Abstract
A workflow for data analysis is introduced to synthesize flux regulation maps of a Metabolic P system from time series of data observed in laboratory. The procedure is successfully tested on a significant case study, the photosynthetic phenomenon called NPQ, which determines plant accommodation to environmental light. A previously introduced MP model of such a photosynthetic process has been improved, by providing an MP system with a simpler regulative network that reproduces the observed behaviors of the natural system. Two regression techniques were employed to find out the regulation maps, and interesting experimental results came out in the context of their residual analysis for model validation.
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ROS are chemical species producing a dangerous effect known as photooxidative damage.
The authors wish to gratefully thank V. Manca, who kindly suggested this idea.
References
Aczel AD, Sounderpandian J (2006) Complete business statistics. McGraw-Hill, Irwin
Ahn TK, Avenson TJ, Ballottari M, Cheng YC, Niyogi KK, Bassi R, Fleming GR (2008) Architecture of a charge-transfer state regulating light harvesting in a plant antenna protein. Science 320(5877):794–797
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Castellini A, Manca V (2009) Learning regulation functions of metabolic systems by artificial neural networks. In: GECCO ’09: Proceedings of the 11th annual conference on genetic and evolutionary computation. ACM, New York, NY, USA, pp 193–200
Crampin EJ, Schnell S, McSharry PE (2004) Mathematical and computational techniques to deduce complex biochemical reaction mechanisms. Prog Biophys Mol Biol 86(1):77–112
Efroymson MA (1960) Multiple regression analysis. Math Methods Digit Comput 1:191–203
Evron Y, McCarty RE (2000) Simultaneous measurement of deltapH and electron transport in chloroplast thylakoids by 9-aminoacridine fluorescence. Plant Physiol 124:407–414
Franco G, Manca V, Pagliarini R (2010). Regulation and covering problems in MP systems. In: Paun Gh, Perez-Jimenez MJ, Riscos-Nunez A (eds) WMC 2009, LNCS 5957. Springer, pp 242–251
Gisselsson A, Szilagyi A, Akerlund H (2004) Role of histidines in the binding of violaxanthin de-epoxidase to the thylakoid membrane as studied by site-directed mutagenesis. Physiologia Plantarum 122:337–343
Goldbeter A (1991) A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. PNAS 88(20):9107–9111
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Hocking RR (1976) The analysis and selection of variables in linear regression. Biometrics 32(1):1–49
Izenman AJ (2008) Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer Publishing Company, Incorporated
Kanazawa A, Kramer DM (2002) In vivo modulation of nonphotochemical exciton quenching (NPQ) by regulation of the chloroplast ATP synthase. PNAS 99(20):12789–12794
Manca V (2008) The metabolic algorithm: principles and applications. Theor Comput Sci 404:142–157
Manca V (2009a) Fundamentals of metabolic P systems. In: Păun G, Rozenberg G, Salomaa A (eds) Handbook of membrane computing, chapter 16. Oxford University Press, Oxford
Manca V (2009b) Log-gain principles for metabolic P systems. In: Condon A, Harel D, Kok JN, Salomaa A, Winfree E (eds) Algorithmic bioprocesses, natural computing series, chapter 28. Springer, Berlin Heidelberg
Manca V, Bianco L (2008) Biological networks in metabolic P systems. Biosystems 91(3):489–498
Manca V, Bianco L, Fontana F (2005) Evolutions and oscillations of P systems: Applications to biochemical phenomena. In: LNCS 3365. Springer, pp 63–84
Manca V, Pagliarini R, Zorzan S (2009) A photosynthetic process modelled by a metabolic P system. Nat Comput 8(4):847–864
Maxwell K, Johnson GN (2000) Chlorophyll fluorescence—a practical guide. J Exp Bot 51(345):659–668
MetaPlab website (2005) http://www.mplab.scienze.univr.it
Pagliarini R, Franco G, Manca V (2009) An algorithm for initial fluxes of metabolic P systems. Int J Comput Commun Control 4(3):263–272
Păun G (2002) Membrane computing. An introduction. Springer, Berlin
Pyle D (1999) Data preparation for data mining (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann, San Fransisco
Rivals I, Personnaz L (2003) MLPs (mono layer polynomials and multi layer perceptrons) for nonlinear modeling. J Mach Learn Res 3:1383–1398
Soranzo N, Bianconi G, Altafini C (2007) Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks. Bioinformatics 23(13):1640–1647
Supplementary material (2005) http://www.mplab.scienze.univr.it/external/natcomp/page.html
Torkkola K (2003) Feature extraction by non-parametric mutual information maximization. J Mach Learn Res 3:1415–1438
Trubitsin BV, Tikhonov AN (2003) Determination of a transmembrane pH difference in chloroplasts with a spin label tempamine. J Magn Reson 163:257–269
von Bertalanffy L (1967) General systems theory: foundations, developments, applications. George Braziller Inc., New York, NY
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Castellini, A., Franco, G. & Pagliarini, R. Data analysis pipeline from laboratory to MP models. Nat Comput 10, 55–76 (2011). https://doi.org/10.1007/s11047-010-9200-6
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DOI: https://doi.org/10.1007/s11047-010-9200-6