(2hw) Computational Modeling of Cellular Metabolism across Spatiotemporal Scales for Health and Biotechnology Applications.

My research interests lie in leveraging the intersection of multi-scale computational models integrated with advanced research techniques to solve problems in the health sector, biotechnology, and environmental remediation. Cells don’t work alone, but work together in multicellular systems to coordinate disease and infection, produce desired chemicals, and remediate environmental pollutants. Within multicellular systems may exist genetic and phenotypic heterogeneity due to the evolution of genetic mutations and emergence of metabolite gradients. Cellular heterogeneity can result in dysregulated cell states causing disease or altered functions impacting desired qualities. Mechanistically measuring and mapping this multicellular heterogeneity is experimentally challenging due to mechanisms occurring across multiple spatial (i.e. intracellular to extracellular environments) and temporal scales (second to days). Current experimental techniques lack simultaneous and dynamic measurements across scales resulting in incomplete datasets and difficulties to uncover mechanisms. Computational mechanistic models are powerful tools to bridge mechanisms across spatiotemporal scales in complex multicellular systems to enable more efficient data collection and predictive outcomes of cellular behavior.

In my work, I have developed a computational tool that bridges multicellular metabolic mechanisms across spatiotemporal scales including: 1) intracellular metabolism (i.e. the complex biochemical metabolic network cells use to consume nutrients for growth), 2) physical cell behaviors (i.e. cell division, adhesion, movement), and 3) extracellular metabolite transport. With my computational model, simulations can predict the underlying genetic and metabolic mechanisms that control emergent multicellular disease and functions.

This computational model has a wide variety of applications to study disease mechanisms in multicellular systems including pathogenic biofilms, diseased tumor states, or human-microbe interactions. The computational model has options to integrate –omics datasets to accurately represent the biological state of different cell phenotypes. Thus, the computational model can quantitatively predict and inform important perturbations (i.e. gene knockouts and altered extracellular nutrient conditions) to test experimentally, saving experimental time and resources. Simulations can also inform how perturbations to individual cell types affect the multicellular function, informing potential disease mechanisms or optimal treatment strategies. I am interested in gaining expertise in spatial transcriptomics techniques and microfluidic disease models to further constrain, validate, and generate predictions from the computational model for complex cell types and diseased states.

This computational model also has applications to map and mechanistically predict multicellular systems for microbial engineering applications. Leveraging dense, multicellular microbial biofilms for bioremediation, production of desired chemicals, and chemical sensing applications is highly advantageous to reduce the industrial volume required, potentially improve scale-up challenges, and easily recover genetically engineered cells from the environment. However, understanding and predicting a multicellular biofilm function is challenging due to the possible heterongeity in cell states within the biofilm. Additionally, division of labor of cellular functions is suggested to be an optimal method to reduce individual microbial burden and enhance population-scale biofilm functions. Thus, I am interested in learning molecular biology techniques and 3D printing technologies to couple with the computational model to further quantitatively measure and predict how diverse microbial functions and biofilm structures can be optimized to achieve desired functions for microbial engineering and remediation applications.