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  1. Hasnain, A., Balakrishnan, S., Joshy, D.M. et al. Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics. Nat Commun 14, 3148 (2023). https://doi.org/10.1038/s41467-023-37897-9 
  2. Fox J, Cummins B, Moseley RC, Gameiro M, Haase SB. A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets. Math Biosci. 2024 Jan;367:109102. doi: 10.1016/j.mbs.2023.109102. Epub 2023 Nov 7. PMID: 37939998; PMCID: PMC10842220.
  3. Motta, F. C., McGoff, K., Moseley, R. C., Cho, C. Y., Kelliher, C. M., Smith, L. M., Ortiz, M. S., Leman, A. R., Campione, S. A., Devos, N., Chaorattanakawee, S., Uthaimongkol, N., Kuntawunginn, W., Thongpiam, C., Thamnurak, C., Arsanok, M., Wojnarski, M., Vanchayangkul, P., Boonyalai, N., Smith, P. L., … Haase, S. B. (2023). The parasite intraerythrocytic cycle and human circadian cycle are coupled during malaria infection. Proceedings of the National Academy of Sciences of the United States of America, 120(24), e2216522120. https://doi.org/10.1073/pnas.2216522120
  4. Campione, S. A., Kelliher, C. M., Orlando, D. A., Tran, T. Q., Haase, S. B. (2023, June 9). Alignment of synchronized time-series data for cross-experiment comparisons: Protocol. JoVE (Journal of Visualized Experiments). https://www.jove.com/t/65466/alignment-synchronized-time-series-data-using-characterizing-loss
  5. Hasnain, A., Balakrishnan, S., Joshy, D. M., Smith, J., Haase, S. B.,  Yeung, E. (2023, May 31). Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics. Nature News. https://www.nature.com/articles/s41467-023-37897-9
  6. Fox, J., Cummins, B., Moseley, R. C., Gameiro, M., Haase, S. B. (2023, February 25). A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets. arXiv.org. https://arxiv.org/abs/2302.12946
  7. A. Leins, D., B., Haase, S., Mohammed Eslami, Joshua Schrier,  T. Freeman, J. (2022, November 29). Collaborative methods to enhance reproducibility and accelerate discovery. Digital Discovery. https://pubs.rsc.org/en/content/articlehtml/2023/dd/d2dd00061j
  8. Cummins, B., Vrana, J., Moseley, R. C., Eramian, H., Deckard, A., Fontanarrosa, P., Bryce, D., Weston, M., Zheng, G., Nowak, J., Motta, F. C., Eslami, M., Johnson, K. L., Goldman, R. P., Myers, C. J., Johnson, T., Vaughn, M. W., Gaffney, N., Urrutia, J., … Haase, S. B. (2023, March 28). Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop. OUP Academic. https://academic.oup.com/synbio/article/8/1/ysad005/7091610
  9. Motta, F. C., Moseley, R. C., Cummins, B., Deckard, A., & Haase, S. B. (2022). Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes. BMC Bioinformatics, 23(1), 1–20. https://doi.org/10.1186/s12859-022-04627-9
  10. Welling, C. M., Singleton, D. R., Haase, S. B., Browning, C. H., Stoner, B. R., Gunsch, C. K., & Grego, S. (2022). Predictive values of time-dense SARS-CoV-2 wastewater analysis in university campus buildings. Science of The Total Environment, 835, 155401. https://doi.org/10.1016/j.scitotenv.2022.155401
  11. Liu, A. B., Davidi, D., Landsberg, H. E., Francesconi, M., Platt, J. T., Nguyen, G. T., Yune, S., Deckard, A., Puglin, J., Haase, S. B., Hamer, D. H., & Springer, M. (2022). Association of COVID-19 Quarantine Duration and Postquarantine Transmission Risk in 4 University Cohorts. JAMA Network Open, 5(2), e220088. https://doi.org/10.1001/jamanetworkopen.2022.0088
  12. Bryce, D., Goldman, R. P., DeHaven, M., Beal, J., Bartley, B., Nguyen, T. T., Walczak, N., Weston, M., Zheng, G., Nowak, J., Lee, P., Stubbs, J., Gaffney, N., Vaughn, M. W., Myers, C. J., Moseley, R. C., Haase, S., Deckard, A., Cummins, B., & Leiby, N. (2022). Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis. ACS Synthetic Biology, 11(2), 608–622. https://doi.org/10.1021/acssynbio.1c00305
  13. Cummins, B., Vrana, J., Moseley, R. C., Eramian, H., Deckard, A., Fontanarrosa, P., Bryce, D., Weston, M., Zheng, G., Nowak, J., Motta, F. C., Eslami, M., Johnson, K. L., Goldman, R. P., Myers, C. J., Johnson, T., Vaughn, M. W., Gaffney, N., Urrutia, J., … Haase, S. B. (2022). Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop. BioRxiv, 2022.06.10.495560. https://doi.org/10.1101/2022.06.10.495560
  14. Cummins, B., Moseley, R. C., Deckard, A., Weston, M., Zheng, G., Bryce, D., Nowak, J., Gameiro, M., Gedeon, T., Mischaikow, K., Beal, J., Johnson, T., Vaughn, M., Gaffney, N. I., Gopaulakrishnan, S., Urrutia, J., Goldman, R. P., Bartley, B., Nguyen, T. T., … Haase, S. B. (2022). Computational Prediction of Synthetic Circuit Function Across Growth Conditions. BioRxiv, 2022.06.13.495701. https://doi.org/10.1101/2022.06.13.495701
  15. Goldman, R. P., Moseley, R., Roehner, N., Cummins, B., Vrana, J. D., Clowers, K. J., Bryce, D., Beal, J., DeHaven, M., Nowak, J., Higa, T., Biggers, V., Lee, P., Hunt, J. P., Haase, S. B., Weston, M., Zheng, G., Deckard, A., Gopaulakrishnan, S., … Colonna-Romano, J. (2022). Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments. BioRxiv, 2022.05.31.493627. https://doi.org/10.1101/2022.05.31.493627
  16. Hasnain, A., Balakrishnan, S., Joshy, D. M., Haase, S. B., Smith, J., & Yeung, E. (2022). Learning transcriptome dynamics for discovery of optimal genetic reporters of novel compounds. bioRxiv, (p. 2022.05.27.493781). https://doi.org/10.1101/2022.05.27.493781
  17. Cummins, B., Motta, F. C., Moseley, R. C., Deckard, A., Campione, S., Gedeon, T., Mischaikow, K., & Haase, S. B. (2022). Experimental Guidance for Discovering Genetic Networks through Iterative Hypothesis Reduction on Time Series. bioRxiv, (p. 2022.04.28.489981). https://doi.org/10.1101/2022.04.28.489981
  18. Zaitzeff, A., Leiby, N., Motta, F. C., Haase, S. B., & Singer, J. M. (2022). Improved datasets and evaluation methods for the automatic prediction of DNA-binding proteins. Bioinformatics, 38(1), 44–51. https://doi.org/10.1093/bioinformatics/btab603
  19. Moseley, R. C., Campione, S., Cummins, B., Motta, F., & Haase, S. B. (2021). Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline. Journal of Visualized Experiments, 178, 63084. https://doi.org/10.3791/63084
  20. Saelens, J. W., Petersen, J. E. V., Freedman, E., Moseley, R. C., Konaté, D., Diakité, S. A. S., Traoré, K., Vance, N., Fairhurst, R. M., Diakité, M., Haase, S. B., & Taylor, S. M. (2021). Impact of Sickle Cell Trait Hemoglobin on the Intraerythrocytic Transcriptional Program of Plasmodium falciparum. MSphere, 6(5), e00755-21. https://doi.org/10.1128/mSphere.00755-21
  21. Motta, F. C., McGoff, K. A., Deckard, A., Wolfe, C. R., Bonsignori, M., Moody, M. A., Cavanaugh, K., Denny, T. N., Harer, J., & Haase, S. B. (2021). Assessment of Simulated Surveillance Testing and Quarantine in a SARS-CoV-2–Vaccinated Population of Students on a University Campus. JAMA Health Forum, 2(10), e213035. https://doi.org/10.1001/jamahealthforum.2021.3035
  22. Moseley, R. C., Motta, F., Tuskan, G. A., Haase, S. B., & Yang, X. (2021). Inference of Gene Regulatory Network Uncovers the Linkage between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi. Cells, 10(9), 2217. https://doi.org/10.3390/cells10092217
  23. Motta, F. C., McGoff, K. A., Deckard, A., Wolfe, C. R., Moody, M. A., Cavanaugh, K., Denny, T. N., Harer, J., & Haase, S. B. (2021). Benefits of Surveillance Testing and Quarantine in a SARS-CoV-2 Vaccinated Population of Students on a University Campus (p. 2021.06.15.21258928). medRxiv. https://doi.org/10.1101/2021.06.15.21258928
  24. Liu, A. B., Davidi, D., Landsberg, H. E., Francesconi, M., Platt, J. T., Nguyen, G. T., Yune, S., Deckard, A., Puglin, J., Haase, S. B., Hamer, D. H., & Springer, M. (2021). Seven-day COVID-19 quarantine may be too short: Assessing post-quarantine transmission risk in four university cohorts (p. 2021.05.12.21257117). medRxiv. https://doi.org/10.1101/2021.05.12.21257117
  25. Denny, T. N., Andrews, L., Bonsignori, M., Cavanaugh, K., Datto, M. B., Deckard, A., DeMarco, C. T., DeNaeyer, N., Epling, C. A., Gurley, T., Haase, S. B., Hallberg, C., Harer, J., Kneifel, C. L., Lee, M. J., Louzao, R., Moody, M. A., Moore, Z., Polage, C. R., … Wolfe, C. R. (2020). Implementation of a Pooled Surveillance Testing Program for Asymptomatic SARS-CoV-2 Infections on a College Campus—Duke University, Durham, North Carolina, August 2–October 11, 2020. Morbidity and Mortality Weekly Report, 69(46), 1743–1747. https://doi.org/10.15585/mmwr.mm6946e1
  26. Smith, L.M., Motta, F.C., Chopra, G., Moch, J.K., Nerem, R.R., Cummins, B., Roche, K.E., Kelliher, C.M., Leman, A.R., Harer, J., Gedeon, T., Waters, N.C., and Haase, S.B. (2020). An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum. Science 368, 754-759.
  27. Berry, E., Cummins, B., Nerem, R.R., Smith, L.M., Haase, S.B., and Gedeon, T. (2020). Using extremal events to characterize noisy time series. J Math Biol 80, 1523-1557.
  28. Motta, F., Tralie, C., Bedini, R., Bini, F., Bini, G., Eramian, H., Gameiro, M., Haase, S., Haddox, H., and Harer, J. (2019). Hyperparameter Optimization of Topological Features for Machine Learning Applications. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). (IEEE), pp. 1107-1114.
  29. Moseley, R.C., Motta, F., Tuskan, G.A., Haase, S., and Yang, X. (2019). Inference of Gene Regulatory Network Uncovers the Linkage Between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi. bioRxiv, 745893.
  30. Freeman, J., Leins, D., Bell IV, C., and Consortium, S.R. (2019). Selecting and assessing challenge problems. Theoretical Issues in Ergonomics Science 20, 27-38.
  31. Cho, C.Y., Kelliher, C.M., and Haase, S.B. (2019). The cell-cycle transcriptional network generates and transmits a pulse of transcription once each cell cycle. Cell Cycle 18, 363-378.
  32. Moseley, R.C., Mewalal, R., Motta, F., Tuskan, G.A., Haase, S., and Yang, X. (2018). Conservation and diversification of circadian rhythmicity between a model crassulacean acid metabolism plant Kalanchoë fedtschenkoi and a model C3 photosynthesis plant Arabidopsis thaliana. Frontiers in plant science 9, 1757.
  33. Kelliher, C.M., Foster, M.W., Motta, F.C., Deckard, A., Soderblom, E.J., Moseley, M.A., and Haase, S.B. (2018). Layers of regulation of cell-cycle gene expression in the budding yeast Saccharomyces cerevisiae. Mol Biol Cell 29, 2644-2655.
  34. Kelliher, C.M., and Haase, S.B. (2017). Connecting virulence pathways to cell-cycle progression in the fungal pathogen Cryptococcus neoformans. Curr Genet.
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  36. McGoff, K.A., Guo, X., Deckard, A., Kelliher, C.M., Leman, A.R., Francey, L.J., Hogenesch, J.B., Haase, S.B., and Harer, J.L. (2016). The Local Edge Machine: inference of dynamic models of gene regulation. Genome Biol 17, 214.
  37. Kelliher, C.M., Leman, A.R., Sierra, C.S., and Haase, S.B. (2016). Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans. PLoS Genet 12, e1006453.
  38. Perea, J.A., Deckard, A., Haase, S.B., and Harer, J. (2015). SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data. BMC bioinformatics 16, 257.
  39. Leman, A.R., Bristow, S.L., and Haase, S.B. (2014). Analyzing transcription dynamics during the budding yeast cell cycle. Methods in molecular biology 1170, 295-312.
  40. Haase, S.B., and Wittenberg, C. (2014). Topology and control of the cell-cycle-regulated transcriptional circuitry. Genetics 196, 65-90.
  41. Bristow, S.L., Leman, A.R., Simmons Kovacs, L.A., Deckard, A., Harer, J., and Haase, S.B. (2014). Checkpoints couple transcription network oscillator dynamics to cell-cycle progression. Genome Biol 15, 446.
  42. Bristow, S.L., Leman, A.R., and Haase, S.B. (2014). Cell cycle-regulated transcription: effectively using a genomics toolbox. Methods in molecular biology 1170, 3-27.
  43. Guo, X., Bernard, A., Orlando, D.A., Haase, S.B., and Hartemink, A.J. (2013). Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program. Proc Natl Acad Sci U S A.
  44. Deckard, A., Anafi, R.C., Hogenesch, J.B., Haase, S.B., and Harer, J. (2013). Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data. Bioinformatics 29, 3174-3180.
  45. Simmons Kovacs, L.A., Mayhew, M.B., Orlando, D.A., Jin, Y., Li, Q., Huang, C., Reed, S.I., Mukherjee, S., and Haase, S.B. (2012). Cyclin-dependent kinases are regulators and effectors of oscillations driven by a transcription factor network. Mol Cell 45, 669-679.
  46. Mayhew, M.B., Guo, X., Haase, S.B., and Hartemink, A.J. (2012). Close Encounters of the Collaborative Kind. Computer 45, 24-30.
  47. Ho, H.J., Lin, T.I., Chang, H.H., Haase, S.B., Huang, S., and Pyne, S. (2012). Parametric modeling of cellular state transitions as measured with flow cytometry. BMC bioinformatics 13 Suppl 5, S5.
  48. Chee, M.K., and Haase, S.B. (2012). New and Redesigned pRS Plasmid Shuttle Vectors for Genetic Manipulation of Saccharomyces cerevisiae. G3: Genes|Genomes|Genetics 2, 515-526.
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