Interdisciplinary PhD in Structural and Computational Biology and Quantitative Biosciences

Reid C. Van Lehn

Conway Assistant Professor, Department of Chemical and Biological Engineering Lab Website 263-9487

3012 Engineering Hall
1415 Engineering Drive
Madison, WI 53706-1607


B.S., Massachusetts Institute of Technology
Ph.D., Massachusetts Institute of Technology
Postdoctoral, California Institute of Technology

Biomolecular simulation; nano-bio interactions; complex bio interfaces; cotranslational protein synthesis; biomembranes

Research in the Van Lehn group is thematically organized around the goal of relating molecular properties to macroscopic function by employing a variety of simulation techniques to interrogate the behavior of soft materials at multiple length and time scales. We focus on understanding and predicting interfacial behavior, studying both synthetic (e.g., nanoparticles, liquid crystals, and peptides) and biological (e.g., proteins, lipids, and biosurfactants) soft materials in close collaboration with experimentalists. Some of our current research areas are detailed below. For all research areas visit the Lab Website.

Integrating Molecular Simulations and Machine Learning for Soft Materials Design

image for Integrating Molecular Simulations and Machine Learning for Soft Materials DesignAn outstanding challenge – and opportunity – for soft materials design is the large set of parameters that can be manipulated synthetically. For example, surface coatings can be created from varying mole fractions of ligands whose physical and chemical properties are dictated by a wide selection of functional groups. Moreover, macroscopic behavior is highly sensitive to these choices; for example, the cellular uptake of nanoparticles depends strongly on particle surface charge and size. Developing design rules for these systems is challenging due to the difficulty in experimentally screening material compositions or modeling long-timescale processes with chemical accuracy. We are developing methods to combine molecular simulations with machine learning techniques to quantitatively predict experimental observables and guide the exploration of high-dimensional design spaces. This approach enables the rapid screening and design of soft materials for applications in drug delivery and catalysis.

Multiscale Modeling of Nanoparticle Interactions with Lipid Bilayers

Multiscale Modeling of Nanoparticle Interactions with Lipid BilayersSynthetic nanoparticles <10 nm in diameter are promising materials for biomedical applications including drug delivery and biosensing. These applications require understanding nanoparticle interactions with the lipid bilayer – the primary structural component of the cell membrane – which dictate critical outcomes including cellular uptake and cytotoxicity. Nanoparticle-bilayer interactions are challenging to predict because they depend on processes occurring at multiple length scales, such as contact between nanoparticle surface components and lipids at a <1 nm length scale and the deformation of the bilayer at ~10-100 nm length scales. We perform atomistic and coarse-grained simulations, coupled with advanced sampling techniques, to model nano-bio interactions for a range of experimentally accessible nanoparticles. These simulations can guide the design of nanoparticles that bypass the cell membrane or assemble on the cell surface for applications in drug delivery and photothermal therapy.

Photo of Reid C. Van Lehn

Areas of Expertise

  • Biotechnology
  • Computational Biology & Bioinformatics
  • Membrane & Cellular Biophysics