Title:
Mathematical modeling to deepen our understanding of complex biological systems
 
Abstract:
As science progresses forward, we acquire more information that allows us to deepen our understanding of complex systems. However, it becomes increasingly challenging to properly integrate all the available information, or to identify which components or processes are critical when considering research questions of interest. This is particularly challenging when the information is numerical or from a variety of sources. We can apply quantitative rigor to our understanding by representing our beliefs as a mathematical model. To illustrate this approach in action, I will provide 3 examples from plant biology. Firstly, modeling helps us understand spatial effects and noise in the plant circadian clock. Secondly, at the protein scale, modeling helps distinguish between two schools of thought on how to interpret bioluminescent reporter data. Finally, modeling applied to the classic problem of plant growth suggests areas of improvement and important processes. These examples will help contextualize mathematical modeling as part of the process of scientific discovery.
 
Bio:
As an undergraduate, Renee studied philosophy and became interested in interdisciplinary approaches. She received her PhD in 2019 from LSU, where she studied enzyme kinetics using mathematical modeling. She is now an NSF postdoctoral fellow at the Donald Danforth Plant Science Center, using modeling to study problems that involve variability and complex processes in plant biology.