• HD Vo, LS Forero-Quintero, LU Aguilera, B Munsky, “Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise,” Frontiers in Cell and Developmental Biology, 11, 1133994, 2023, https://doi.org/10.3389/fcell.2023.1133994
  • L. Forero, W. Raymond, T. Handa, M. Saxton, T. Morisaki, H. Kimura, E. Bertrand, B. Munsky*, T. Stasevich*, “Live-cell imaging reveals the spatiotemporal organization of endogenous RNA polymerase II phosphorylation at a single gene”, Nature Communications, https://doi.org/10.1101/2020.04.03.024414
  • A L Koch, L Aguilera, T Morisaki, B E Munsky*, T J Stasevich*, “Quantifying the spatiotemporal dynamics of IRES versus Cap translation with single-molecule resolution in living cells,” Nature Structural and Molecular Biology, 27:12, 2020. https://doi.org/10.1038/s41594-020-0504-7
  • K.R. Lyon Jr., L.U. Aguilera, T. Morisaki, B. Munsky*, T.J. Stasevich*, Live-cell single RNA imaging reveals bursts of translational frameshifting, Molecular Cell, 178:2, 2019, https://doi.org/10.1016/j.molcel.2019.05.002
  • L. Aguilera, W. Raymond, Z. R. Fox, M. P. May, E. Djokic, T. Morisaki, T. J. Stasevich, B. Munsky, Computational design and interpretation of live-cell, single-RNA translation experiments, PLoS Computational Biology, 15:10, e1007425, 2019, https://doi.org/10.1371/journal.pcbi.1007425
  • J. Thompson, R. Johansen, J. Dunbar, B. Munsky, Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition, PLoS One, 14:7, e0215502, 2019, https://doi.org/10.1371/journal.pone.0215502
  • Z. Fox, B. Munsky, The finite state projection based Fisher information matrix approach to estimate and maximize the information in single-cell experiments, PLoS Computational Biology, 15:1, e1006365, 2019, online here
  • G Neuert, B Munsky, R-Z Tan, L Teytelman, M Khammash, A van Oudenaarden, Systematic Identification of Signal-Activated Stochastic Gene Regulation, Science, 339:6119, 584-587 (2013). reprint (.pdf)
  • Munsky, B., Khammash, M., The Finite State Projection Algorithm for the Solution of the Chemical Master Equation, Journal of Chemical Physics, 124 :044104 (2006). reprint (.pdf)
  • William S Raymond, Sadaf Ghaffari, Luis U Aguilera, Eric Ron, Tatsuya Morisaki, Zachary R Fox, Michael P May, Timothy J Stasevich, Brian Munsky, “Using mechanistic models and machine learning to design single-color multiplexed nascent chain tracking experiments,” Frontiers in Cell and Developmental Biology, 11, 1151318, 2023, https://doi.org/10.3389/fcell.2023.1151318
  • Huy D Vo, Linda S Forero-Quintero, Luis U Aguilera, Brian Munsky, “Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise,” Frontiers in Cell and Developmental Biology, 11, 1133994, 2023, https://doi.org/10.3389/fcell.2023.1133994
  • Michael P May, Brian Munsky, “Exploiting Intrinsic Noise for Heterogeneous Cell Control Under Time Delays and Model Uncertainties”, BioRxiv, 2023, https://doi.org/10.1101/2023.10.07.561335
  • Michael P May, Brian Munsky, “Optogenetic Maxwell Demon to Exploit Intrinsic Noise and Control Cell Differentiation Despite Time Delays and Extrinsic Variability”, BioRxiv, 2022, https://doi.org/10.1101/2022.07.05.498841
  • Michael P May, Brian Munsky, “Biochemical noise enables a single optogenetic input to control identical cells to track asymmetric and asynchronous reference signals”, BioRxiv, 2022, https://doi.org/10.1101/2022.07.05.498842
  • Tayte P Campbell, Danielle EM Ulrich, Jason Toyoda, Jaron Thompson, Brian Munsky, Michaeline BN Albright, Vanessa L Bailey, Malak M Tfaily, John Dunbar, “Microbial communities influence soil dissolved organic carbon concentration by altering metabolite composition,” Frontiers in Microbiology, 12, 799014, 2022, https://doi.org/10.3389/fmicb.2021.799014
  • Cynthia Shaheen, Cameron Hastie, Kimberly Metera, Shane Scott, Zhi Zhang, Sitong Chen, Gracia Gu, Lisa Weber, Brian Munsky, Fedor Kouzine, David Levens, Craig Benham, Sabrina Leslie, “Non-equilibrium structural dynamics of supercoiled DNA plasmids exhibits asymmetrical relaxation,” Nucleic Acids Research, 50:5, 2754-2764, 2022, https://doi.org/10.1093/nar/gkac101
  • Linda S Forero-Quintero, William Raymond, Brian Munsky, Timothy J Stasevich, “Visualization, Quantification, and Modeling of Endogenous RNA Polymerase II Phosphorylation at a Single-copy Gene in Living Cells,” Bio-Protocol, 12:12, e4482, 2022, https://bio-protocol.org/pdf/Bio-protocol4482.pdf
  • D Kalb+, H D. Vo+ , S Adikari3, E Hong-Geller3, B Munsky*, J  Werner*, “Visualization and Modeling of Inhibition of IL-1β and TNFα mRNA Transcription at the Single-Cell Level,” bioRxiv, 2020, https://doi.org/10.1101/2020.10.16.342576 (+contributed equally),
  • L. Forero, W. Raymond, T. Handa, M. Saxton, T. Morisaki, H. Kimura, E. Bertrand, B. Munsky*, T. Stasevich*, “Live-cell imaging reveals the spatiotemporal organization of endogenous RNA polymerase II phosphorylation at a single gene”, Nature Communications, in press, https://doi.org/10.1101/2020.04.03.024414
  • Marie E Kroeger, M Rae DeVan, Jaron Thompson, Renee Johansen, La Verne Gallegos‐Graves, Deanna Lopez, Andreas Runde, Thomas Yoshida, Brian Munsky, Sanna Sevanto, Michaeline BN Albright, John Dunbar, “Microbial community composition controls carbon flux across litter types in early phase of litter decomposition,” Environmental Microbiology, 23:11, 6676-6693, 2021, https://doi.org/10.1111/1462-2920.15705
  • Michael P May, Brian Munsky, “Exploiting noise, non-linearity, and feedback for differential control of multiple synthetic cells with a single optogenetic input,” ACS Synthetic Biology, 10:12, 3396-3410, 2021, https://doi.org/10.1021/acssynbio.1c00341
  • M B. N. Albright, R Johansen, J Thompson, D Lopez, LV Gallegos-Graves, M Kroeger, A Runde, R C Mueller, A Washburne, B Munsky, T Yoshida, J Dunbar, “Soil bacterial and fungal richness forecast patterns of early pine litter decomposition,” Frontiers in Microbiology, 11, 2020, https://doi.org/10.3389/fmicb.2020.542220
  • J. Thompson, R. Johansen, J. Dunbar, B. Munsky, Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition, PLoS One, 14:7, e0215502, 2019, https://doi.org/10.1371/journal.pone.0215502
  • A L Koch, L Aguilera, T Morisaki, B E Munsky*, T J Stasevich*, “Quantifying the spatiotemporal dynamics of IRES versus Cap translation with single-molecule resolution in living cells,” Nature Structural and Molecular Biology, 27:12, 2020. https://doi.org/10.1038/s41594-020-0504-7
  • T.A. Catanach, H.D. Vo, B. Munsky, “Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo,” International Journal for Uncertainty Quantification, 10:6, 2020, https://doi.org/10.1615/int.j.uncertaintyquantification.2020033241, arXiv: https://arxiv.org/abs/2001.01373
  • Z. Fox, G. Neuert, B. Munsky, “Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments,” Complexity, 2020:8536365, 2020, https://doi.org/10.1155/2020/8536365
  • H. Jashnsaz, Z. Fox, J. Hughes, G. Li, B. Munsky*, G. Neuert*, “Diverse cell stimulation kinetics identify predictive signal transduction models,” iScience, 23:10, 2020, 10.1016/j.isci.2020.101565
  • M B N Albright, J Thompson, M E Kroeger, R Johansen, D E M Ulrich, LV  Gallegos-Graves, B Munsky, J Dunbar, “Differences in substrate use linked to divergent carbon flow during litter decomposition,” FEMS Microbiology Ecology, 96:8, 2020, doi: https://doi.org/10.1093/femsec/fiaa135
  • H Vo, B Munsky, “A Parallel Implementation of the Finite State Projection Algorithm for the Solution of the Chemical Master Equation,” bioRxiv, 2020, https://doi.org/10.1101/2020.06.30.180273
  • L. Aguilera, W. Raymond, Z. R. Fox, M. P. May, E. Djokic, T. Morisaki, T. J. Stasevich, B. Munsky, Computational design and interpretation of live-cell, single-RNA translation experiments, PLoS Computational Biology, 15:10, e1007425, 2019, https://doi.org/10.1371/journal.pcbi.1007425
  • J. Thompson, R. Johansen, J. Dunbar, B. Munsky, Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition, PLoS One, 14:7, e0215502, 2019, online here
  • Z. Fox, B. Munsky, The finite state projection based Fisher information matrix approach to estimate and maximize the information in single-cell experiments, PLoS Computational Biology, 15:1, e1006365, 2019, online here
  • H.D. Vo, Z.R. Fox, A. Baetica, B. Munsky, Bayesian estimation for stochastic gene expression using multifidelity models, J Physical Chemistry B, 123:10, 2217-2234, 2019, online here
  • M. Tanhaemami, E. Alizadeh, C. Sanders, B. Marrone, B. Munsky, Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation, Physical Biology, 16, 055001, 2019, online here
  • K.R. Lyon Jr., L.U. Aguilera, T. Morisaki, B. Munsky, T.J. Stasevich, Live-cell single RNA imaging reveals bursts of translational frameshifting, Molecular Cell, 178:2, 458-472.e19, 2019, online here
  • B. Munsky, G. Li, Z. Fox, D. P Shepherd, G. Neuert, Distribution shapes govern the discovery of predictive models for gene regulation, Proceedings of the National Academy of Sciences USA, 115:29, 7533-7538 (2018), online here
  • L Weber, W Raymond, B Munsky, Identification of Gene Regulation Models from Single-Cell Data, Physical Biology, 15:5 (2018). online here
  • Z Fox, B Munsky, Stochasticity or Noise in Biochemical Reactions, To appear in Quantitative Biology: Theory, Computational Methods and Examples of Models, edited by B Munsky, L S Tsimring, and W S Hlavacek, MIT Press, ch. 5 (2018). get exam copy or purchase here , get arXiv version
  • L Weber, W Raymond, B Munsky, Tutorial on the Identification of Gene Regulation Models from Single-Cell Data, To appear in Quantitative Biology: Theory, Computational Methods and Examples of Models, edited by B Munsky, L S Tsimring, and W S Hlavacek, MIT Press, ch. 30 (2018). get exam copy or purchase here
  • Quantitative Biology: Theory, Computational Methods and Examples of Models, edited by B Munsky, L S Tsimring, and W S Hlavacek, MIT Press, (2018). get exam copy or purchase here
  • R. Johnson and B. Munsky, The finite state projection approach to analyze dynamics of heterogeneous populations, Physical Biology, 14:3, 035002 (2017), online here
  • L.F. Hartje, B. Munsky, T.W. Ni, CJ Ackerson, C.D. Snow, Adsorption-Coupled Diffusion of Gold Nanoclusters Within a Large-Pore Protein Crystal Scaffold, J Physical Chemistry B, 121:32, 7652-7659 (2017), PubMed Entry

2016

  • Z. Fox, G. Neuert and B. Munsky, Finite state projection based bounds to compare chemical master equation models using single-cell data, Journal of Chemical Physics, 145:7, 074101 (2016), online here

2015

  • P. Szymanska, N. Gritti, J. M. Keegstra, M. Soltani and B. Munsky, Using noise to control heterogeneity of isogenic populations in homogenous environments, Physical Biology, 12:4, 045003 (2015), online here
  • B. Munsky, Z. Fox, G. Neuert, Integrating Single-Molecule Experiments and Discrete Stochastic Models to Understand Heterogeneous Gene Transcription, Methods, 85, 12-21, (2015), online here
  • B. Munsky, G. Neuert, From analog to digital models of gene regulation, Physical Biology, 12:4, 045004, (2015), online here

2014

  • A Senecal, B Munsky, F Proux, N Ly, FE Braye, C Zimmer, F Mueller, X Darzacq, Transcription Factors Modulate c-Fos Transcriptional Bursts, Cell Reports, 8:1, 75-83 (2014), online here
  • O Resnekov, B Munsky, WS Hlavacek, Perspective on the q-bio Summer School and Conference: 2007-2014 and beyond, Quantitative Biology, 2:1, 54-58, online here

2013

  • D Shepherd, N Li, S N Micheva-Viteva, B Munsky, E Hong-Geller, J H Werner, Counting Small RNA in Pathogenic Bacteria, Analytical Chemistry, 85:10, 4938-4943 (2013) (Cover Article), reprint (.pdf)
  • G Neuert, B Munsky, R-Z Tan, L Teytelman, M Khammash, A van Oudenaarden, Systematic Identification of Signal-Activated Stochastic Gene Regulation, Science, 339:6119, 584-587 (2013). reprint (.pdf)
  • Nemenman, I., Gnanakaran, S.,Munsky, B., Wall, M.E., Jiang, Y., Hlavacek, W.S., Faeder. J. The Fifth Annual q-bio Conference on Cellular Information Processing, Physical Biology, 9:050201, 2012. reprint (.pdf)

2012

  • Munsky, B., Neuert, G., van Oudenaarden, A., Using Gene Expression Noise to Understand Gene Regulation, Science, 336:6078, 183-187, (2012). reprint (.pdf)
  • Lou, C., Stanton, B., Chen, Y-C., Munsky, B., Voigt, C. A., Ribozyme-based “insulator parts” buffer synthetic circuits from genetic context, Nature Biotechnology, 30:11, 1137-1142 (2012). reprint (.pdf)
  • Munsky, B., Modeling Cellular Variability, in Quantitative Biology From Molecular to Cellular Systems, pp. 234-266, M. Wall, Ed. (Taylor & Francis Group, New York, 2012). preprint (.pdf)
  • DP Shepherd, N Li, E Hong-Geller, B Munsky, JH Werner, New tools for discovering the role sRNA plays in cellular regulation, Proc. SPIE, 8228:822808, (2012). preprint (.pdf)
  • Tapia, J., Faeder, J., Munsky, B., Adaptive Coarse-Graining for Transient and Quasi-Equilibrium Analyses of Stochastic Gene Regulation, Proc. of the 51st IEEE Conference on Decision and Control, 5361-5366, Maui, HI, Dec. 2012. reprint (.pdf)
  • Nemenman, I., Gnanakaran, S., Hlavacek, W., Jiang, Y., Munsky, B., Wall, M., Faeder. J. The Fifth Annual q-bio Conference on Cellular Information Processing, Physical Biology, 9:050201, 2012. reprint (.pdf)

2010

  • Bel, G., Munsky, B., Nemenman, I., Simplicity of Completion Time Distributions for Common Complex Biochemical Processes, Physical Biology, 7:016003 (2010). reprint (.pdf)
  • Munsky, B., Khammash, M., Identification from stochastic cell-to-cell variation: A genetic switch case study, IET Systems Biology, 4:6, 356-366 (2010). reprint (.pdf)
  • Khammash, M. Munsky, B., Stochastic Gene Expression: Modeling, Analysis, and Identification, in The Control Handbook, B. Levine, Ed. (Taylor & Francis Group, New York, NY, Second Edition, 2010). preprint (.pdf)

2009

  • Munsky, B., Trinh, B., Khammash, M., Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters, Molecular Systems Biology, 5:318 (2009). reprint (.pdf) [Journal Site]
  • Munsky, B., Nemenman, I. Bel, G., Specificity and Completion Time Distributions of Biochemical Processes, Journal of Chemical Physics, 131, No. 235104 (2009). reprint (.pdf)

2008

  • Munsky, B., Khammash, M., The FSP Approach for the Analysis of Stochastic Noise in Gene Networks, IEEE Transactions on Automatic Control, 53, 201-214 (2008). reprint (.pdf)
  • Munsky, B., Khammash, M., Transient Analysis of Stochastic Switches and Trajectories with Applications to Gene Regulatory Networks, IET Systems Biology, 2, No. 2, 323-333 (2008).[.pdf] [IEEE Site]
  • Munsky, B., Khammash, M., Using Noise Transmission Properties to Identify Stochastic Gene Regulatory Networks, Proc. 46th IEEE Conference on Decision and Control, Cancun, Mexico (2008).[.pdf]
  • Munsky, B., Khammash, M., Computation of Switch Time Distributions in Stochastic Gene Regulatory Networks, Proc. 2008 American Control Conference, THB10.2, Seattle, WA.[.pdf]

2007

  • Inglesias, P., Khammash, M., Munsky, B., Sontag, E., Del Vecchio, D., Systems Biology and Control – A Tutorial, Proc. 45th IEEE Conference on Decision and Control, New Orleans, LA (2007).[.pdf]
  • Munsky, B., Khammash, M., A Multiple Time Interval Finite State Projection Algorithm for the Solution to the Chemical Master Equation, Journal of Computational Physics, 226, 818-835 (2007).[.pdf]
  • Munsky, B., Peles, S., Khammash, M., Stochastic analysis of gene regulatory networks using finite state projections and singular perturbation, Proc. 2007 American Control Conference, 1323-1328 (2007). [.pdf]
  • Munsky, B., Khammash, M., Analysis of Noise Induced Stochastic Fluctuations in Gene Regulatory Networks , J. SICE, 5, 405-411 (2007).[.pdf]
  • Peles, S., Munsky, B., Khammash, M., Reduction and Solution of the Chemical Master Equation Using Time Scale Separation and Finite State Projection, Journal of Chemical Physics, 125, 204104 (2007).[.pdf]

2006

  • Munsky, B., Khammash, M., The Finite State Projection Algorithm for the Solution of the Chemical Master Equation, Journal of Chemical Physics, 124 :044104 (2006). reprint (.pdf)
  • Munsky, B., Khammash, M., A Reduced Model Solution for the Chemical Master Equation Arising in Stochastic Analyses of Biological Networks, Proc. 45th IEEE Conference on Decision and Control, San Diego, CA (2006).[.pdf]
  • Munsky, B., Khammash, M., The Finite State Projection Algorithm for the Solution of the Chemical Master Equation, Journal of Chemical Physics, 124 , 044104 (2006).[.pdf]
  • Khammash, M., Munsky, B., Systems Theory Applications in Biology: From Stochastic Chemical Kinetics to Deterministic Model Invalidation, Invited paper-European Control Conference, Kos, Greece (2007).[.pdf]