Session: Rising Stars of Mechanical Engineering Celebration & Showcase

Paper Number: 147924

147924 - Bacterial Multiphysical Interactions With Hard and Soft Materials Interfaces: Towards Computational Design of Engineered Living Materials 

Diverse sets of mechanisms underpin the ubiquity of bacterial interactions with surfaces and allow different bacterial species to adhere and proliferate. These mechanisms can often be effectively harnessed as guiding principles for producing materials for engineering purposes. For instance, microbially induced carbonate precipitation can help create new building materials that are less energy-intensive to manufacture and have lower lifetime operating costs. We computationally coupled multiphysical interactions in individual-based models to determine how bacteria form biofilm and biomineralize on patterned surfaces and in porous structures. We modeled bacteria cells and other structures as discrete, granular particles that have both biological (growth, death, biochemistry) and physical (stiffness, adhesion) properties. These models were implemented in the software platform, Newcastle University Frontier in Engineering Biology (NUFEB), which is built on top of the easily available, open-source molecular dynamics (MD) software, Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). We simulated bacteria growth and adhesion in porous structures, while capturing the morphology of the resulting biominerals and biofilms that form. We also determined the chemical and nutrient environment needed to sustain bacteria within concrete subjected to biomineralization. Structural designs were proposed using data-driven modeling to optimize targeted biofilm properties, such as growth or adhesion. Similarly, differences in the molecular structures of healthy and diseased mucus in the human gut can alter the adhesion of bacteria on mucus. Intestinal mucus is the first line of microbial defense, a comparatively soft interface that allows for bacteria adhesion but obstructs penetration. We have constructed a dual-component, coarse-grained model of mucus, specifically designed for the environment of colorectal cancer. This model incorporates Muc2 and Muc5AC, the two most prevalent glycoproteins in gastrointestinal (GI) mucus. Our model illustrates the impact of molecular composition and concentration on the size of mucus pores, which is a crucial factor in nanoparticle permeability. We used this computational model to examine the diffusion rate of nanoparticles coated with polyethylene glycol (PEG), a common type of muco-penetrating nanoparticle. Our model was validated using experimentally determined mucus pore sizes and the diffusion coefficients of PEG-coated nanoparticles in mucus obtained from cultured human colorectal goblet cells. We utilized machine learning fingerprints to gain a mechanistic insight into the diffusional behavior of nanoparticles. Our findings indicate that larger nanoparticles are more likely to be trapped in mucus for extended periods, but they exhibit more ballistic diffusion over shorter durations. These findings suggest that our model offers a valuable tool for studying pharmacokinetics in the GI mucus layer.

Presenting Author: Jingjie Yeo Cornell University

Presenting Author Biography: Prof. Jingjie Yeo is an assistant professor in Cornell University’s Sibley School of Mechanical and Aerospace Engineering. He is the Principal Investigator at the J² Lab for Engineering Living Materials. The overarching goal of his research program is to lead advances in computationally designing and characterizing environmentally sustainable materials, with a focus on bacteria-based engineered living materials (ELMs) and the biopolymers that they produce. He is also a co-instructor in Station1, a social nonprofit organization dedicated to building the foundations of the university of the future through educational opportunity and socially-directed frontier STEM education, research, and internships. Prof. Jingjie Yeo is the recipient of multiple awards, including the NSF's most prestigious award, the NSF CAREER award, and the highest teaching award in Cornell's College of Engineering, the Dennis G. Shepherd Excellence in Teaching Award. Before joining Cornell University in 2020, Prof. Yeo was a research scientist in the Institute of High Performance Computing, Singapore where he helped develop cutting-edge, silk-based cosmeceuticals. Prior, he was a postdoc at both Tufts University and Massachusetts Institute of Technology, where he developed and performed numerous multiscale simulations with density functional theory and fully-atomistic to coarse-grained molecular dynamics modeling on a broad variety of biomaterials such as squid skin, silk and silk-elastin-like proteins, and graphene. He received both his Ph.D. and his B.Eng. degrees from the School of Mechanical and Aerospace Engineering in Nanyang Technological University Singapore.

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