In my administrative role as Associate Chief Medical Information Officer at Stanford Children’s Health, I oversee the development and maintenance of clinical decision support tools within the electronic medical record. These clinical decision support tools are designed to enhance patient safety, efficiency, and quality of care. My research focuses on rigorously evaluating–1) how these tools affect clinician knowledge, attitudes, and behaviors; and 2) how these tools affect clinical outcomes and efficiency of health care delivery. As a pediatric intensivist, I have a specific interest in tools that facilitate improved situational awareness and information processing in the ICU as well as early warning systems that can detect clinical deterioration.
Dr. Nolan’s laboratory focuses on the analysis of biological events at the single cell level using novel genetic and FACS-based approaches at the intersection of immunology, autoimmunity, biochemistry, and cancer. The laboratory studies phospho-protein immune cell and cancer signaling, and other metabolic parameters by analysis of biochemical functions at the single cell level in primary cell populations. This includes interrogation of cancer (Cell, 2004) and immune signaling networks in complex cell populations (Science, 2005), drug screening approaches (Nature Methods, 2005, (cover article), Nature Chemistry and Biology, 2007a, Nature Chemistry and Biology, 2007b (cover article)) and using multiparameter data to stratify signaling maps from patient samples, (Cancer Cell, 2008, cover article). Other major interest areas of the laboratory include mapping of signaling networks within complex populations of immune cells, developing systems biology approaches to develop an atlas of immune cell differentiation (Nature Biotechnology, In Press, 2011), the development of mechanism-based diagnostics for use in clinical trial studies. The data generated at the single cell level ranges from 10-15 parameters per cell (hundreds of thousands of cells per sample, and dozens of samples per experiment) to up to 50-100 parameters per cell using a new mass spectrometer flow cytometer we have co-developing and recently published upon (Bendall et al, Science, 2011). To analyze these datasets and infer signaling networks within each cell subpopulation, we have developed novel hardware (Field Programmable Gate Arrays and GPUs tethered to standard CPUs with novel compiler/distributor architecture) to implement the more computationally intensive algorithms we are using for our Bayesian inference and other bioinformatics approaches. The combination of hardware/software/biology applied in the laboratory to clinical samples sits at the edge of the translational arena in that we focus on developing techniques that can provide mechanistically relevant answers to clinicians while simultaneously helping biologist answer questions of basic importance to biology.
Primary research interests are in the areas of clinical and research information systems design, development and evaluation including multimedia clinical systems, integration of data to support patient care and clinical research, biomedical terminologies, automated indexing of biomedical documents, cancer information systems and biomedical data security.
The landscape of RNA editing in the transcriptomes The main interest of Jin Billy Li’s lab is to identify and interpret the RNA editing sites using a variety of approaches including genomics, technology development, and computational biology. RNA editing is a phenomenon where genomically encoded information is changed in the RNA. Adenosine-to-Inosine (A-to-I) editing is the most common type of editing, and is achieved by enzymes called Adenosine deaminase acting on RNA (ADAR). RNA editing is critical because ADAR knockout mice die before or shortly after birth. Despite the fact that RNA editing was first discovered over twenty years ago, it has been surprisingly under appreciated and under explored. Very few RNA editing sites had been discovered in humans, mainly due to technological barriers. We recently expanded the RNA “editome” to about 400 sites by computational prediction followed by targeted next generation sequencing (Li et al., Science 2009, 324:1210-1213). This, however, is probably just tip of the iceberg. Our lab will continue the discovery of the RNA editing sites in the transcriptomes of human and may model organisms, as well as various disorders such as autism and cancers. Our main approach is next generation sequencing and computational data analysis. Bioinformatics skills are also needed in a genome-wide association study to link genetic variations with the RNA editing level of a nearby editing site. In a longer term, we aim to perform functional genomic screening of these newly identified RNA editing sites.