(4id) Modeling Amphiphilic Biomolecules at Interfaces

I am interested in biological processes driven by interfacial properties and collective behavior of molecular assemblies. Cellular membranes provide a platform for biomolecules to organize and assemble into ordered structures for proper biological function (e.g., raft formation). I develop coarse-grained models and use molecular dynamics (MD) simulations in tandem with machine learning and thermodynamics to describe molecular phenomena that underlie these processes. My vision is to build off my research background in membrane proteins and lipoproteins and apply computational methods to improve the treatment and prevention of cardiovascular diseases using biophysical insights gained from computational methods.

As a leader of a research group, I will develop a collaborative research program based on computational biophysics in the context of human health, especially cardiovascular disease. Cardiovascular disease is a leading cause of death and atherosclerosis is the underlying pathologic process that leads to heart attack and stroke. Lipoproteins are large macromolecular assemblies of protein, neutral lipids, and amphiphilic lipids. Their structure and composition are highly dynamic as lipoproteins are continuously exchanging proteins and lipids during metabolism. Their dynamism and heterogeneity make them difficult to probe experimentally. Molecular modeling is a powerful complement to targeted experimental studies. My immediate interests are modeling the structures of atherosclerotic lipoproteins (e.g., low-density lipoprotein, sometimes called “bad cholesterol”) and their interactions with receptors, exchangeable apolipoproteins, and the next generation of peptide-based drugs. My first set of projects will be a foundation in the field of computational chemistry and lipoprotein metabolism. My long-term goals extend to broader applications like peptide-based drug design and modeling macromolecular assemblies (i.e., lipid nanoparticles) and their uptake.

Teaching Interests:

As a faculty member, I can teach any course from the core chemical engineering curricula. My research and teaching experience lends itself to courses like thermodynamics, statistics, and numerical methods. Based on my competencies, I can also develop courses on special topics like biophysics, machine learning, and molecular modeling.

In all courses, my goals are for students to develop competency and intuition, to apply their knowledge to real-world problems, and to explore their scientific interests in a respectful, welcoming environment. For more classes, I would advocator for a project-based structure especially when students are enabled to choose the topic. Projects can provide a framework for students to investigate a topic, to find an application, and to become an expert as well as create deliverables (e.g., presentation or report) to demonstrate their expertise. My opinion is based on my experience as a teaching assistant for a cross-listed molecular modeling course where the first portion was typical lectures and homework assignments and latter part focused on student-led projects implementing modern modeling techniques. Throughout the course, I helped design the homework assignments, mentored them through their projects, and gave a couple of lectures.

Student-led projects demonstrate a need for diversity in the classroom. When the application of chemical engineering is based on student interests, students can engage with the core curriculum on from their perspective. It is difficult for coursework derived from a single interest or perspective to apply to all students and this can lead to a lack of student engagement. I want my students to feel enabled by their coursework to explore their interests and applications of chemical engineering in a classroom based on mutual respect, openness to new ideas, and a commitment to learning.

Research Experience:

My background is in chemical and molecular engineering and my research experience is in computational biophysics, molecular modeling, and thermodynamics. Throughout my research, my goal has been to apply computational tools to explain experimental phenomena and provide insights into human health and disease.

My first research experience as an undergraduate was to better understand ionic liquids and their propensity to dissolve sugars and cellulose. This early experience exposed me to high-performance computing, molecular dynamics (MD) simulations, and enhanced sampling techniques. Most of all, my undergraduate research made me excited about computation and its applications.

As a graduate, I used multiscale approaches to develop coarse-grained models to better understand protein-mediated membrane remodeling, explained perplexing experimental observations, and elucidated lipid-mediated interactions between I-BAR domains that stabilize membrane tubules. This was enabled by a fruitful collaboration with experimentalists that was formative to my scientific aspirations because it showed me the possibilities of synergistic modeling and experiments.

My postdoctoral work continues to apply all-atom and coarse-grained modeling in collaboration with experimentalists. Indeed, part of my work has been related to molecular assemblies with projects involving peptide-lipid self-assembly and ion-mediated organization of negatively charged lipids in bilayers. Most relevant to my future work, I currently simulate structural apolipoproteins to show how they stabilize of lipid assemblies and amphipathic peptide-based drugs with lipoproteins to further their design. The throughline of my research experience is an excitement for using new computational methods from the perspective of statistical mechanics and thermodynamics to better understand experimental phenomena.