School of Engineering


Showing 1-10 of 17 Results

  • Antoine Falisse

    Antoine Falisse

    Postdoctoral Scholar, Bioengineering

    BioDr. Falisse is a postdoctoral fellow in Bioengineering working on computational approaches to study human movement disorders. He primarily uses optimization methods, biomechanical modeling, and data from various sources (wearables, videos, medical images) to get insights into movement abnormalities and design innovative treatments and rehabilitation protocols.

    Dr. Falisse received his PhD from KU Leuven (Belgium) where he worked on modeling and simulating the locomotion of children with cerebral palsy. His research was supported by the Research Foundation Flanders (FWO) through a personal fellowship. Dr. Falisse received several awards for his PhD work, including the David Winter Young Investigator Award, the Andrzej J. Komor Young Investigator Award, the VPHi Thesis Award in In Silico Medicine, and the KU Leuven Research Council Award in Biomedical Sciences.

  • Yuhang Fan

    Yuhang Fan

    Ph.D. Student in Bioengineering, admitted Autumn 2018

    BioI work on understanding the statistical physics and optimization principles of organized biological systems. Specifically, I use planarian as model system to study cell collective behavior and the molecular mechanisms of adaption.

    I am interested in a lot of things: development, evolution, statistical physics, dynamic systems, and biophysics. I also spend time developing sequencing and fluorescence imaging technology required for depicting concrete biological systems.

  • Jeffrey A. Feinstein, MD, MPH

    Jeffrey A. Feinstein, MD, MPH

    Dunlevie Family Professor of Pulmonary Vascular Disease and Professor, by courtesy, of Bioengineering

    Current Research and Scholarly InterestsResearch interests include (1) computer simulation and modeling of cardiovascular physiology with specific attention paid to congenital heart disease and its treatment, (2) the evaluation and treatment of pulmonary hypertension/pulmonary vascular diseases, and (3) development and testing of medical devices/therapies for the treatment of congenital heart disease and pulmonary vascular diseases.

  • Michael Fischbach

    Michael Fischbach

    Associate Professor of Bioengineering and of Medicine (Microbiology and Immunology)

    Current Research and Scholarly InterestsThe human microbiome is linked to a range of phenotypes in the host, but it remains difficult to test causality and explore the mechanisms of these interactions. Our lab focuses on two research areas that share a common goal of studying host-microbiota interactions at the level of molecular mechanism:

    1) Technology development. Much of what we know about biology has been learned by deleting individual genes from mice, worms, flies and yeast. The ability to do single-strain and single-gene deletion in the microbiome would be transformative but does not yet exist. We are developing technology in three areas to make this possible:

    Synthetic ecology: There is a pressing need for model systems for the microbiome that are defined, but of an order of complexity that approaches the native state. Key experiments in the field often show that a host phenotype can be transferred to a germ-free mouse via fecal transplant. If these phenomena could be recapitulated with a defined, high-complexity community, then reductionist experiments to probe mechanism would be possible. We are developing the technology required to build highly complex defined communities (100-200 bacterial species), make them stable upon transplantation into mice, and probe their function in vitro and in vivo.

    Genetics: It is difficult to probe mechanism without genetics, and genetic tools exist for only ~10% of the bacterial species in the gut and skin microbiome. We are developing technologies that will make it possible to delete and insert genes, and build mutant libraries, in many of the most common bacterial strains in the gut and skin microbiome.

    Computation: In previous work from the lab, we have developed computational algorithms that identify small-molecule-producing genes in bacterial genomes. In current work, we are devising algorithms for genome mining that are specific to the microbiome, and new tools for predicting the chemical structures of small molecules from untargeted metabolomics data.

    2) Molecular mechanisms. Many of the early findings in microbiome research are correlative or associative. We are applying the tools described above to explore the mechanisms underlying these phenomena:

    Small molecules: Our lab has had a long-standing interest in small molecules from the microbiota. These include: 1) host-derived molecules metabolized by the microbiome, like bile acids; 2) characteristic components of the bacterial membrane and cell wall, including LPS and capsular polysaccharides; and 3) hundreds of other diffusible small molecules (e.g., the products of polysaccharide and amino acid metabolism) that are present in the bloodstream at high concentrations. Our work in this area seeks to establish the mechanisms by which these molecules modulate host biology, especially by deleting them one at a time in the background of a complex community; and to discover new microbiome-derived metabolites present in the bloodstream and host tissues.

    Ecology of complex communities: Synthetic ecology at the 100+ strain scale is entirely unexplored, and the emergent properties of complex communities are not well understood. Our work in this area seeks to understand basic principles outlined by the following questions: How many meaningful interactions exist in a community of hundreds of strains? What constitutes a niche, molecularly and spatially, and how do strains map to niches? What are the molecular correlates of stability, and how does a community reconfigure in response to a perturbation? How rare or common are stable states, and how predictable is the process by which a consortium will evolve toward a stable state? To what extent do priority effects (early colonists and events) determine the outcome of ecosystem development? Can the results of ecosystem competition be predicted or engineered?