students when unexpected errors arose. This allowed the professors to devote their attention to the class as a whole while the mentors ensured that no one was left behind. A diverse and gender-balanced group of mentors were selected, ensuring that students would see people like them represented in respected positions in the department. Continue reading →
As an academic, work comes from many different sources and it’s up to you to keep it all under control. As a grad student, you have your research projects, your classes, obligations to your lab, and the need to balance a personal life. By the time you are a faculty member, you still have research (now guiding numerous projects), classes (now teaching), a research lab (that you’re leading), and a life outside of work (hopefully), but you’re also expected to write grants, serve on a myriad of committees, advise students, write reference letters, and review the work of others (manuscripts, proposals, tenure cases, etc.) Each of these can easily become a full-time job unto itself if you’re not careful.
Finding the right graduate program is normally a bit of a challenge. However, this challenge can be magnified if you’re looking to go into a field outside of clearly defined disciplinary lines. You may be sure that there are people studying what you’re interested in, but figuring out who they are and what words they’re using to describe their research is often difficult. During the beginning of my senior year of college, I almost gave up on trying to find a place where I could study the combination of things that I wanted to. It felt like I was just pouring in more and more effort without getting any results. Obviously, I ultimately succeeded – the Ofria Lab is a pretty darn good fit for my interests – but only via a combination of brute force and dumb luck. Nevertheless, I think I learned some things over the course of my search, and I thought I’d share them here in hopes of making it easier for others: Continue reading →
While we’re widely interested in the field of digital evolution/artificial life at the Devolab, we have various more specific projects currently being investigated. We’re planning on trying to keep the Projects page up-to-date whenever a new major project is undertaken or an old one wrapped up (hopefully with a publication to share!), I decided to draw your attention to our current work now that everything is up-to-date: Continue reading →
Hello, world, we’re back! The Devolab (i.e. Dr. Charles Ofria’s research lab) has been up to lots of research, but not much blogging – until now. We’ve got lots of plans for discussing interesting papers, ongoing research, behind-the-scenes looks at grad student life, and much more. For now, plan on seeing new posts on Tuesdays.
To start things off, we thought you’d like to know what we’ve been up to in the past year or so, therefore here is a list of the recent publications from the lab. Going forward, we’ll aim to have posts with high-level discussions of our new papers, and slowly fill in with posts about some of the more exciting previous papers. Without further ado, we present research: Continue reading →
“Digital Evolution Exhibits Surprising Robustness to Poor Design Decisions” by David M. Bryson and Charles Ofria
When designing an evolving software system, a researcher must set many aspects of the representation and inevitably make arbitrary decisions. Here we explore the consequences of poor design decisions in the development of a virtual instruction set in digital evolution systems. We evaluate the introduction of three different severities of poor choices. (1) functionally neutral instructions that water down mutational options, (2) actively deleterious instructions, and (3) a lethal die instruction. We further examine the impact of a high level of neutral bloat on the short term evolutionary potential of genotypes experiencing environmental change. We observed surprising robustness to these poor design decisions across all seven environments designed to analyze a wide range challenges. Analysis of the short term evolutionary potential of genotypes from the principal line of descent of case study populations demonstrated that the negative effects of neutral bloat in a static environment are compensated by retention of evolutionary potential during environmental change.
“Finger-painting Fitness Landscapes: An Interactive Tool for Exploring Complex Evolutionary Dynamics.” by Luis Zaman, Charles Ofria, and Richard E. Lenski
Evolution involves only a few simple processes, yet the resulting dynamics are surprisingly rich and complex. Sewall Wright developed the metaphor of fitness landscapes to provide deeper insight into the complex workings of evolution. Here we extend that metaphor by visualizing in real time the dynamic processes that drive evolution. We allow viewers to construct fitness landscapes interactively while also varying key parameters including population size, mutation effect size, mode of reproduction (asexual or sexual), and densitydependent selection. This application is both mechanistic and visual, and it thereby allows the active exploration of evolutionary processes. We walk the reader through several exercises including both simple activities potentially suitable for education and examples of deeply conceptual topics that remain the focus of current research in evolutionary biology.
“Evolutionary Potential is Maximized at Intermediate Diversity Levels” by Bess L. Walker and Charles Ofria
Diversity in a population is often cited as a major facilitator for the evolution of new complex features. The intuition behind this dynamic is that if a population is exploring multiple regions of a fitness landscape, more opportunities exist to find new functionality. We use the digital evolution software platform Avida to explore the effect of multiple limited resources on phenotypic Shannon diversity and, in turn, on evolvability of populations. We show that Shannon diversity peaks at intermediate levels of resource availability to the population, and we map the evolvability of a complex computational task on this availability-diversity gradient. While the evolvability of the complex task is highest at intermediate availabilities, it does not peak at the same resource inflow level as Shannon diversity, and it is more robust than diversity in its response to inflow level. These results indicate that while phenotypic Shannon diversity may play into the evolution of complex features, the selective pressures caused by diversity cannot be the only — or indeed even the main — pressures behind such evolution.
At the MSU Digital Evolution Laboratory (Devolab), we perform experimental studies on digital organisms with the twin goals of improving our understanding of how natural evolution works, and applying this knowledge to solving computational, engineering, and biological problems.