Abstract
This talk explores mathematical modeling approaches for designing control strategies to maintain health and combat disease across multiple biological scales. Using mathematical models gives insight into a wide array of biomedical applications ranging from engineered cell-based therapies to diet-based therapies.
In engineered cell-based therapies, feedback control of cell population density is central to genetic designs. In this multicellular coordination problem, control action takes place on two levels: i) individual cells can activate or repress relevant genes, ii) cells can access the ensemble state of the entire population as obtained through diffusible signaling molecules. This enables engineered cells to function as “smart therapies” that make autonomous decisions based on environmental cues and intercellular communication. This multi- level control provides robust and optimized therapeutic potential but requires careful modeling to address stability and performance challenges. By applying control theory, models can pinpoint strategies to enhance the stability and functionality of these systems.
In addition, diet-based interventions offer potential to modulate skin health through interorgan feedback pathways between the gut microbiome and skin. Models can be used to examine how dietary changes affect gut microbiota, activate interorgan communication, and modify inflammatory responses. This approach helps identify treatment strategies that leverage the gut-skin axis, while applying control theory supports hypothesis generation and predictive interventions, even though interorgan connections remain incompletely understood.
In this talk, I present mathematical frameworks from an integrated control, computational biology, and healthcare perspective to characterize mechanisms, performance properties, and design principles for the aforementioned biomedical therapies. These biomedical case studies demonstrate how engineering principles like stability, robustness, and feedback control apply to biological systems, illustrating how cross-disciplinary frameworks bridge gaps between engineered and biological systems, opening avenues for precision medicine and treatment optimization.
About the Speaker
Dr. Michaëlle N. Mayalu is an Assistant Professor of Mechanical Engineering. She received her Ph.D., M.S., and B.S., degrees in Mechanical Engineering at the Massachusetts Institute of Technology. She was a postdoctoral scholar at the California Institute of Technology in the Computing and Mathematical Sciences Department. She was a 2019 Burroughs Wellcome Fund Postdoctoral Enrichment Program award recipient. She also was the 2023 Hypothesis Fund Award Recipient.
Dr. Michaëlle N. Mayalu's are of expertise is in mathematical modeling and control theory of synthetic biological and biomedical systems. She is interested in the development of control theoretic tools for understanding, controlling, and predicting biological levels to optimize therapeutic intervention.
She is the director of the Mayalu Lab whose research objective is to investigate how to optimize biomedical therapeutic designs using theoretical and computational approaches coupled with experiments. Initial project concepts include: i) theoretical and experimental design of bacterial "microrobots" for preemptive and targeted therapeutic intervention, ii) system-level multi-scale modeling of gut associated skin disorders for virtual evaluation and optimization of therapy, iii) theoretical and experimental design of "microrobotic" swarms of engineered bacteria with sophisticated centralized and decentralized control schemes to explore possible mechanisms of pattern formation. The experimental projects in the Mayalu Lab utilize established techniques borrowed from the field of synthetic biology to develop synthetic genetic circuits in E. coli to make bacterial "microrobots". Ultimately the Mayalu Lab aims to develop accurate and efficient modeling frameworks that incorporate computation, dynamical systems, and control theory that will become more widespread and impactful in the design of electro-mechanical and biological therapeutic machines. Website: https://mayalulab22.sites.stanford.edu
Host: Professor: Geir Dullerud