Designing experiments to understand the variability in biochemical reaction networks

Journal of the Royal Society Interface

Volumn 10, Issue 88, 2013, Pages

  Ruess, Jakob ^a   Milias Argeitis, Andreas ^a   Lygeros, John ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Cell to cell variability; Continuous time Markov chains; Fisher information; Gene expression; Optimal experimental design

Indexed keywords

CELLS; CONTINUOUS TIME SYSTEMS; CYTOLOGY; FISHER INFORMATION MATRIX; GENE EXPRESSION; GENES; KINETIC PARAMETERS; MARKOV PROCESSES; PARAMETER ESTIMATION; STATISTICS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

BIOCHEMICAL REACTION NETWORK; CELL-TO-CELL VARIABILITY; CHEMICAL MASTER EQUATION; CONTINUOUS TIME MARKOV CHAIN; FISHER INFORMATION; GAUSSIAN APPROXIMATIONS; OPTIMAL EXPERIMENTAL DESIGNS; SINGLE CELL MEASUREMENT;

DESIGN OF EXPERIMENTS;

ARTICLE; CHEMICAL REACTION; GENE EXPRESSION SYSTEM; GENE INDUCTION; MATHEMATICAL COMPUTING; QUANTITATIVE ANALYSIS; STOCHASTIC MODEL; YEAST; BIOLOGICAL MODEL; COMPUTER SIMULATION; METHODOLOGY; STATISTICS;

COMPUTER SIMULATION; MODELS, BIOLOGICAL; RESEARCH DESIGN; STOCHASTIC PROCESSES;

EID: 84896591356     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2013.0588     Document Type: Article

References (44)

1. Munsky B, Trinh B, Khammash M. 2009 Listening to the noise: random fluctuations reveal gene network parameters. Mol. Syst. Biol. 5, 318. (doi:10.1038/msb.2009.75)
2. Neuert G, Munsky B, Tan R.Z., Teytelman L., Khammash M, van Oudenaarden A. 2013 System identification of signal-activated stochastic gene regulation. Science 339, 584-587. (doi:10.1126/science.1231456)
3. Zechner C, Ruess J, Krenn P., Pelet S, Peter M, Lygeros J., Koeppl H. 2012 Moment-based inference predicts bimodality in transient gene expression. Proc. Natl Acad. Sci. USA 109, 8340-8345. (doi:10.1073/pnas.1200161109)
4. Singh A, Razooky B, Dar R., Weinberger L. 2012 Dynamics of protein noise can distinguish between alternate sources of gene-expression variability. Mol. Syst. Biol. 8, 607. (doi:10.1038/msb.2012.38)
5. Komorowski M, Costa M, Rand D., Stumpf M. 2011 Sensitivity, robustness, and identifiability in stochastic chemical kinetics models. Proc. Natl Acad. Sci. USA 108, 8645-8650. (doi:10.1073/pnas.1015814108)
6. Gillespie D. 1992 A rigorous derivation of the chemical master equation. Physica A 188, 404-425. (doi:10.1016/0378-4371(92)90283-V)
7. Munsky B, Khammash M. 2006 The finite state projection algorithm for the solution of the chemical master equation. J. Chem. Phys. 124, 044104. (doi:10.1063/1.2145882)
8. van Kampen N. 2006 Stochastic processes in physics and chemistry. Amsterdam, The Netherlands: Elsevier Science.
9. Gillespie D. 2000 The chemical Langevin equation. J. Chem. Phys. 113, 297-306. (doi:10.1063/1.481811)
10. Snijder B, Pelkmans L. 2011 Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol. 12, 119-125. (doi:10.1038/nrm3044)
11. Shahrezaei V, Ollivier J, Swain P. 2008 Colored extrinsic fluctuations and stochastic gene expression. Mol. Syst. Biol. 4, 196. (doi:10.1038/msb.2008. 31)
12. Hilfinger A, Paulsson J. 2011 Separating intrinsic from extrinsic fluctuations in dynamic biological systems. Proc. Natl Acad. Sci. USA 108, 12167-12172. (doi:10.1073/pnas.1018832108)
13. Koeppl H, Zechner C, Ganguly A., Pelet S, Peter M. 2011 Accounting for extrinsic variability in the estimation of stochastic rate constants. Int. J. Robust Nonlin. 22, 1103-1119. (doi:10.1002/rnc.2804)
14. Hilfinger A, Chen M, Paulsson J. 2012 Using temporal correlations and full distributions to separate intrinsic and extrinsic fluctuations in biological systems. Phys. Rev. Lett. 109, 248104. (doi:10.1103/PhysRevLett.109. 248104)
15. Elowitz M, Levine A, Siggia E., Swain P. 2002 Stochastic gene expression in a single cell. Science 297, 1183-1186. (doi:10.1126/science.1070919)
16. Colman-Lerner A., Gordon A, Serra E., Chin T, Resnekov O, Endy D., Pesce G, Brent R. 2005 Regulated cell-to-cell variation in a cell-fate decision system. Nature 437, 699-706. (doi:10.1038/nature03998)
17. Volfson D, Marciniak J, Blake W., Ostroff N, Tsimring L, Hasty J. 2005 Origins of extrinsic variability in eukaryotic gene expression. Nature 439, 861-864. (doi:10.1038/nature04281)
18. Chabot J, Pedraza J, Luitel P., Van Oudenaarden A. 2007 Stochastic gene expression out-of-steady-state in the cyanobacterial circadian clock. Nature 450, 1249-1252. (doi:10.1038/nature06395)
19. Rosenfeld N, Young J, Alon U., Swain P, Elowitz M. 2005 Gene regulation at the single-cell level. Science 307, 1962-1965. (doi:10.1126/science.1106914)
20. Milias-Argeitis A., Summers S, Stewart-Ornstein J, Zuleta I., Pincus D, El-Samad H, Khammash M, Lygeros J. 2011 In silico feedback for in vivo regulation of a gene expression circuit. Nat. Biotechnol. 29, 1114-1116. (doi:10.1038/nbt.2018)
21. Hespanha J. 2006 Modeling and analysis of stochastic hybrid systems. IEE Proc. Control Theory Appl. 153, 520-535. (doi:10.1049/ip-cta:20050088)
22. Ruess J, Milias-Argeitis A, Summers S., Lygeros J. 2011 Moment estimation for chemically reacting systems by extended Kalman filtering. J. Chem. Phys. 135, 165102. (doi:10.1063/1.3654135)
23. Singh A, Hespanha J. 2011 Approximate moment dynamics for chemically reacting systems. IEEE Trans. Automat. Contr. 56, 414-418. (doi:10.1109/TAC. 2010.2088631)
24. Ale A, Kirk P, Stumpf M. 2013 A general moment expansion method for stochastic kinetic models. J. Chem. Phys. 138, 174101. (doi:10.1063/1.4802475)
25. Bandara S, Schlöder J, Eils R., Bock H, Meyer T. 2009 Optimal experimental design for parameter estimation of a cell signaling model. PLoS Comput. Biol. 5, e1000558. (doi:10.1371/journal.pcbi.1000558)
26. Busetto A, Ong C, Buhmann J. 2009 Optimized expected information gain for nonlinear dynamical systems. in Proc. 26th Annual Int. Conf. on Machine Learning, Montreal, Canada, 14-18 June, pp. 97-104. New York, NY: ACM.
27. Franceschini G, Macchietto S. 2008 Model-based design of experiments for parameter precision: state of the art. Chem. Eng. Sci. 63, 4846-4872. (doi:10.1016/j.ces.2007.11.034)
28. Liepe J, Filippi S, Komorowski M., Stumpf M. 2013 Maximizing the information content of experiments in systems biology. PLoS Comput. Biol. 9, e1002888. (doi:10.1371/journal.pcbi.1002888)
29. Zechner C, Nandy P, Unger M., Koeppl H. 2012 Optimal variational perturbations for the inference of stochastic reaction dynamics. in IEEE 51st Annual Conf. on Decision and Control (CDC), Maui, Hawaii, 10-13 December, pp. 5336-5341. New York, NY: IEEE.
30. Hagen D, White J, Tidor B. 2013 Convergence in parameters and predictions using computational experimental design. Interface Focus 3, 20130008. (doi:10.1098/rsfs.2013.0008)
31. Wlodarczyk M, Lipniacki T, Komorowski M. 2013 Functional redundancy in the NF-kB signalling pathway. (http://arxiv.org/abs/1303.3109).
32. Arkin A, Ross J, McAdams H. 1988 Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149, 1633-1648.
33. Lukacs E. 1942 A characterization of the normal distribution. Ann. Math. Stat. 13, 91-93. (doi:10.1214/aoms/1177731647)
34. Jarret R. 1984 Bounds and expansions for Fisher information when the moments are known. Biometrika 71, 101-113. (doi:10.1093/biomet/71.1.101)
35. Kreutz C, Timmer J. 2009 Systems biology: experimental design. FEBS J. 276, 923-942. (doi:10.1111/j.1742-4658.2008.06843.x)
36. Hunter W, Hill W, Henson T. 1969 Designing experiments for precise estimation of all or some of the constants in a mechanistic model. Can. J. Chem. Eng. 47, 76-80. (doi:10.1002/cjce.5450470114)
37. Walter E, Pronzato L. 1990 Qualitative and quantitative experiment design for phenomenological models-a survey. Automatica 26, 195-213. (doi:10.1016/0005-1098(90)90116-Y)
38. Pronzato L, Walter E. 1985 Robust experimental design via stochastic approximation. Math. Biosci. 75, 103-120. (doi:10.1016/0025-5564(85)90068-9)
39. Chaloner K, Larntz K. 1989 Optimal Bayesian design applied to logistic regression experiments. J. Stat. Plan Inference 21, 191-208. (doi:10.1016/0378-3758(89)90004-9)
40. Menolascina F, di Bernardo M, di Bernardo D. 2011 Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks. Automatica 47, 1265-1270. (doi:10.1016/j.automatica.2011. 01.073)
41. Uhlendorf J, Miermont A, Delaveau T., Charvin G, Fages F, Bottani S., Batt G, Hersen P. 2012 Longterm model predictive control of gene expression at the population and single-cell levels. Proc. Natl Acad. Sci. USA 109, 14271-14276. (doi:10.1073/pnas.1206810109)
42. Swain P, Elowitz M, Siggia E. 2002 Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795-12800. (doi:10.1073/pnas.162041399)
43. Bowsher C, Swain P. 2012 Identifying sources of variation and the flow of information in biochemical networks. Proc. Natl Acad. Sci. USA 109, E1320-E1328. (doi:10.1073/pnas.1119407109)
44. Friedman N, Cai L, Xie S. 2006 Linking stochastic dynamics to population distribution: an analytical framework of gene expression. Phys. Rev. Lett. 97, 168302. (doi:10.1103/PhysRevLett.97.168302)