MODULUS: Data-Driven Structured Population Modeling for Prediction of Complex Photosynthetic Phenotypes

Populations can be divided based on the properties of individual members, including features such as size and age. Mathematical models describing how features are spread across a population are called “structured population models” and are widespread in the biological sciences. In this project investigators will develop a novel algorithm and software to automatically create structured population models.

Mathematical models will be used to study photosynthesis in bacteria with the goal of improving photosynthetic efficiency. This work could potentially lead to substantial improvements in bioreactors, water treatment, and biofuel production. As part of the Broader Impact activities investigators will provide training opportunities for postdoctoral, graduate and undergraduate students.

Outreach activities for high school students and the larger community will also be conducted through their institution’s Summer Multicultural Access to Research Training (SMART) program and in partnership with the CU Society for Women in Engineering(SWE) organizat

Recent advances in sparse-regression based data-driven modeling allow for the creation of mathematical models directly from data, reducing reliance on conventional single candidate model creation, simulation, and validation methodologies. A central premise of this research is that the applied framework will yield novel structured models, leading to biological insight into population-driven adaptations that impact the use of light energy for the conversion of CO2 into biomolecules critical for life on earth.

The investigators will extend the data-driven framework to create a rigorous methodology for the discovery of structured population models in photosynthetic cyanobacteria. Advances in computational tools and models will inform the development of experimental studies to investigate the relationship between population structure and complex photosynthetic phenotypes yielding maximal bacterial growth.

Experimental studies will leverage mutant strains and use long-term time-lapse imaging and carboxysome tracking to investigate hypotheses. Research-driven Broader Impact activities will include interdisciplinary training of students and outreach to high school students and the broader community through institutional and professional society (SWE) partnerships.

This award is co-funded by the Systems and Synthetic Biology program in the Division of Molecular and Cellular Biosciences and the Mathematical Biology program in the Division of Mathematical Sciences.

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.