A distribution-matching method for parameter estimation and model selection in computational biology

International Journal of Robust and Nonlinear Control

Volumn 22, Issue 10, 2012, Pages 1065-1081

  Lillacci, Gabriele ^a   Khammash, Mustafa ^a  

^a University of California   (United States)

Author keywords

distribution matching; model selection; Parameter estimation

Indexed keywords

BIOLOGICAL PROCESS; CALIBRATION PROCEDURE; CLASSICAL TECHNIQUES; COMPUTATIONAL BIOLOGY; COMPUTATIONAL MODELING; CUMULATIVE DISTRIBUTION FUNCTION; DISTRIBUTION MATCHING; ENZYMATIC REACTION; ESTIMATION PROBLEM; KINETIC RATE CONSTANTS; LEAST-SQUARES ESTIMATION; MODEL PARAMETERS; MODEL PREDICTION; MODEL SELECTION; PARAMETER VALUES; PARTICLE FILTER; REAL-TIME POLYMERASE CHAIN REACTION; STATE OBSERVER; TIME COURSE;

BIOINFORMATICS; ESTIMATION; FLOW CYTOMETRY; GENE EXPRESSION; LEAST SQUARES APPROXIMATIONS; PARAMETER ESTIMATION; POLYMERASE CHAIN REACTION; RATE CONSTANTS;

BIOLOGICAL SYSTEMS;

EID: 84861648926     PISSN: 10498923     EISSN: 10991239     Source Type: Journal    
DOI: 10.1002/rnc.2794     Document Type: Article

References (36)

1. Ingram PJ, Stumpf MPH, Stark J,. Network motifs: structure does not determine function. BMC Genomics 2006; 7 (1): 108-119. DOI: 10.1186/1471-2164-7- 108.
2. Mayo AE, Setty Y, Shavit S, Zaslaver A, Alon U,. Plasticity of the cis-regulatory input function of a gene. PLoS Biology 2006; 4 (4): 555-561. DOI: 10.1371/journal.pbio.0040045.
3. Mendes P, Kell D,. Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 1998; 14 (10): 869-883. DOI: 10.1093/bioinformatics/14.10.869.
4. Kirkpatrick S, Gelatt CD, Vecchi MP,. Optimization by simulated annealing. Science 1983; 220 (4598): 671-680. DOI: 10.1126/science.220.4598.671.
5. Srinivas M, Patnaik L,. Genetic algorithms: a survey. Computer Jun 1994; 27 (6): 17-26. DOI: 10.1109/2.294849.
6. Ashyraliyev M, Jaeger J, Blom J,. Parameter estimation and determinability analysis applied to drosophila gap gene circuits. BMC Systems Biology 2008; 2 (1): 83-101. DOI: 10.1186/1752-0509-2-83.
7. Moles CG, Mendes P, Banga JR,. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Research 2003; 13: 2467-2474. DOI: 10.1101/gr.1262503.
8. Muller TG, Faller D, Timmer J, Swameye I, Sandra O, Klingmuller U,. Tests for cycling in a signalling pathway. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2004; 53 (4): 557-568. DOI: 10.1111/j.1467-9876. 2004.05148.x.
9. Bortz DM, Nelson PW,. Model selection and mixed-effects modeling of HIV infection dynamics. Bullettin of Mathematical Biology 2006; 68 (8): 2005-2025. DOI: 10.1007/s11538-006-9084-x.
10. Wilkinson DJ,. Bayesian methods in bioinformatics and computational systems biology. Briefings in Bioinformatics 2007; 8 (2): 109-116. DOI: 10.1093/bib/bbm007.
11. McAdams HH, Arkin A,. It's a noisy business! Genetic regulation at the nanomolar scale. Trends in Genetics 1999; 15 (2): 65-69. DOI: 10.1016/S0168-9525(98)01659-X.
12. Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH,. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface 2009; 6 (31): 187-202. DOI: 10.1098/rsif.2008.0172.
13. Brooks SP,. Markov chain Monte Carlo method and its application. Journal of the Royal Statistical Society, Series D (The Statistician) 1998; 47 (1): 69-100. DOI: 10.1111/1467-9884.00117.
14. Brown KS, Sethna JP,. Statistical mechanical approaches to models with many poorly known parameters. Physical Reviews E Aug 2003; 68 (2): 021904-1-9. DOI: 10.1103/PhysRevE.68.021904.
15. Battogtokh D, Asch DK, Case ME, Arnold J, Schottler HB,. An ensemble method for identifying regulatory circuits with special reference to the qa gene cluster of Neurospora crassa. Proceedings of the National Academy of Sciences of the United States of America 2002; 99 (26): 16904-16909. DOI: 10.1073/pnas.262658899.
16. Sisson SA, Fan Y, Tanaka MM,. Sequential Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences of the United States of America 2007; 104 (6): 1760-1765. DOI: 10.1073/pnas.0607208104.
17. Hulhoven X, Wouwer AV, Bogaerts P,. Hybrid extended Luenberger-asymptotic observer for bioprocess state estimation. Chemical Engineering Science 2006; 61 (21): 7151-7160. DOI: 10.1016/j.ces.2006.06.018.
18. Quach M, Brunel N, d'Alche Buc F,. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinformatics 2007; 23 (23): 3209-3216. DOI: 10.1093/ bioinformatics/btm510.
19. Sun X, Jin L, Xiong M,. Extended Kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks. PLoS ONE 2008; 3 (11): e3758-1-13. DOI: 10.1371/journal.pone.0003758.
20. Wang Z, Liu X, Liu Y, Liang J, Vinciotti V,. An extended Kalman filtering approach to modelling nonlinear dynamic gene regulatory networks via short gene expression time series. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2009; 6 (3): 410-419. DOI: 10.1109/TCBB.2009.5.
21. Bullinger E, Allgower F,. An adaptive high-gain observer for nonlinear systems. 36th IEEE Conference on Decision and Control (CDC97), San Diego, CA, USA, vol. 5, 1997; 4348-4353. DOI: 10.1109/CDC.1997.649541.
22. Farina M, Findeisen R, Bullinger E, Bittanti S, Allgower F, Wellstead P,. Results towards identifiability properties of biochemical reaction networks. 45th IEEE Conference on Decision and Control (CDC06), San Diego, CA, USA, 2006; 2104-2109. DOI: 10.1109/CDC.2006.376925.
23. Fey D, Findeisen R, Bullinger E,. Parameter estimation in kinetic reaction models using nonlinear observers is facilitated by model extensions. 17th IFAC World Congress, Seoul, Korea, 2008, DOI: 10.3182/20080706-5-KR-1001. 00053.
24. Munsky B, Trinh B, Khammash M,. Listening to the noise: random fluctuations reveal gene network parameters. Mol Syst Biol 10 2009; 5. DOI: 10.1038/msb.2009.75.
25. Poovathingal S, Gunawan R,. Global parameter estimation methods for stochastic biochemical systems. BMC Bioinformatics 2010; 11 (1): 414-425. DOI: 10.1186/1471-2105-11-414.
26. Lillacci G, Khammash M,. Parameter estimation and model selection in computational biology. PLoS Computational Biology 2010; 6 (3): e1000696-1-17. DOI: 10.1371/journal.pcbi.1000696.
27. 2 criteria. 49th IEEE Conference on Decision and Control (CDC10), Atlanta, GA, USA, 2010, DOI: 10.1109/CDC.2006.376908.
28. Simon D,. Optimal state estimation. Wiley: Hoboken, New Jersey, USA, 2006.
29. Ristic B, Arulampalam S, Gordon N,. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech Print on Demand: London, 2004.
30. Dvoretzky A, Kiefer J, Wolfowitz J,. Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator. Annals of Mathematical Statistics 1956; 27 (3): 642-669. DOI: 10.1214/aoms/1177728174.
31. Massart P,. The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality. Annals of Probability 1990; 18 (3): 1269-1283. DOI: 10.1214/aop/1176990746.
32. Fisher RA,. Statistical Methods for Research Workers. Oliver and Boyd: Edinburgh, 1925.
33. Nocedal J, Wright SJ,. Numerical Optimization. Springer: New York, 1999.
34. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, et al,. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science Oct 2002; 298 (5594): 799-804. DOI: 10.1126/science.1075090.
35. Gillespie DT,. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 1976; 22 (4): 403-434. DOI: 10.1016/0021-9991(76)90041-3.
36. Galassi M, Davies J, Theiler J, Gough B, Jungman G, Alken P, Booth M, Rossi F,. GNU Scientific Library Reference Manual, Third edn. Network Theory: United Kingdom, 2009.