David A. Bell Ph.D.
Laboratory of Receptor Biology and Gene Expression
• University at Albany (SUNY) – B.S. (Physics) – 1996-2000
• University of Tennessee Space Institute – M.S./Ph.D. (Physics) – 2000-2006
• Sigma Pi Sigma Physics Honor Society
2000-2006: Graduate Research Assistant, University of Tennessee Space Institute
Training in general physics with an emphasis on optics. Research focused on the development of instrumentation and data-reduction methods to use the sensitive detection of fluorescence by confocal microscopy as a means of monitoring molecular dynamics.
• Constructed custom microscope for Single-Molecule Detection and Imaging by Laser-Induced Fluorescence using both epi-illumination and prism-based total internal reflection (TIRF).
• Aided in development of data collection and analysis software for Fluorescence Correlation Spectroscopy (FCS).
• Developed software to a) analyze temporal brightness fluctuations (blinking) of single fluorescent molecules, and b) deconvolve scattered light signal from time-correlated single-photon counting (TCSPC) measurements to extract fluorescence lifetime.
2007-2010: Postdoctoral Associate, Virginia Bioinformatics Institute
Live cell imaging to measure expression of proteins regulating the cell cycle in Saccharomyces cerevisiae, and the characterization of synthetic biology constructs in Escherichia coli.
• Developed image processing algorithm to automatically identify cells in phase-contrast microscope images, and track changes in their mean and localized fluorescence over time.
• Trained and mentored undergraduate and high-school students in the use and analysis of microscopy, flow cytometry and bulk fluorescence measurements for investigations of gene expression.
2011-2014: Senior Research Associate, Virginia Bioinformatics Institute
Live- and fixed-cell imaging to study cell cycle regulation in Saccharomyces cerevisiae, with added responsibilities of laboratory management and software development.
• Developed algorithms to automatically count in hundreds of individual yeast cells the number of mRNA molecules from cell cycle regulating genes tagged by single-molecule fluorescence in situ hybridization (FISH), and detected with fluorescence microscopy.
• Led the development of GenoSIGHT, an adaptive microscope control system written in MATLAB to automate the characterization of gene expression in live cells.
• Created models of gene networks using both COPASI and MATLAB’s SimBiology toolbox
• Directly supervised a team of four undergraduate students as part of the 2011 International Genetically Engineered Machines (iGEM) competition. The goal of the students’ project was to characterize the maturation kinetics of multiple fluorescent proteins in both yeast and bacteria.
2014-present: Staff Scientist, OMC .
Develops methods of quantification of Single Molecule Tracking, smFIS data modeling. Supports SMT and cell segmentation in tissues. Builds and maintains custom microscopes.
Professional Activities at the NCI/NIH
Dr. Ball provides expert advice and support to Core customers on data collection, data analysis and quantification including but not limited to quantitative analysis of high-resolution statistical mapping of the super-resolution images, FRAP, FCS, RICS, Single molecule tracking, Number and Brightness analysis, and Single Molecule FISH quantification. Dr. Ball is an expert in assembling prototype microscopes that are not yet available commercially and that utilize new optical principles and technical design. Currently Dr. Ball develops instrumentation for the Single Molecule Tracking/Super-resolution microscope and TILT lattice
• Kongju National University, Department of Chemistry, Kongju, South Korea, 2006
1. Feedback-controlled image acquisition: “Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology,” D.A. Ball, M.W. Lux, N.R. Adames, and J. Peccoud, PLoS ONE. 9 (9): e107087 (2014)• University of Tennessee Space Institute – M.S./Ph.D. (Physics) – 2000-2006
2. FISH: “Measurement and modeling of transcriptional noise in the cell cycle regulatory network,” D.A. Ball, N.R. Adames, N. Reischmann, D. Barik, C.T. Franck, J.J. Tyson, and J. Peccoud, Cell Cycle, 12 (19), 3203 (2013).
3. Live Imaging: “Oscillatory dynamics of cell cycle proteins in single yeast cells analyzed by imaging cytometry,” D.A. Ball, J. Marchand, M. Poulet, W.T. Baumann, K.C. Chen, J.J. Tyson, and J. Peccoud, PLoS ONE, 6 (10): e26272 (2011).
4. Live imaging and Modeling: Stochastic exit from mitosis in budding yeast: Model predictions and experimental observations. D.A. Ball, T.-H. Ahn, P. Wang, K.C. Chen, Y. Cao, J.J. Tyson, J. Peccoud and W.T. Baumann, Cell Cycle, 10 (6), 999 (2011).
5. Feedback-controlled image acquisition: Cyto•IQ: An adaptive cytometer for extracting the noisy dynamics of molecular interactions in live cells. D.A. Ball, S.E. Moody, and J. Peccoud, in Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues VII, D.L Farkas, D.V. Nicolau, and R.C. Leif, eds., Proc. SPIE, 7568, 75681D (2010).
6. Single Molecules: “Single-Molecule Detection with axial flow near micron-sized capillaries,” D. A. Ball, G. Shen, and L. M. Davis, Appl. Opt., 46, 1157-1164 (2007)
7. FCS: Data reduction methods for application of fluorescence correlation spectroscopy to pharmaceutical drug discovery. L.M. Davis, P.E. Williams, D.A. Ball, E.D. Matayoshi, and K.M. Swift, Curr. Pharm. Biotech. 4, 451-462 (2003).
8. FCS: Dealing with reduced data acquisition times in fluorescence correlation spectroscopy for HTS applications. L. M. Davis, D. A. Ball, P. E. Williams, E. D. Matayoshi, and K. M. Swift, in Microarrays and Combinatorial Technologies for Biomedical Applications, D. V. Nicolau and R. Raghavachari, eds., Proc. SPIE, 4966, 117-128 (2003)
9. Single Molecules: Imaging of single-chromophore molecules in aqueous solution near a fused-silica interface. L.M. Davis, W.C. Parker, D.A. Ball, J.G.K. Williams, G.R. Bashford, P. Sheaff, R. Eckles, D.T. Lamb, and L.R. Middendorf, Proc. SPIE, 4262, pp. 301-311 (2001)
10. SMT: Ball, D.A., Mehta, G. D., Salomon-Kent, R., Mazza, D., Morisaki, T., Mueller, F., McNally, J.G., Karpova, T. S. Single Molecule Tracking Of Ace1p In Saccharomyces cerevisiae Defines A Characteristic Residence Time For Non-specific Interactions Of Transcription Factors With Chromatin. (2016) Nucleic Acids Res 44:e160.
11. SMT: Presman, D., Ball, D., Paakinaho, V., Grimm, J., Lavis, L., Karpova, T., Hager, G.: Quantifying transcription factor dynamics at the single-molecule level in live cells. (2017) Methods 123: 76-88 PMID: 28315485
12. SMT: Paakinaho, V., Presman DM, Ball D, Schiltz, RL, Levitt, P, Johnson, T, Mazza D., Morisaki T, Karpova TS, Hager GL. Single-Molecule analysis of steroid receptor and cofactor actin in living cells. (2017). Nature Communications 8: 15896.
13. Quantification of cells in tissues: Rahman, M.A., McKinnon, K.M., Karpova T.S., Ball D.A., Venzon, D. J. Fan, W., Kang, G, Li, Q., Robert-Guroff, M. Associations of Simian Immunodeficiency Virus (SIV)-Specific follicular CD8 T cells with Other Follicular T cells suggest complex contributions to SIV Viremia control. (2018) J. Immunol. 200: 2714-2726.
14. SMT: Serebryannyy, L. A., Ball, D. A., Karpova T. S., Misteli, T. Single molecule analysis of lamin dynamics (2018) Methods, 157:56-65
15. SMT and smFISH: Mehta G.D., Ball D. A., Eriksson, P. R., Chereji, R. V., Clark D. J., McNally J. G., Karpova, T. S. Single-Molecule analysis reveals linked cycles of RSC chromatin remodeling and Ace1p transcription factor binding in yeast (2018) Molecular Cell 72: 875-887