Evaluation of design strategies for time course experiments in genetic networks: the XlnR regulon in Aspergillus niger
Authors: 
Jimmy Omony, Astrid R. Mach-Aigner, Leo H. de Graaff, Gerrit van Straten, Anton J. B. van BoxtelAuthors Info & Claims
Pages 25 - 33
Abstract
One of the challenges in the reconstruction of genetic network is to find experimental designs that maximize the information content in the data. In this work the information value of time course experiments (TCEs) is used to rank experimental designs. The study concerns the dynamic response of genes in the XlnR regulon of Aspergillus niger, whereby it was the goal to find the best moment to administer an extra pulse of inducing D-xylose. Low and high trigger concentrations are considered. The models that govern the regulation of the target genes in this regulon are used for simulation. Parameter sensitivity analysis, Fisher Information Matrix (FIM) and the E-modified criterion are used for the design performance assessment. The results show that the best time to give a second pulse of a low concentration trigger of D-xylose is when the D-xylose concentration from the first pulse is not yet completed reduced. Secondly, pulses with high trigger concentrations were simulated, parameter sensitivities computed, and the experimental designs evaluated. Overall, after the first pulse of 1 mM D-xylose, using a second pulse of 5 (or 10) mM D-xylose yields the best experimental design - leading to improved parameter estimates.
References
[1]
E. Balsa-Canto, A. A. Alonso, and J. R. Banga. Computational procedures for optimal experimental design in biological systems. IET Syst. Biol., 2(4):163--172, Jul. 2008.
[2]
M. Baltes, R. Schneider, C. Sturm, and M. Reus. Optimal experimental design for parameter estimation in unstructured growth models. Biotechnol., 10(5):480--488, Sep. 1994.
[3]
M. Bentele, I. Lavrik, M. Ulrich, S. Stober, D. W. Heermann, H. Kalthoff, P. H. Krammer, and R. Eils. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J. Cell Biol., 166(6):839--851, Sep. 2004.
[4]
K. Bernaerts, K. P. M. Gysemans, T. N. Minh, and J. F. van Impe. Optimal experiment design for cardinal values estimation: guidelines for data collection. Int. J. Food Microbiol., 100(1--3):153--165, Jan. 2005.
[5]
L. H. de Graaff, H. C. van den Broek, A. J. J. van Ooijen, and J. Visser. Regulation of the xylanase-encoding xlnA gene of Aspergillus tubingensis. Mol. Microbiol., 12(3):479--490, May 1994.
[6]
D. J. W. de Pauw and P. A. Vanrolleghem. Practical aspects of sensitivity function approximation for dynamic models. Math. Comp. Modell. of Dyn. Sys., 12(5):395--414, Oct. 2006.
[7]
V. J. R. P. de Vries and L. H. de Graaff. CreA modulates the XlnR-induced expression on xylose of Aspergillus niger genes involved in xylan degradation. Res. Microbiol., 150(4):281--285, May 1999.
[8]
J. M. Dresch, X. Liu, D. N. Arnosti, and A. Ay. Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects. BMC Syst. Biol., 4:142, Oct. 2010.
[9]
X. J. Feng and H. Robitz. Optimal identification of biochemical reaction networks. Biophys. J, 86(3):1270--81, Mar. 2004.
[10]
F. Geier, J. Timmer, and C. Fleck. Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge. BMC Syst. Biol., 1(Suppl 1):11, Feb. 2007.
[11]
K. Godfrey and J. di Stefano III. Identifiability of model parameters. In Identification and System Parameter Estimation, 174(2):89--114, 1985.
[12]
G. Goodwin. Identification: Experiment design. Systems and Control Encyclopedia, 4:2257--2264, 1987.
[13]
A. V. Hill. The possible effect of the aggregation of the molecules of haemoglobin on its dissociation curves. J. Physiol., 40(4):4--7, 1910.
[14]
K. J. Keesman and J. D. Stigter. Optimal parametric sensitivity control for the estimation of kinetic parameters in bioreactors. Mathematical Biosciences, 179(1):95--111, Jul-Aug. 2002.
[15]
H. Kitano. Systems biology: a brief overview. Science, 295(5560):1662--1664, Mar. 2002.
[16]
Z. Kutalik, K. H. Cho, and O. Wolkenhauer. Optimal sampling time selection for parameter estimation in dynamic pathway modelling. BioSystems, 75(1--3):43--55, Jul. 2004.
[17]
L. Ljung. Systems identification. Theory for the user, Prentice Hall PRT., page 21, 1999.
[18]
B. Melykuti, E. August, A. Papachristodoulou, and H. El-Samad. Discriminating between rival biochemical network models: three approaches to optimal experiment design. BMC Syst. Biol., 4(1):381, Apr. 2010.
[19]
C. Michalik, M. Stuckert, and W. Marquardt. Optimal experimental design for discriminating numerous model candidates: The AWDC criterion. Ind. Eng. Chem. Res., 49(2):913--919, Dec. 2009.
[20]
J. Omony, L. H. de Graaff, G. van Straten, and A. J. B. van Boxtel. Modeling and analysis of the dynamics behavior of the XlnR regulon in Aspergillus niger. BMC Syst. Biol., 5(Suppl 1):S14, May 2011.
[21]
A. Polynikis, S. J. Hogan, and M. di Bernardo. Comparing different ODE modeling approaches of gene regulatory networks. J. Theor. Biol., 261(4):511--530, Dec. 2009.
[22]
L. Pronzato. Optimal experimental design and some related control problems. IFAC: Automatica, 44(2):303--325, Nov. 2008.
[23]
G. J. G. Ruijter, S. A. Vanhanen, M. M. C. Gielkens, P. J. I. van de Vondervoort, and J. Visser. Isolation of Aspergillus niger creA mutants and effects of the mutations on expression of arabinases and L-arabinose catabolic enzymes. Microbiology, 143:2991--2998, 1997.
[24]
K. Selvarajoo and M. Tsuchiya. Systematic determination of biological network topology: Nonintegral connectivity method (NICM). Introduction to systems biology, Choi S (editor). The Humana Press Inc., Part IV:449--471, 2007.
[25]
J. D. Stigter and K. J. Keesman. Optimal parametric sensitivity control of a fed-batch reactor. Automatica, 40(8):1459--1464, Apr. 2004.
[26]
D. van der Veen, J. M. Oliveira, W. A. M. van den Berg, and L. H. de Graaff. Variance component analysis reveals contribution of sample processing to transcript variation. Appl. Environ. Microbiol., 75(8):2414--2422, Feb. 2009.
[27]
N. N. M. E. van Peij, M. M. C. Gielkens, R. P. de Vries, J. Visser, and L. H. de Graaff. The transcriptional activator XlnR regulates both xylanolytic and endoglucanase gene expression in Aspergillus niger. Appl. Environ. Microbiol., 64(10):3615--3619, Oct. 1998.
[28]
P. Vanrolleghem, M. van Daele, P. van Overschee, and G. Vansteenkiste. Model structure characterization of nonlinear water treatment systems. In: M. Blanke and T. Soderstrom (eds.): Proc. 10th IFAC Conf. Syst. Ident., Danish Automation Society, Copenhagen, DK, 1:279--284, Jul. 1994.
[29]
P. A. Vanrolleghem, F. Coen, K. Gernaey, B. Petersen, B. de Clercq, and J. P. Ottoy. Limitations of short term experiments designed for identification of activated sludge biodegradation models by fast dynamic phenomena. In Proceedings 7th IFAC Conference on Computer Application in Biotechnology CAB7, pages 567--572, Jun. 1998.
[30]
P. A. Vanrolleghem, M. van Daele, and D. Dochain. Practical identifiability of a biokinetic model of activated sludge respiration. Wat. Res., 29(11):2561--2570, Nov. 1995.
[31]
K. J. Versyck, J. E. Claes, and J. F. V. Impe. Optimal experimental design for practical identification of unstructured growth models. Math. Comput. Simul., 46(5--6):621--629, Jun. 1998.
[32]
F. X. Wu, L. Mu, and R. Luo. Complexity analysis and optimal experimental design for parameter estimation of biological systems. IEEE Xplore, 4--7:393--398, Jul. 2008.
[33]
W. H. Wu, F. S. Wang, and M. S. Chang. Dynamic sensitivity analysis of biochemical systems. BMC Bioinformatics, 9(Suppl 12):S17, Dec. 2008.
[34]
M. K. S. Yeung, J. Tegner, and J. J. Collins. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci., 99(9):6163--6168, Apr. 2002.
[35]
H. Yue, M. Brown, J. Knowles, H. Wang, D. S. Broomhead, and D. B. Kell. Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-κB signalling pathway. Mol. Biosyst., 2(12):640--649, Oct. 2006.
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Published: 21 September 2011
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