Showing 21-30 of 42 Results
George D. Smith Professor of Molecular and Genetic Medicine and Professor of Pathology and of Genetics
Current Research and Scholarly InterestsWe study natural cellular mechanisms for adapting to genetic change. These include systems activated during normal development and those for detecting and responding to foreign or unwanted genetic activity. Underlying these studies are questions of how a cells can distinguish information as "self" versus "nonself" or "wanted" versus "unwanted".
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?
David Starr Jordan ProfessorOn Leave from 10/01/2022 To 06/30/2023
Current Research and Scholarly InterestsEvolutionary & ecological dynamics & diversity, microbial, expt'l, & cancer
Professor of Radiology (Cardiovascular Imaging)
Current Research and Scholarly InterestsNon-invasive Cardiovascular Imaging
Contrast Medium Dynamics
Assistant Professor of Mechanical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsHuman Computer Interaction, Haptics, Robotics, Human Centered Design
Professor of Medicine (Oncology) and of Genetics and, by courtesy, of Pediatrics
Current Research and Scholarly InterestsMammalian DNA repair and DNA damage inducible responses; p53 tumor suppressor gene; transcription in nucleotide excision repair and mutagenesis; genetic determinants of cancer cell sensitivity to DNAdamage; genetics of inherited cancer susceptibility syndromes and human GI malignancies; clinical cancer genetics of BRCA1 and BRCA2 breast cancer and mismatch repair deficient colon cancer.
Assistant Professor of Bioengineering and of Genetics
Current Research and Scholarly InterestsThe Fordyce Lab is focused on developing new instrumentation and assays for making quantitative, systems-scale biophysical measurements of molecular interactions. Current research in the lab is focused on three main platforms: (1) arrays of valved reaction chambers for high-throughput protein expression and characterization, (2) spectrally encoded beads for multiplexed bioassays, and (3) sortable droplets and microwells for single-cell assays.
Michael B. Fowler, MBBS, FRCP
Professor of Medicine (Cardiovascular), Emeritus
Current Research and Scholarly InterestsAdrenergic nervous system; beta-adrenergic function in, heart failure; drugs in heart failure.
Professor of Statistics and of Computer Science
BioEmily Fox is a Professor in the Department of Statistics and, by courtesy, Computer Science at Stanford University. Prior to Stanford, she was the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington. From 2018-2021, Emily led the Health AI team at Apple, where she was a Distinguished Engineer. Before joining UW, Emily was an Assistant Professor at the Wharton School Department of Statistics at the University of Pennsylvania. She earned her doctorate from Electrical Engineering and Computer Science (EECS) at MIT where her thesis was recognized with EECS' Jin-Au Kong Outstanding Doctoral Thesis Prize and the Leonard J. Savage Award for Best Thesis in Applied Methodology.
Emily has been awarded a CZ Biohub Investigator Award, Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan Research Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Her research interests are in large-scale Bayesian dynamic modeling, interpretability and computations, with applications in health and computational neuroscience.
Paige Fox, MD, PhD, FACS
Associate Professor of Surgery (Plastic and Reconstructive Surgery)
BioDr. Paige Fox is Board Certified Plastic Surgeon who specialized in hand surgery, reconstructive microsurgery, as well as peripheral nerve and brachial plexus surgery. She is an Associate Professor in the Division of Plastic and Reconstructive surgery in the Department of Surgery. She works with adult and pediatric patients. Her research focuses on wound healing, disorders of the upper extremity, and surgical biosensors. Dr. Fox has a passion for sustainability and health care's effect on the environment. She is involved in efforts to green the OR and the clinics at Stanford.