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1
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73149105740
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Listening to the noise: Random fluctuations reveal gene network parameters
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B. Munsky, B. Trinh and M. Khammash, "Listening to the noise: random fluctuations reveal gene network parameters", Molecular Systems Biology, vol. 5, no. 1, pp.318, 2009.
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Molecular Systems Biology
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Munsky, B.1
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Systematic identification of signal-activated stochastic gene regulation
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G. Neuert, B. Munsky, R. Tan, L. Teytelman, M. Khammash and A. van Oudenaarden, "Systematic identification of signal-activated stochastic gene regulation", Science, vol. 339, no. 6119, pp.584-587, 2013.
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Science
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Neuert, G.1
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Van Oudenaarden, A.6
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3
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84883481038
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The signal within the noise: Efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations
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doi: 10.1093/bioinformatics/btt380
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G. Lillacci and M. Khammash, "The signal within the noise: efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations", Bioinformatics, doi: 10.1093/bioinformatics/ btt380, 2013.
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(2013)
Bioinformatics
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Lillacci, G.1
Khammash, M.2
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4
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84861443923
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Moment-based inference predicts bimodality in transient gene expression
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C. Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter, J. Lygeros and H. Koeppl, "Moment-based inference predicts bimodality in transient gene expression", Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 21 pp.8340-8345, 2012.
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Zechner, C.1
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Lygeros, J.6
Koeppl, H.7
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5
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LNCS Series, vol. N.5469, Springer, Berlin/Heidelberg, Germany
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E. Cinquemani, A. Milias-Argeitis, S. Summers and J. Lygeros, "Local Identification of Piecewise Deterministic Models of Genetic Networks", LNCS Series, vol. N.5469, Springer, Berlin/Heidelberg, Germany, pp. 105119, 2009.
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Local Identification of Piecewise Deterministic Models of Genetic Networks
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Cinquemani, E.1
Milias-Argeitis, A.2
Summers, S.3
Lygeros, J.4
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6
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Accounting for extrinsic variability in the estimation of stochastic rate constants
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H. Koeppl, C. Zechner, A. Ganguly, S. Pelet and M. Peter, "Accounting for extrinsic variability in the estimation of stochastic rate constants", Int. J. Robust. Nonlinear Control, vol. 22, pp.1103-1119, 2011.
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84893293657
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Identification of biological models from singlecell data: A comparison between mixed-effects and moment-based inference
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Zurich, Switzerland
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A. Gonzalez, J. Uhlendorf, J. Schaul, E. Cinquemani, G. Batt and G. Ferrari-Trecate, "Identification of biological models from singlecell data: A comparison between mixed-effects and moment-based inference", Proceedings of the 12th European Control Conference, Zurich, Switzerland, 2013.
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Proceedings of the 12th European Control Conference
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Gonzalez, A.1
Uhlendorf, J.2
Schaul, J.3
Cinquemani, E.4
Batt, G.5
Ferrari-Trecate, G.6
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8
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84896591356
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Designing experiments to understand the variability in biochemical reaction networks
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J. Ruess, A. Milias-Argeitis and J. Lygeros, "Designing experiments to understand the variability in biochemical reaction networks", Journal of the Royal Society Interface, vol. 10, no. 88, pp.20130588, 2013.
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, Journal of the Royal Society Interface
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Ruess, J.1
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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models
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M. Komorowski, M. Costa, D. Rand and M. Stumpf, "Sensitivity, robustness, and identifiability in stochastic chemical kinetics models", Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 21, pp.8645-8650, 2011.
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Proceedings of the National Academy of Sciences of the United States of America
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Komorowski, M.1
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Stumpf, M.4
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10
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33751347484
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The finite state projection algorithm for the solution of the chemical master equation
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B. Munsky and M. Khammash, "The finite state projection algorithm for the solution of the chemical master equation", The Journal of Chemical Physics, vol. 124, pp.044104, 2006.
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(2006)
The Journal of Chemical Physics
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Munsky, B.1
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11
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64549159754
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A moment closure method for stochastic reaction networks"
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C. Lee, K. Kim and P. Kim, "A moment closure method for stochastic reaction networks", The Journal of Chemical Physics, vol. 130, pp.134107, 2009.
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The Journal of Chemical Physics
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Lee, C.1
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12
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Approximate moment dynamics for chemically reacting systems
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A. Singh and J. Hespanha, "Approximate moment dynamics for chemically reacting systems", IEEE Transactions on Automatic Control, vol. 56, no. 2, pp.414-418, 2011.
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IEEE Transactions on Automatic Control
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Singh, A.1
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Moment estimation for chemically reacting systems by extended kalman filtering
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J. Ruess, A. Milias-Argeitis, S. Summers and J. Lygeros, "Moment estimation for chemically reacting systems by extended Kalman filtering", The Journal of Chemical Physics, vol. 135, pp.165102, 2011.
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The Journal of Chemical Physics
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Ruess, J.1
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14
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Model-based design of experiments for parameter precision: State of the art
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G. Franceschini and S. Macchietto, "Model-based design of experiments for parameter precision: State of the art", Chemical Engineering Science, vol. 63, no. 19, pp.4846-4872, 2008.
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Chemical Engineering Science
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Franceschini, G.1
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Linking stochastic dynamics to population distribution: An analytical framework of gene expression"
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N. Friedman, L. Cai and S. Xie, "Linking stochastic dynamics to population distribution: An analytical framework of gene expression", Physical Review Letters, vol. 97, no. 16, pp.168302, 2006.
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Physical Review Letters
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Friedman, N.1
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Transient analysis of stochastic switches and trajectories with applications to gene regulatory networks
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B. Munsky and M. Khammash, "Transient analysis of stochastic switches and trajectories with applications to gene regulatory networks", IET Systems Biology, vol. 2, no. 5, pp.323-333, 2008.
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(2008)
IET Systems Biology
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Munsky, B.1
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17
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78149453584
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Identification from stochastic cellto- cell variation: A genetic switch case study"
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B. Munsky and M. Khammash, "Identification from stochastic cellto- cell variation: A genetic switch case study", IET Systems Biology, vol. 4, no. 6, pp.356-366, 2010.
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(2010)
IET Systems Biology
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Munsky, B.1
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