Projects

Evolution of Suicidal Altruism

Suicidal altruism is the most extreme form of altruism and therefore studying its evolution sheds light on many altruistic behaviors. We previously studied the environmental factors that influence the evolution of suicidal altruism and how quorum sensing influences that behavior. We are currently studying suicidal altruism when the altruistic act produces a public good.
Researchers: A. E. Johnson, E. O’Hara, H. Goldsby, S. Goings, C. Ofria
Contact: anyaejo@msu.edu

Division of Labor

Cells in your body, leafcutter ants in a eusocial colony, and factory workers in a plant are just a few examples of groups exhibiting division of labor strategies in order to survive and thrive. My research explores hypotheses for why division of labor is an adaptive strategy and the role division of labor plays within the major transitions of evolution. These issues are extremely challenging to study using organic systems because of the slow pace of evolution and imperfect historical data. To address these challenges, I perform experiments using digital organisms (populations of self-replicating computer programs that undergo open-ended evolution). Insights from these experiments shed light on evolutionary questions surrounding division of labor and can also be applied to evolving solutions to engineering problems, such as developing teams of specialist robots that must cooperate to achieve an overall objective. This research is funded by an NSF Postdoctoral Fellowship.
Researchers: H. Goldsby
Contact: hjg@msu.edu

Major Transitions

Major transitions in evolution occur when formerly distinct individuals form a higher-level unit that functions as a single reproductive entity. These transitions can be fraternal, where genetically similar individuals (i.e., close kin) differentiate to perform various tasks, or egalitarian in which formerly distinct organisms create a super-organism that replicates all of its genetic material. A fundamental aspect of major transitions in evolution is the role of division of labor, where lower-level individuals specialize and cooperate as part of a higher-level unit to survive. These transitions raise evolutionary questions regarding the conditions under which formerly distinct individuals would cooperate with others, and once they did, how this arrangement persisted. Such questions are incredibly challenging to study with organic systems due to imperfections in the historical data and long generation times that preclude systematic study within a reasonable time frame. For this project, we use digital evolution, a form of experimental evolution where organisms are self-replicating computer programs, to study questions surrounding major transitions in evolution.
Researchers: H. Goldsby, C. Ofria, A. E. Johnson
Contact: hjg@msu.edu

Sexual Selection and Lateral Gene Transfer

Researchers: R. Canino-Koning, C. Bohm, J. Bundy
Contact: caninoko@msu.edu

Resource Spatial Heterogeneity

A central question in ecology has long been how such a wide diversity of life can evolve within a seemingly limited set of niches. Many suspect that spatial heterogeneity is an important factor in making this possible, but this is a very challenging principle to test in biological populations. To try and understand whether spatial heterogeneity is a general driver of diversity, we are experimentally testing it in Avida.
Researchers: E. Dolson
Contact: dolsonem@msu.edu

Evolutionary Breast Cancer Detection

Determining whether or not a mammogram image depicts breast cancer is a challenging problem, even for experienced radiologists. We are attempting to evolve agents, controlled by Markov Brains, that explore images to determine whether or not cancer is present in them. We hope that this work will enable us to detect cancer earlier and more accurately.
Researchers: E. Dolson, C. Bohm
Contact: dolsonem@msu.edu

Anomaly detection and classification

Wireless sensor networks are now being used to collect vast quantities of data. We are developing an algorithm, inspired by robotics algorithms, to facilitate the processing of this data. Our approach is to categorize time points and then leverage relationships between variables within these categories to detect anomalous events and classify them as either the result of sensor faults or non-erroneous rare events. We hope that this will ultimately prove to be a useful tool, both for maintaining and utilizing sensor networks, and for enabling them to make smarter decisions about what data to collect.
Researchers: E. Dolson
Contact: dolsonem@msu.edu

Inferring Rugged Adaptive Landscapes via Spatial Structure

Adaptive landscapes are a metaphor for understanding how populations evolve over many generations. Rugged landscapes are problematic for evolution as populations have difficulty reaching optimal genotypes. I am determining the degree of ruggedness of bacteria evolving in a novel environment (using spatial structure) and hope to generalize this method to other laboratory populations.
Researchers: J. Nahum
Contact: nahumjos@msu.edu

Traversing Adaptive Landscapes Across Changing Environments

For most (really all) evolving organisms, their environment changes over time, exposing them to different selective pressures. Sometimes, combinations of environments can be useful in directing evolution toward more profitable outcomes. I am investigating the relationship between the organism’s effect and response toward environmental change, especially with regard to achieving more optimal solutions.
Researchers: J. Nahum
Contact: nahumjos@msu.edu

Early Evolution of Associative Memory

My group has been using the Avida platform to evolve agents capable of navigating a twisting path, signaled with arbitrary cues. In order to succeed at the task, the agents must be able to associate the arbitrary cues with their meanings, such as ‘turn right’ or ‘turn left’, early on and use that knowledge to navigate the remainder of the path.
Researchers: A. Pontes
Contact: pontes@msu.edu

A New Digital Evolution Platform Based on Gene Regulation Dynamics

I want to use digital evolution to study cells’ regulatory networks. I believe this can help us understand the mechanisms behind behavioral decisions of the simplest organisms. This project will require extensive software development, and I hope it will result in a platform that can reproduce many of the regulatory circuits observed in real organisms.
Researchers: A. Pontes
Contact: pontes@msu.edu