Location is everything

As a biologist and mathematician by training, my fascination lies in leveraging computational techniques to redefine our understanding and prediction of biological phenomena at an unprecedented scale. This interest has been a consistent theme throughout my academic research, beginning with my undergraduate focus on single molecule and single cell studies, and evolving to encompass population-level dynamics now.

Currently, in the laboratory of Professor Lingchong You, I am using mathematical models and machine learning to investigate pattern formation in living systems. I am deeply invested in understanding how spatial microbial communities are formed and operate across various temporal and length scales, how to program de novo pattern formation, how pathogens spread in space, and how phenotypic characteristics can be leveraged for practical applications such as material fabrications and biocomputing.

Pattern formation, a widespread phenomenon in nature, emerges from the complex interplay of cell-cell and cell-environment interactions. The words of Richard Feynman, "What I cannot create, I do not understand," resonate deeply in this context. Although synthetic biology has enabled creation of diverse types of dynamics, programming pattern formation continues to present significant challenges. My PhD research is dedicated to addressing these challenges, both computationally and experimentally. In a recent work, I used generative ML to accelerate solving PDE numerical simulations. My approach of combining mechanistic modeling and ML enabled discovery biological rules.

Relevant publication: 

• Discovery of interpretable pattern formation rules by integrating mechanistic modeling and deep learning, bioRxiv, 2024
• Advances and challenges in programming pattern formation using living cells, Current opinion in chemical biology, 2022

The majority of microbial communities exhibit spatial structuring, ranging from biofilms to gut microbiota. The spatial structure is pivotal in influencing the fitness, function and evolution of these communities. For instance, Pseudomonas aeruginosa is known to form extensive, branching colonies. We showed that bacteria optimize their chance of survival through forming spatial patterns. Through serial spatial evolution, we demonstrated that the population's spatial expansion can extend the duration of cooperative swarming, making the community susceptible to cheaters. It uncovered a novel mechanism by which spatial structure can modulate cooperation and ultimately shape the evolutionary trajectory of microbial communities.

Relevant publication: 

• Collective colony growth is optimized by branching pattern formation in Pseudomonas aeruginosa Molecular systems biology, 2021
• The collapse of cooperation during range expansion of Pseudomonas aeruginosa, Nature Microbiology, 2024 

Biology is a natural information processor by transforming genetic and environmental information into a diverse array of phenotypes. Inspired by this concept, I developed a workflow for encoding and decoding information within biological self-organized patterns. This approach leveraged noise and fuzziness in biology, compared to conventional computing. 

In an on-going collaboration, this concept is being further explored and enhanced through the integration of AI and external stimuli. Stay tuned for more updates!

Relevant publication: 

• Distributed information encoding and decoding using self-organized spatial patterns, Patterns, 2022

In sequencing and diagnostics, precise control over DNA molecules is often crucial. DNA molecules are charged and forms an electric dipole moment within an in vitro environment. Under the guidance of Professor Walter Reisner and Professor Sarah Mahshid, I designed and fabricated microfluidic chips for controlling DNA single molecules and studied how they respond to electric field, as well as their interactions under nano-confinement.

Relevant publication: 

Transverse dielectrophoretic-based DNA nanoscale confinement, Scientific reports, 2018

Super-resolution microscopy is an invaluable tool for tracking intracellular dynamics and investigating biological processes. In Professor Rodrigo Reyes-Lamothe lab, I studied DNA polymerase with PALM and SBML modeling.