We study non-genetic mechanisms of cancer metastasis, with particular focus on the roles cell morphogenesis plays as a driver of oncogenic signals. The analysis of the underlying mechanochemical systems requires the reconstruction of complex regulatory circuitry without experimental interventions. Our lab melds molecular cell biology, live cell imaging, computer vision, and graphics to generate high quality observational data cues that permit the inference of relations between circuit components in minimally perturbed biological systems.
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.
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 algorithms and statistical methods to accurately and rapidly analyze biological data, in particular, genomic and transcriptomic data. We are creating a new computer programming language called L++ that enables researchers to represent genomes in both human and computer-interpretable ways. In parallel, we are developing a platform to run this organism code, i.e. to simulate and visualize organisms, with the goal of in-silico execution of organisms faithfully mirroring in-vivo execution. We plan to use this platform to design and create novel organisms.
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.
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.
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.
With the aim of understanding the extent of phenotypic variability and its underlying cause, we investigate genetic and molecular basis using state-of-the-art experimental and computational tools. Our projects span the areas of antibiotic resistance and persistence, protein evolution, and novel antibiotic compounds that can select against resistance.
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 resitance. 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.