CAREER: Understanding and Harnessing the Unreasonable Effectiveness of Evolution with Microbial and Computational Model Systems

One of the deepest mysteries in biology is how life has become so astonishingly complex, which is hard to ignore when looking around at the remarkable biodiversity on earth. Scientists know that evolution is ultimately responsible, but they don't fully understand what fuels the apparent drive for complexity. One hypothesis is that when species face an antagonistic push-and-pull interaction with one another, like between populations of hosts and their parasites, evolution speeds up and produces more novelty than when organisms are simply adapting to a changing environment. This project investigates whether such biological conflict is truly special in its ability to drive evolutionary innovation. To do this, the research team at the University of Michigan combines custom-built devices that control evolution in bacteria in real time, self-replicating computer programs that evolve following the same laws as life, and targeted experiments with bacteriophages—viruses that infect bacteria. Together, these tools let the research team watch and control evolution at a speed and scale far greater than nature alone allows. This work has direct relevance to biotechnology, particularly in understanding and countering antimicrobial resistance, informing potential phage therapy strategies when antibiotics are no longer an option, and improving directed evolution approaches for engineering more adaptable biological systems. Beyond the lab, this project trains Detroit-area teachers through a summer research program so they can bring real evolutionary expertise or experiments directly to their students. An interactive museum exhibit at the University of Michigan Museum of Natural History and online tools will make these ideas explorable to anyone curious about where life's creativity comes from. This award integrates computational modeling, microbial experiments, and theoretical analysis to understand fundamental questions in evolution. Starting with observations from a rather unusual system of self-replicating computer programs, the work investigates how host-parasite coevolution creates changing fitness landscapes and feedback between populations that promote continued innovation. Projects examine whether biotic interactions like parasitism drive fundamentally different kinds of evolutionary dynamics compared to abiotic environmental pressures, and investigates how evolution can move populations toward (or away from) more "evolvable" regions of their fitness landscapes. Three research aims systematically disentangle the effects of antagonistic coevolution by 1) using custom bioreactors to carefully control for the strength and dynamics of selection, by 2) developing mathematical and computational theory for understanding evolvability on increasingly more complex fluctuating fitness landscapes, and by 3) experimentally testing predictions about the evolution of evolvability with bacteriophage evolution experiments. Educational and outreach components are strategically integrated within these research themes. Interactive web-based tools will translate complex evolutionary concepts into accessible simulations for the classroom, while a year-long museum exhibit will showcase how “Unnatural History”, like the self-replicating computer programs described here, can illuminate fundamental biological principles. A Research Experience for Teachers program will engage Detroit-area educators in authentic bacteriophage/computational evolution experiments every summer that advance the scientific aims of this proposal, while also helping them create classroom-ready materials that demystify the practice of genuine scientific inquiry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2540912 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Luis Zaman | Institution: Regents of the University of Michigan - Ann Arbor, ANN ARBOR, MI | Award Amount: $1,347,258 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540912 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540912.html