Lahdesmaki H, Shmulevich I:On learning gene regulatory networks under the boolean network odel. Machine Learn. 2008, 71: 185-217. 10.1007/s10994-008-5053-y. Springer Netherlands Publishers,

Article
Google Scholar

Chemmangattuvalappil N, Task K, Banerjee I:An integer optimization algorithm for robust identification of non-linear gene regulatory networks. BMC Syst Biol. 2013, 2 (6): 119-

Google Scholar

Bonneau R:Learning biological networks: from modules to dynamics. Nat Chem Biol. 2008, 4 (11): 658-664. 10.1038/nchembio.122.

Article
CAS
PubMed
Google Scholar

Laubenbacher R, Stigler B:A computational algebra approach to the reverse-engineering of gene regulatory networks. J Theor Biol. 2004, 229: 523-537. 10.1016/j.jtbi.2004.04.037.

Article
CAS
PubMed
Google Scholar

Madar A, Greenfield A:DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator. PLoS One. 2004, 5 (3): 9803-

Article
Google Scholar

Porreca R, Cinquemani E, Lygeros J, Ferrari-Trecate G:Identification of genetic network dynamics with unate structure. Bioinformatics. 2010, 26 (9): 1239-12345. 10.1093/bioinformatics/btq120.

Article
CAS
PubMed
Google Scholar

Hempel S, Koseska A, Nikoloski Z, Kurths J:Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study. BMC Bioinformatics. 2011, 12: 292-10.1186/1471-2105-12-292.

Article
PubMed Central
PubMed
Google Scholar

Jaynes E:Prior information and ambiguity in inverse problems. SIAM AMS Proc. 1984, 14: 151-166.

Google Scholar

Wahde M, Hertz J:Modeling genetic regulatory dynamics in neural development. J Comput Biol. 2001, 8: 429-442. 10.1089/106652701752236223.

Article
CAS
PubMed
Google Scholar

Yeung MKS, Tegner J, Collins JJ:Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci. 2002, 99 (9): 6163-6168. 10.1073/pnas.092576199.

Article
PubMed Central
CAS
PubMed
Google Scholar

Gardner TS, di Bernardo DX, Collins JJ:Inferring genetic networks and identifying compound mode of action via expression profiling. Science. 2003, 301: 102-105. 10.1126/science.1081900.

Article
CAS
PubMed
Google Scholar

Marbach D: Evolutionary Reverse Engineering of Gene Networks. 2009, Ph.D. thesis: Ecole Polytechnique Federale de Laussane,

Google Scholar

Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, Klamt S, Sorger PK:Discrete logical modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Sys Biol. 2009, 5: 331-

Google Scholar

Greenfield A, Madar A, Ostrer H, Bonneau R:DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models. PLoS ONE. 2010, 5 (10): e13397-10.1371/journal.pone.0013397.

Article
PubMed Central
PubMed
Google Scholar

Lo K, Raftery AE, Dombek KM, Zhu J, Schadt EE, Bumgarner RE, Yeung KY:(2012). Integrating external biological knowledge in the construction of regulatory networks from time-series expression data. BMC Syst Biol. 2012, 6: 101-10.1186/1752-0509-6-101.

Article
PubMed Central
PubMed
Google Scholar

Wang SQ, Li HX:Bayesian inference based modelling for gene transcriptional dynamics by integrating multiple source of knowledge. BMC Syst Biol. 2012, 6 (Suppl 1): S3-10.1186/1752-0509-6-S1-S3.

Article
PubMed Central
PubMed
Google Scholar

Xiao Y, Dougherty E:The impact of function perturbations in Boolean networks. Bioinformatics. 2007, 23 (10): 1265-1273. 10.1093/bioinformatics/btm093.

Article
CAS
PubMed
Google Scholar

Yu J, Smith J, Hartemink A, Jarvis ED:Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics. 2004, 20 (18): 3594-3603. 10.1093/bioinformatics/bth448.

Article
CAS
PubMed
Google Scholar

Jarrah A, Laubenbacher R, Stigler B, Stillman M:Reverse engineering of polynomial dynamical systems. Adv Appl Math. 2007, 39 (4): 477-489. 10.1016/j.aam.2006.08.004.

Article
Google Scholar

Friedman N, Linial M, Nachman I, Pe’er D:Using Bayesian networks to analyze expression data. J Comput Biol. 2000, 7: 601-620. 10.1089/106652700750050961.

Article
CAS
PubMed
Google Scholar

Shmulevich I, Dougherty ER, Kim S, Zhang W:Probabilistic Boolean networks: a rule based uncertainty model for gene regulatory networks. Bioinformatics. 2002, 18: 261-274. 10.1093/bioinformatics/18.2.261.

Article
CAS
PubMed
Google Scholar

Ferrazzi F, Sebastiani P, Ramoni M, Bellazzi R:Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks. BMC Bioinformatics. 2007, 8 (Suppl 5): S2-10.1186/1471-2105-8-S5-S2.

Article
PubMed Central
PubMed
Google Scholar

Zhao W, Serpedin E, Dougherty ER:Inferring connectivity of genetic regulatory networks using information-theoretic criteria. Comput Biol and Bioinformatics, IEEE/ACM Trans. 2008, 5 (2): 262-274.

Article
CAS
Google Scholar

Chaouiya C, Remy E, Thieffry D:Petri net modelling of biological regulatory networks. Jo Discrete Algo. 2008, 6 (2): 165-177. 10.1016/j.jda.2007.06.003.

Article
Google Scholar

Dimitrova Es, Garcia-Puente LD, Hinkelmann F, Jarrah AS, Laubenbacher R, Stigler B, Stillman M, Vera-Licona P:Parameter estimation for boolean models of biological networks. J Theor Comput Science. 2011, 412: 26-

Article
Google Scholar

Noman N, Iba H:Inference of genetic networks using S-system: information criteria for model selection. GECCO ’06: Proceedings of the 8th annual conference on Genetic and evolutionary computation. 2006, New York, NY, USA: ACM, 263-270.

Chapter
Google Scholar

Ferrazzi F, Magni P, Sacchi L, Nuzzo A, Petrovic U, Bellazzi R:Inferring gene regulatory networks by integrating static and dynamic data. Int J Med Info. 2007, 76 (Supplement 3): S462-S475.

Article
Google Scholar

Xu R, Venayagamoorthy GK, Donald C, Wunsch I:Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization. Neural Netw. 2007, 20 (8): 917-927. 10.1016/j.neunet.2007.07.002.

Article
PubMed
Google Scholar

Lee WP, Yang KC:A clustering-based approach for inferring recurrent neural networks as gene regulatory networks. Neurocomputation. 2008, 71: 600-610. 10.1016/j.neucom.2007.07.023.

Article
Google Scholar

Kotte O, Heinemann M:A divide-and-conquer approach to analyze underdetermined biochemical models. Bioinformatics. 2009, 25 (4): 519-525. 10.1093/bioinformatics/btp004.

Article
CAS
PubMed
Google Scholar

Bauer DC, Bailey TL:Optimizing static thermodynamic models of transcriptional regulation. Bioinformatics. 2009, 25 (13): 1640-1646. 10.1093/bioinformatics/btp283.

Article
PubMed Central
CAS
PubMed
Google Scholar

Haury AC, Mordelet F, Vera-Licona P, Vert JP:TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst Biol. 2012, 6 (1): 145-10.1186/1752-0509-6-145.

Article
PubMed Central
PubMed
Google Scholar

Stolovitzky G, Monroe D, Califano A:Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann New York Acad Sci. 2007, 1115: 1-22. 10.1196/annals.1407.021.

Article
Google Scholar

Stolovitzky G, Prill RJ, Califano A:Lessons from the DREAM2 challenges: a community effort to assess biological network inference. Ann New York Acad Sci. 2009, 1158 (37): 159-195.

Article
CAS
Google Scholar

Marbach D, Costello J, Kuffner R, Vega N, Pril l, Camacho D, Allison K, Kellis M, Collins J, Stolovitzky G:The DREAM5 Consortium. Wisdom of crowds for robust gene network inference. Nat Methods. 2012, 9 (8): 796-804. 10.1038/nmeth.2016.

Article
PubMed Central
CAS
PubMed
Google Scholar

Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V, Bansal M, Santini S, di Bernardo M, di Bernardo D, Cosma MP:A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell. 2009, 137 (1): 172-181. 10.1016/j.cell.2009.01.055.

Article
CAS
PubMed
Google Scholar

Vera-Licona P: Algorithms for modeling and simulation of biological systems: applications to gene regulatory networks. 2007, Ph.D. thesis: Virginia Polytechnic Institute and State University,

Google Scholar

Stigler B:Polynomial dynamical systems in systems biology. AMS 2006 Proceedings of Symposia in Applied Mathematics. Edited by: Laubenbacher R. 2006, American Mathematical Society, Providence, RI,, 59-84.

Google Scholar

Garg A, Di Cara A, Xenarios I, Mendoza L, DeMicheli G:Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics. 2008, 1: 306-312.

Google Scholar

Albert R, Othmer H:The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J Theor Biol. 2003, 223: 1-18. 10.1016/S0022-5193(03)00035-3.

Article
CAS
PubMed
Google Scholar

Albert I, Thakar J, Li S, Zhang R, Albert R:Boolean network simulations for life scientists. Source Code Biol Med. 2008, 3: 16-10.1186/1751-0473-3-16.

Article
PubMed Central
PubMed
Google Scholar

Boettiger AN, Levine M:Synchronous and stochastic patterns of gene activation in the drosophila embryo. Science. 2009, 325: 471-473. 10.1126/science.1173976.

Article
PubMed Central
CAS
PubMed
Google Scholar

Walker KA, Miller VL:Synchronous gene expression of the Yersinia enterocolitica Ysa Type III secretion system and its effectors. J Bacteriol. 2009, 191 (6): 1816-1826. 10.1128/JB.01402-08.

Article
PubMed Central
CAS
PubMed
Google Scholar

Kervizic G, Corcos L:Dynamical modeling of the cholesterol regulatory pathway with Boolean networks. IBMC Syst Biol. 2008, 2: 99-10.1186/1752-0509-2-99.

Article
Google Scholar

Chaves M, Albert R, Sontag ED:Robustness and fragility of Boolean models for genetic regulatory networks. J Theor Biol. 2005, 235 (3): 431-449. 10.1016/j.jtbi.2005.01.023.

Article
PubMed
Google Scholar

Garg A, Xenarios I, Mendoza L, DeMicheli G:An Efficient method for dynamic analysis of gene regulatory networks. RECOMB 2007. Edited by: Speed T, Huang H. 2007, Springer-Verlag Berlin, Heidelberg, 62-76.

Google Scholar

Onn S, Thomas R, Babson E:The Hilbert zonotope and a polynomial time algorithm for universal Gröbner bases. Adv Appl Math. 2003, 30: 529-544. 10.1016/S0196-8858(02)00509-2.

Article
Google Scholar

Mitchell M: An Introduction to Genetic Algorithms. 1999, Cambridge Massachusetts: The MIT Press,

Google Scholar

Fogel DB:Introduction to Evolutionary Computation. Evolutionary Computation 1: Basic Algorithms and Operators. Edited by: Back T, Fogel DB, Michalewicz Z. 2000, Institute of Physics Publishing Bristol and Philadelphia: Philadelphia, PA, 1-3.

Google Scholar

Sirbu A: Gene regulatory network modelling with evolutionary algorithms -an integrative approach. PhD thesis. Dublin City University, 2011,

Google Scholar

Pal S, Bandyopadhyay S, Ray S:Evolutionary computation in bioinformatics: a review. Systems, Man, and Cybernetics, Part C: Applications and Reviews. IEEE Trans. 2006, 36 (5): 601-615.

Google Scholar

Spieth C, Worzischek R, Streichert F, Supper J, Speer N, Zell A:Comparing evolutionary algorithms on the problem of network inference. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO 2006). ACM Press, New York, NY, 2006, 305–306,

Google Scholar

Sirbu A, Ruskin HJ, Crane M:Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics. 2010, 11: 59-10.1186/1471-2105-11-59.

Article
PubMed Central
PubMed
Google Scholar

Eiben AE, Smith JE:Introduction to evolutionary computing. Natural Computing Series. 2003, Berlin: Springer,

Google Scholar

Schwarz G:Estimating the dimension of a model. Ann Stat. 1978, 6 (2): 461-466. 10.1214/aos/1176344136.

Article
Google Scholar

Akaike H:Information theory and an extension of the maximum likelihood principle. Proc 2nd Int Symp Information Theory. Edited by: Petrov BN, Csaki F. 1973, Budapest: Akademiai Kiado, 267-281.

Google Scholar

Olsson G, Sandberg A, Dahlblom O:On latin hypercube sampling for structural reliability analysis. Struct Safety. 2003, 25 (1): 47-68. 10.1016/S0167-4730(02)00039-5.

Article
Google Scholar

Kimura S, Ide K, Kashihara A, Kano M, Hatakeyama M, Masui R, Nakagawa N, Yokoyama S, Kuramitsu S, Konagaya A:Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics. 2005, 21: 1154-1163. 10.1093/bioinformatics/bti071.

Article
CAS
PubMed
Google Scholar

Tsai KY, Wang FS:Evolutionary optimization with data collocation for reverse engineering of biological networks. Bioinformatics. 2005, 21 (7): 1180-1188. 10.1093/bioinformatics/bti099.

Article
CAS
PubMed
Google Scholar

Swain M, Hunniford etal.:Reverse engineering gene regulatory networks using evolutionary algorithms and grid computing. Clinical Monitoring Comput. 2005, 19: 329-337. 10.1007/s10877-005-0678-x.

Article
Google Scholar

Bevilacqua V, Mastronardi G:Bayesian gene regulatory network inference optimization by means of genetic algorithms. J Universal Comput Sci. 2009, 15 (4): 826-839.

Google Scholar

Dasgupta B, Vera-Licona P, Sontag E:Reverse engineering of molecular networks from a common combinatorial approach. Algorithms in Computational Molecular Biology. Edited by: Eiloumi M, Zomaya AY. 2011, New York: Wiley,

Google Scholar

Dimitrova E, Vera-Licona P, McGee J, Laubenbacher R:Discretization of time series data. J Comput Biol. 2010, 17 (6): 853-868. 10.1089/cmb.2008.0023.

Article
PubMed Central
CAS
PubMed
Google Scholar

Zoppoli P, Morganella S, Ceccarelli M:TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics. 2010, 11: 154-10.1186/1471-2105-11-154.

Article
PubMed Central
PubMed
Google Scholar

Zou C, Ladroue C, Guo S, Feng J:Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach. BMC Bioinformatics. 2010, 11: 337-10.1186/1471-2105-11-337.

Article
PubMed Central
PubMed
Google Scholar

Summer G, Perkins T:Functional data analysis for identifying nonlinear models of gene regulatory networks. BMC Genomics. 2011, 11 (Suppl 4): S18-

Article
Google Scholar

Bansal M, di Bernardo D:Inference of gene networks from temporal gene expression profiles. IET, Syst Biol. 2007, 1: 306-312. 10.1049/iet-syb:20060079.

Article
CAS
Google Scholar

Weng L, Dai H, Zhan Y, He Y, Stepaniants S, Bassett D:Rosetta error model for gene expression analysis. Bioinformatics. 2006, 22 (9): 1111-1121. 10.1093/bioinformatics/btl045.

Article
CAS
PubMed
Google Scholar

Klebanov L, Yakovlev A:How high is the level of technical noise in microarray data. Biology Direct. 2007, 2: 9-15. 10.1186/1745-6150-2-9.

Article
PubMed Central
PubMed
Google Scholar

Chen J, Hsueh H, Delongchamp R, Lin C, Tsai C:Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data. BMC Bioinformatics. 2007, 8: 412-418. 10.1186/1471-2105-8-412.

Article
PubMed Central
PubMed
Google Scholar