Purvis Lab

The Cell Cycle Browser

We built an interactive web interface based on real-time reporter data collected in proliferating human cells. This tool facilitates visualizing, organizing, simulating, and predicting the outcomes of perturbing cell-cycle parameters. Borland D, Yi H, Grant GD, Kedziora KM, Chao HX, Haggerty RA, Kumar J, Wolff SC, Cook JG, Purvis JE. The Cell Cycle Browser: An Interactive Tool for Visualizing, Simulating, and Perturbing Cell-Cycle Progression. (2018) Cell Systems 7(2):180-4. PMID: 30077635
Dynamic Responses

Different dynamical patterns of the tumor suppressor protein p53 alter the selection and timing of gene expression and affect cellular outcomes in response to DNA damage. Purvis JE, Karhohs KW, Batchelor E, Loewer A, Lahav G. p53 dynamics control cell fate. (2012) Science 336(6087):1440-4. PMID: 22700930
DNA Damage Checkpoint Dynamics

Each cell-cycle phase shows a distinct sensitivity and temporal response to DNA damage, explaining why differences in the timing of DNA damage can lead to heterogeneous cell fate outcomes. Chao HX, Poovey CE, Privette AA, Grant GD, Chao HY, Cook JG, Purvis JE. Orchestration of DNA Damage Checkpoint Dynamics across the Human Cell Cycle. (2017) Cell Systems 5(5):445-459.e5. PMID: 29102360
Licensed to Differentiate

Slowing down the rate at which replication origins are licensed causes stem cells to differentiate more readily. These findings show that the processes of cell differentiation and DNA replication are closely linked. Matson JP, Dumitru R, Coryell P, Baxley RM, Chen W, Twaroski K, Webber BR, Tolar J, Bielinsky AK, Purvis JE, Cook JG. Rapid DNA replication origin licensing protects stem cell pluripotency. (2017) Elife 6. pii: e30473. PMID: 29148972
Single-Cell Signaling Patterns

The ability to visualize signaling proteins in real time and at single-cell resolution has revealed a staggering picture of complexity in cellular signal transduction. This complexity is so prevalent that it has spurred the development of new computational strategies to analyze single-cell signaling patterns.

Davis DM, Purvis JE. Computational analysis of signaling patterns in single cells. (2014) Seminars in Cell and Developmental Biology 112(10):4069-79. PMID: 18596227

#soliloquy-container-2381{opacity:1}#soliloquy-container-2381 li > .soliloquy-caption{display:none}#soliloquy-container-2381 li:first-child > .soliloquy-caption{display:block}

Our lab uses computational and experimental approaches to study signaling mechanisms in stem cells and cancer pathways. We are especially interested in understanding the molecular mechanisms underlying “irreversible” cell fate decisions such as apoptosis, senescence, and differentiation. We study the timing and mechanism of these decisions using a combination of time-lapse microscopy and computational modeling. Our ultimate goal is to not only understand how cells make decisions under physiological conditions, but to discover how to manipulate these decisions to treat disease.