Overview of current project areas.

Area 1. Systems biology

We seek to understand design principles of biological systems and diseases. 

Fly eye, and mouse gut orgnoid
Current areas of investigation include: 
1) Tissue patterning: We combine intravital imaging, novel organoid systems, computer vision and mathematical modeling to understand how tissues pattern and maintain homeostasis. Two ongoing projects are: neural circuit wiring in the fly (In the news) and morphogenic signaling in intestinal epithelium. 
2) Tissue disregulation: We study mechanisms underlying how biological networks rewire during disease progression. Our main areas of focus are: the emergence of drug resistance in cancer (In the news) and the dis-regulation of proteostasis in neurodegeneration (Exciting new area in the lab!).

Area 2. Systems pharmacology

We seek to develop rapid, scalable and effective ways to identify drugs.

Current areas of investigation include:
1) Machine learning.  We develop computational strategies to predict small molecule function from diverse data sources, including high-throughput microscopy and genomics information.
2) Drug discovery. We combine systems-biology and high-throughput approaches for discovering early drug leads for cancer and neurodegeneration.  (In the news.)

Overview of published and previous work.

Spatial patterning. In previous work, we focused on understanding design principles underlying the ability of cells to form spatial patterns, or “polarize”. Highlights of our past research program include the discovery of a fundamentally new polarity mechanism, referred to as the "neutral drift model," in which positive feedback drives recurrent stochastic clustering of signaling molecules, a new approach for reverse-engineering information flow among signaling modules in rapidly polarizing human neutrophils, and an investigation into the developmental rules of the neural superposition brain circuit in the fly visual system. 

Relevant publications include: 1. Spontaneous cell polarization through actomyosin-based delivery of the Cdc42 GTPase, Science; 2. Endocytosis optimizes the dynamic localization of membrane proteins that regulate cortical polarity, Cell; 3. On the spontaneous emergence of cell polarity, Nature; 4. A density-dependent switch drives stochastic clustering and polarization of signaling molecules, PLoS Computational Bio.; 5. Network crosstalk dynamically changes during neutrophil polarization, Cell; 6.  The developmental Rules of Neural Superposition in Drosophila. Cell.

Drug discovery. Over the past decade, our lab pioneered computer vision and machine learning approaches for scalable, microscopy-based drug discovery. We developed approaches for identifying optimal biomarkers, extracting informative signatures of cellular perturbations and quantifying stereotyped cellular responses. Our approaches are at the heart of many of our research projects and enable quantitative characterization of individual and collective cellular behaviors. Our methodology has been widely adapted in academics and industry. 

Relevant publications include: 1. Multidimensional drug profiling by automated microscopy. Science; 2. Image-based multivariate profiling of drug responses from single cells. Nature Methods; 3. Characterizing heterogeneous cellular responses to perturbations. PNAS; 4. An approach for extensibly profiling the molecular states of cellular subpopulations. Nature Methods; 5. SimuCell: a flexible framework for creating synthetic microscopy images. Nature Methods; 6. PhenoRipper: software for rapidly profiling microscopy images. Nature Methods; 7. A simple image correction method for high-throughput microscopy. Nature Methods.; 8. GSK-3 modulates cellular responses to a broad spectrum of kinase inhibitors. Nature Chemical Biology; 9. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nature Biotechnology.

Cellular heterogeneity. Our lab pioneered experimental, computational and theoretical approaches for identifying functional roles in patterns of cell-to-cell differences. We showed that patterns of cell-cell differences can reveal hidden biological information about how cells make decisions and serve as an informative readout of disease progression and drug response. Understanding the origins and impact of cellular heterogeneity has profound implications to all fields of biology, from understanding emergent behaviors of living systems, to developing improved approaches for tissue engineering, to treating disease. 

Relevant publications include: 1. Characterizing heterogeneous cellular responses to perturbations. Proc Natl Acad Sci.; 2. Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities. Mol Syst Biol.; 3. Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3-L1 preadipocytes. J Cell Biol.; 4. Cellular heterogeneity: do differences make a difference? Cell; 5. Identifying network motifs that buffer front-to-back signaling in polarized neutrophils. Cell Reports. 6. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nature Communications.