Primary Appointments, Tenure Track
Our research is focused on elucidating cell morphogenesis and its impact on oncogenic signaling. Using advanced live cell imaging in 3D environments, we measure dynamics of cell architecture, forces, and chemical signals to subsequently incorporate these findings with molecular details and spatiotemporal resolution for informing predictive models of the coupling between cell shape and signal transduction.
Our lab is focused on development of advanced imaging technologies that can provide unprecedented insight into cell biology in 3D environments, both ex-vivo and in-vivo. Our work has led to improvements of the state-of-the-art in fluorescence microscopy, and to new insights into imaging theory. Most importantly, our instrumentation has become an integral part in multiple interdisciplinary research programs across UT Southwestern.
Our lab develops computer algorithms and statistical methods to accurately and rapidly analyze biological data with a focus on sequencing data. We have embarked upon a long-term synthetic biology project where we reverse-engineer biochemical pathways and represent them as high-level programming code, then use forward engineering to obtain a whole genome sequence from this code to transform the sequence into living organisms. We also apply our expertise to develop AI-based methods for flow cytometry, immunohistochemistry, and karyotyping data analyses to support disease diagnoses.
We study complex interactions between cancer and our immune system in the tumor microenvironment using genomics, computation, statistics, and experimental approaches. Our long-term research goal is designing immune checkpoint blockage therapies based on predictive biomarkers that inform clinical diagnoses and prognoses.
Our research is focused on the development of theoretical models combined with computational tools to deduce how system-wide properties emerge from elementary interactions within biological systems to enable function in the relevant time scale and why such emergence is evolutionarily scalable. These questions are explored in the context of protein folding, dynamics, aggregation, and signal transmission in molecular and cellular networks.
We develop the theory and application of deep learning to inform diagnoses, prognoses, and therapeutic decision making in healthcare. Our research strives to constantly improve predictive models to make them more interpretable, more accurate, less biased so that new patterns can be gleaned from information-rich biomedical data. We integrate data from non-invasive neuroimaging, multi-omics (genomics, proteomics, metabolomics) and clinicodemographics to build and enhance predictive models for neurodegenerative diseases, neurodevelopmental disorders, and mental disorders.
We focus on developing biology-guided machine learning analyses to unravel the information in tissue morphology. We apply these methods in two broad areas. First, in the context of kidney cancer, we connect tumor architecture to underlying molecular state, drug response and tumor evolution. Second, in the context of neurodegenerative tauopathies, we use morphology and spatial distribution of protein aggregates to better stratify diseases and reconcile their classical neuropathology definitions with the emerging understanding based on protein conformations.
Our laboratory uses sequence information drawn from diverse organisms to understand the relationship between genotype and phenotype, with a focus on bacteria. The central idea is to build models that describe the “design” of molecular systems through the statistical analysis of evolutionary conservation and co-evolution. We test these models experimentally using deep mutational scanning of individual proteins, genome-wide second-site suppressor screens, and high-throughput CRISPRi measurements that relate variation in gene expression to bacterial growth rate.
We study computational principles of neural information processing in the brain by developing normative theories and neural circuit models. We collaborate closely with experimental neuroscientists to verify our models and provide experimental predictions, and also collaborate with computer scientists to provide insights in developing artificially intelligent algorithms.
Our research involves the development of machine learning and statistical methods for biomedical data to decode regulatory genomic circuits, 3D genome architecture, evolutionary remodeling of biological circuits, and subsequent effects on human health. We also focus on building softwares that can rapidly but accurately generate machine learning models tailored to specific research projects with automated statistical inference for robustness and reproducibility.
Primary Appointments, Research Track
Our research is focused on identifying molecular mechanisms that enable cancer cells to colonize and populate a distant tissue. For this, novel methods are needed to pinpoint rare metastatic colonization events in large tissue volumes, with molecular specificity and high resolution. We are approaching this via autonomous microscopy, molecular multiplexing, and content-rich histopathology.
Andrew Jamieson, Ph.D.
Dr. Jamieson enjoys working at the intersection of science, technology, and medicine. He is interested in using computational image analysis to represent complex spatiotemporal phenomena such as live cells, decoding the spatial biology of cancer, and designing video-based machine learning for applications in robotic surgery analysis.
Jeon Lee, Ph.D.
Dr. Lee's research is aimed at innovating and translating computational technology to advance biomedical research and medical diagnoses/treatments. We develop and re-purpose state-of-the-art computational algorithms for big and heterogenous biomedical data such as multi-modal high-throughput screening data, multi-omics data, single cell genomics data, and medical imaging data. We make these algorithms available as computational tools for use in the clinical environment to support and improve medical diagnoses and treatments.
Jungsik Noh, Ph.D.
Dr. Noh's research specializes in deducing causal inference for time series microscopy images and developing statistical methods for genomic data analysis. Currently, he collaborates with peers in the Green Center for Systems Biology and in the Lyda Hill Department of Bioinformatics to develop statistical methods to resolve complex biomedical data. Dr. Noh published a machine-learning based pipeline in 2021 to estimate numbers of COVID-19 infected populations worldwide; focusing on those that are under-ascertained and therefore unknown.
Dr. Wang leads our biomedical high-performance computing center (BioHPC) and assists all researchers with their computational needs and project guidance. BioHPC regularly offers training sessions for UT Southwestern students, staff, and faculty for various bioinformatics topics.
UTSW Distinguished Fellow
Our research focuses on using machine learning to drive better cancer drug discovery. We integrate heterogenous data on chemical-protein interactions, synthetic lethality, and proteo-genomic databases to make accurate targeted therapy predictions. We then test these few drug candidates in clinically relevant assays. These assays are expensive and complex, therefore they cannot be used to run high throughput screening as in traditional drug discovery. However, our accurate computational selection enables us to use them from the beginning, which means that we avoid the errors or missed opportunities caused by using reductionist assays. We already have two patented drug candidates (SU11652 for melanoma and TC-E 5008 for ER+ breast cancer) with more patent applications underway.
Our research leverages high throughput genetics to map the molecular interactions that shape bacterial cellular physiology and functioning. A key area of emphasis is antibiotic resistance. We develop designer mutant libraries for genomic modifications on a high throughput scale that will enable quantification of interactions between cellular components, leading to novel insights and drug design.
Dr. Hon’s primary research focus is to decode transcriptional regulation to understand the genetic basis of disease and to engineer cells for regenerative medicine. His expertise in computational and experimental single cell genomics leads to valuable collaborations with our primary faculty at the level of methods development and interpretation of gene expression patterns.
Dr. Jaqaman’s research focuses on the spatiotemporal organization and mechanics of signaling proteins and the development of quantitative single molecule microscopy. She lends her expertise in Biophysics to complement bioinformatics approaches for developing diverse computational analyses and high-resolution microscopy algorithms to reveal the dynamic interactions between cytoskeletal elements and cell surface receptors.
Dr. Lehmann is an accomplished pediatrics specialist and clinical informatician who continues to make significant strides in expanding the scope of computer automation in daily medical practice. He applies his skills to enhance the design of patient databases in the clinic and develops applications for supporting clinical decisions, which complements and solidifies our Department’s mission to offer innovative computational tools with a real-world impact.
Dr. Toprak’s research spans the areas of antibiotic resistance and persistence, protein evolution, and novel antibiotic compounds that can select against resistance. His expertise in pharmacology is complemented and supported by innovative bioinformatics tools and application design enables identification of phenotypic variability at the genetic and molecular level followed by experimental testing. He is part of a team of faculty housed in the Green Center for Systems Biology focused in establishing a circuit theory of antibiotic resistance.
Dr. Xiao lends his informatics expertise to develop computational models and algorithms for multi-modal patient data spanning images to genomic sequences. The ultimate goal of his research is to assist clinicians in providing patient-specific tailored treatment plans, which complements some of the translational research goals in our Department.
Dr. Xie’s research focuses on development of predictive models for drug response, spatial modeling, and integrative analysis of molecular profiling datasets. As Director of Quantitative Biomedical Research Center (QRBC), she contributes to the design of online tools and packages for interdisciplinary biological research amongst multiple labs. Her knowledge and experience enriches our department’s resources and offers partnerships in serving the broader UTSW community with first-class data science.
Dr. Xing’s collaborative research focuses on identifying genes underlying complex traits of metabolism, pulmonary diseases, and eye diseases. As Director of the Bioinformatics Lab in the McDermott Center, he and his team provide essential and targeted research support via cutting-edge genetic/genomic analysis services. His expertise in epidemiology and population genetics complements the department’s research on genomics and gene regulation