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One genetic algorithm per gene to infer gene networks from expression data

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Abstract

Gene regulatory networks inference from gene expression data is an important problem in systems biology field, in which the main goal is to comprehend the global molecular mechanisms underlying diseases for the development of medical treatments and drugs. This problem involves the estimation of the gene dependencies and the regulatory functions governing these interactions to provide a model that explains the dataset (usually obtained from gene expression data) on which the estimation relies. However, such problem is considered an open problem, since it is difficult to obtain a satisfactory estimation of the dependencies given a very limited number of samples subject to experimental noises. Several gene networks inference methods exist in the literature, including those based on genetic algorithms, which codify whole networks as possible solutions (chromosomes). Given the huge search space of possible networks, genetic algorithms are suitable for the task, even though it is still hard to achieve good networks that explain the data by codifying whole networks as solutions. The objective of this work is the proposal of a method based on genetic algorithms to infer gene networks, whose main idea consists in applying one genetic algorithm for each gene independently, instead of applying a unique genetic algorithm to determine the whole network as usually done in the literature. Besides, the method involves the application of a network inference method to generate the initial populations to serve as more promising starting points for the genetic algorithms than random populations. To guide the genetic algorithms, we propose the use of Akaike information criterion (AIC) as fitness function. Results obtained from inference of artificial Boolean networks show that AIC correlates very well with popular topological similarity metrics even in cases with small number of samples. Besides, the benefit of applying one genetic algorithm per gene starting from initial populations defined by a network inference technique is evident according to the results. Comparative analysis involving a recently proposed genetic algorithm method for the same purpose is presented, showing that our method achieves superior performance.

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References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  MathSciNet  MATH  Google Scholar 

  • Akutsu T, Miyano S, Kuhara S (1999) Identification of genetic networks from a small number of gene expression patterns under the boolean network model. Pac Symp Biocomput 4:17–28

    Google Scholar 

  • Albert R (2005) Scale-free networks in cell biology. J Cell Sci 118(21):4947–4957

    Article  Google Scholar 

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  Google Scholar 

  • Barrera J, Cesar-Jr RM, Martins-Jr DC, Vencio RZN, Merino EF, Yamamoto MM, Leonardi FG, Pereira CAB, del Portillo HA (2007) Constructing probabilistic genetic networks of Plasmodium falciparum from dynamical expression signals of the intraerythrocytic development cycle. In: Methods of microarray data analysis V, chap 2. Springer, Berlin, pp 11–26

  • Borelli FF, de Camargo RY, Martins-Jr DC, Rozante LCS (2013) Gene regulatory networks inference using a multi-gpu exhaustive search algorithm. BMC Bioinform 14(Suppl 18):S5

    Article  Google Scholar 

  • Brun M, Dougherty ER, Shmulevich I (2005) Steady-state probabilities for attractors in probabilistic boolean networks. Signal Process 85(10):1993–2013

    Article  MATH  Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, Berlin

    Google Scholar 

  • Dougherty ER, Brun M, Trent JM, Bittner ML (2009) A conditioning-based model of contextual regulation. IEEE/ACM Trans Comput Biol Bioinform 6(2):310–320

    Article  Google Scholar 

  • Dougherty ER (2011) Validation of gene regulatory networks: scientific and inferential. Brief Bioinform 12(3):245–252

    Article  Google Scholar 

  • Erdös P, Rényi A (1959) On random graphs. Publ Math Debr 6:290–297

    MATH  Google Scholar 

  • Friedman N, Linial M, Nachman I, Pe’er D (2000) Using bayesian networks to analyze expression data. J Comput Biol 7:601–620

    Article  Google Scholar 

  • Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R (2009) Gene regulatory network inference: data integration in dynamic models-a review. Biosystems 96:86–103

    Article  Google Scholar 

  • Husmeier D (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics 19:2271–2282

    Article  Google Scholar 

  • Ivanov I, Dougherty ER (2006) Modeling genetic regulatory networks: continuous or discrete? J Biol Syst 14(2):219–229

    Article  MATH  Google Scholar 

  • Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL (2000) The large-scale organization of metabolic networks. Nature 467:651–654

    Google Scholar 

  • Jimenez RD, Martins-Jr DC, Santos CS (2014) Gene networks inference through one genetic algorithm per gene. In: 14th IEEE international conference on bioinformatics and bioengineering (BIBE), Boca Raton, FL, pp 1–8

  • Jong HD (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9(1):67–103

    Article  Google Scholar 

  • Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9(10):770–780. doi:10.1038/nrm2503

    Article  Google Scholar 

  • Kauffman SA (1969) Homeostasis and differentiation in random genetic control networks. Nature 224(215):177–178

    Article  Google Scholar 

  • Lähdesmäki HL, Shmulevich I (2003) On learning gene regulatory networks under the boolean network model. Mach Learn 52:147–167

    Article  MATH  Google Scholar 

  • Liang S, Fuhrman S, Somogyi R (1998) Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Simp Biocomput 3:18–29

    Google Scholar 

  • Lopes FM, Martins-Jr DC, Cesar-Jr RM (2008) Feature selection environment for genomic applications. BMC Bioinform 9(451)

  • Lopes FM, Martins-Jr DC, Barrera J, Cesar-Jr RM (2014) A feature selection technique for inference of graphs from their known topological properties: revealing scale-free gene regulatory networks. Inf Sci 272:1–15. doi:10.1016/j.ins.2014.02.096. http://www.sciencedirect.com/science/article/pii/S0020025514002023

  • Lopes FM, Ray SS, Hashimoto RF, Cesar-Jr RM (2014b) Entropic biological score: a cell cycle investigation for GRNs inference. Gene 541(2):129–137

    Article  Google Scholar 

  • Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci 107(14):6286–6291

    Article  Google Scholar 

  • Mendoza MR, Lopes FM, Bazzan ALC (2012) Reverse engineering of grns: An evolutionary approach based on the tsallis entropy. In: Proceedings of the 14th international conference on genetic and evolutionary computation (GECCO), Philadelphia, pp 185–192

  • Nam D, Seo S, Kim S (2006) An efficient top-down search algorithm for learning boolean networks of gene expression. Mach Learn 65:229–245

    Article  Google Scholar 

  • Pal SK, Bandyopadhyay S, Ray SS (2006) Evolutionary computation in bioinformatics: A review. IEEE Trans Syst Man Cybern 36:601–615

    Article  Google Scholar 

  • Ristevski B (2013) A survey of models for inference of gene regulatory networks. Nonlinear Anal Model Control 18(4):444–465

    MathSciNet  MATH  Google Scholar 

  • Shin A, Iba H (2003) Construction of genetic network using evolutionary algorithm and combined fitness function. Genome Inform 14:94–103

    Google Scholar 

  • Shmulevich I, Dougherty ER, Kim S, Zhang W (2002) Probabilistic boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2):261–274

    Article  Google Scholar 

  • Shmulevich I, Dougherty ER (2007) Genomic signal processing. Princeton University Press, NJ

    Book  MATH  Google Scholar 

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Acknowledgments

We would like to especially thank Carlos Fernando Montoya Cubas for the program codes to generate the groundtruth networks and the simulated data. We also thank FAPESP Grants # 2011/50761-2 and # 2014/21050-9, CNPq, CAPES and NAP eScience—PRP—USP for the financial support.

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Authors and Affiliations

  1. Center of Mathematics, Computing and Cognition, Federal University of ABC, Santo André, SP, Brazil

    Ray Dueñas Jimenez, David Correa Martins-Jr & Carlos Silva Santos

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  1. Ray Dueñas Jimenez
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  2. David Correa Martins-Jr
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  3. Carlos Silva Santos
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Correspondence to David Correa Martins-Jr.

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The authors declare no conflict of interest and that this research did not involve human participants and/or animals.

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Jimenez, R.D., Martins-Jr, D.C. & Santos, C.S. One genetic algorithm per gene to infer gene networks from expression data. Netw Model Anal Health Inform Bioinforma 4, 19 (2015). https://doi.org/10.1007/s13721-015-0092-3

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  • DOI: https://doi.org/10.1007/s13721-015-0092-3

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