- Research Article
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Comparison of Gene Regulatory Networks via Steady-State Trajectories
EURASIP Journal on Bioinformatics and Systems Biology volume 2007, Article number: 82702 (2007)
The modeling of genetic regulatory networks is becoming increasingly widespread in the study of biological systems. In the abstract, one would prefer quantitatively comprehensive models, such as a differential-equation model, to coarse models; however, in practice, detailed models require more accurate measurements for inference and more computational power to analyze than coarse-scale models. It is crucial to address the issue of model complexity in the framework of a basic scientific paradigm: the model should be of minimal complexity to provide the necessary predictive power. Addressing this issue requires a metric by which to compare networks. This paper proposes the use of a classical measure of difference between amplitude distributions for periodic signals to compare two networks according to the differences of their trajectories in the steady state. The metric is applicable to networks with both continuous and discrete values for both time and state, and it possesses the critical property that it allows the comparison of networks of different natures. We demonstrate application of the metric by comparing a continuous-valued reference network against simplified versions obtained via quantization.
De Jong H: Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 2002, 9(1):67-103. 10.1089/10665270252833208
Srivastava R, You L, Summers J, Yin J: Stochastic vs. deterministic modeling of intracellular viral kinetics. Journal of Theoretical Biology 2002, 218(3):309-321. 10.1006/jtbi.2002.3078
Albert R, Barabási A-L: Statistical mechanics of complex networks. Reviews of Modern Physics 2002, 74(1):47-97. 10.1103/RevModPhys.74.47
Kim S, Li H, Dougherty ER, et al.: Can Markov chain models mimic biological regulation? Journal of Biological Systems 2002, 10(4):337-357. 10.1142/S0218339002000676
Albert R, Othmer HG: The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster . Journal of Theoretical Biology 2003, 223(1):1-18. 10.1016/S0022-5193(03)00035-3
Aburatani S, Tashiro K, Savoie CJ, et al.: Discovery of novel transcription control relationships with gene regulatory networks generated from multiple-disruption full genome expression libraries. DNA Research 2003, 10(1):1-8. 10.1093/dnares/10.1.1
Goutsias J, Kim S: A nonlinear discrete dynamical model for transcriptional regulation: construction and properties. Biophysical Journal 2004, 86(4):1922-1945. 10.1016/S0006-3495(04)74257-5
Li H, Zhan M: Systematic intervention of transcription for identifying network response to disease and cellular phenotypes. Bioinformatics 2006, 22(1):96-102. 10.1093/bioinformatics/bti752
Datta A, Choudhary A, Bittner ML, Dougherty ER: External control in Markovian genetic regulatory networks. Machine Learning 2003, 52(1-2):169-191.
Choudhary A, Datta A, Bittner ML, Dougherty ER: Control in a family of boolean networks. IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS '06), College Station, Tex, USA, May 2006
Devroye L, Györffi L, Lugosi G: A Probabilistic Theory of Pattern Recognition. Springer, New York, NY, USA; 1996.
Ivanov I, Dougherty ER: Modeling genetic regulatory networks: continuous or discrete? Journal of Biological Systems 2006, 14(2):219-229. 10.1142/S0218339006001763
Ivanov I, Dougherty ER: Reduction mappings between probabilistic boolean networks. EURASIP Journal on Applied Signal Processing 2004, 2004(1):125-131. 10.1155/S1110865704309182
Ott S, Imoto S, Miyano S: Finding optimal models for small gene networks. Proceedings of the Pacific Symposium on Biocomputing (PSB '04), Big Island, Hawaii, USA, January 2004 557-567.
Wessels LF, van Someren EP, Reinders MJ: A comparison of genetic network models. Proceedings of the Pacific Symposium on Biocomputing (PSB '01), Lihue, Hawaii, USA, January 2001 508-519.
Elowitz MB, Levine AJ, Siggia ED, Swain PS: Stochastic gene expression in a single cell. Science 2002, 297(5584):1183-1186. 10.1126/science.1070919
Kauffman SA: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, New York, NY, USA; 1993.
Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P: Molecular Biology of the Cell. 4th edition. Garland Science, New York, NY, USA; 2002.
Kauffman SA: Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology 1969, 22(3):437-467. 10.1016/0022-5193(69)90015-0
Lynn PA: An Introduction to the Analysis and Processing of Signals. John Wiley & Sons, New York, NY, USA; 1973.
Arkin A, Ross J, McAdams HH: Stochastic kinetic analysis of developmental pathway bifurcation in phage -infected Escherichia coli cells. Genetics 1998, 149(4):1633-1648.
Iyer V, Struhl K: Absolute mRNA levels and transcriptional initiation rates in Saccharomyces cerevisiae . Proceedings of the National Academy of Sciences of the United States of America 1996, 93(11):5208-5212. 10.1073/pnas.93.11.5208
Lorsch JR, Herschlag D: Kinetic dissection of fundamental processes of eukaryotic translation initiation in vitro. EMBO Journal 1999, 18(23):6705-6717. 10.1093/emboj/18.23.6705
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Brun, M., Kim, S., Choi, W. et al. Comparison of Gene Regulatory Networks via Steady-State Trajectories. J Bioinform Sys Biology 2007, 82702 (2007). https://doi.org/10.1155/2007/82702
- Predictive Power
- Regulatory Network
- Model Complexity
- System Biology
- Computational Power