MORE: Mixed optimization for reverse engineering-An application to modeling biological networks response via sparse systems of nonlinear differential equations

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 9, Issue 5, 2012, Pages 1459-1471

  Sambo, Francesco ^a   Montes De Oca, Marco A ^b   Di Camillo, Barbara ^a   Toffolo, Gianna ^a   Stützle, Thomas ^c  

^a UNIVERSITY OF PADOVA   (Italy)

^b UNIVERSITY OF DELAWARE   (United States)

^c UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

Author keywords

biological networks; mixed optimization; Reverse engineering; sparse systems of differential equations

Indexed keywords

BIOLOGICAL NETWORKS; BIOLOGICAL VARIABLES; CONTINUOUS OPTIMIZATION; DISCRETE COMPONENTS; DISCRETE/CONTINUOUS; INFERRED NETWORKS; INTERACTION NETWORKS; MIXED OPTIMIZATION; NONLINEAR DIFFERENTIAL EQUATION; OPTIMIZATION ALGORITHMS; OPTIMIZATION APPROACH; PRIORI INFORMATION; REAL-WORLD NETWORKS; SPARSE SYSTEMS; STATE OF THE ART; TIME-SERIES DATA;

ACTIVE NETWORKS; ALGORITHMS; BIOLOGICAL SYSTEMS; DIFFERENTIAL EQUATIONS; ELECTRIC NETWORK TOPOLOGY; OPTIMIZATION;

REVERSE ENGINEERING;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; NONLINEAR SYSTEM;

ALGORITHMS; MODELS, BIOLOGICAL; NONLINEAR DYNAMICS;

EID: 84864932570     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2012.56     Document Type: Article

References (48)

1. L. Hunter, "Life and Its Molecules: A Brief Introduction," AI Magazine, special issue on AI and bioinformatics, vol. 25, no. 1, pp. 9-22, 2004.
2. N. Soranzo, G. Bianconi, and C. Altafini, "Comparing Association Network Algorithms for Reverse Engineering of Large-Scale Gene Regulatory Networks: Synthetic versus Real Data," Bioinformatics, vol. 23, no. 13, pp. 1640-1647, July 2007. (Pubitemid 47244454)
3. L.G. Alexopoulos, J. Saez Rodriguez, B.D. Cosgrove, D.A. Lauffenburger, and P.K. Sorger, "Networks Inferred from Biochemical Data Reveal Profound Differences in TLR and Inflammatory Signaling between Normal and Transformed Hepatocytes," Molecular and Cellular Proteomics, vol. 9, no. 9, pp. 1849-65, 2010.
4. J. Saez-Rodriguez, L.G. Alexopoulos, J. Epperlein, R. Samaga, D.A. Lauffenburger, S. Klamt, and P.K. Sorger, "Discrete Logic Modelling as a Means to Link Protein Signalling Networks with Functional Analysis of Mammalian Signal Transduction," Molecular Systems Biology, vol. 5, article 331, Dec. 2009.
5. R.J. Prill, D. Marbach, J. Saez-Rodriguez, P.K. Sorger, L.G. Alexopoulos, X. Xue, N.D. Clarke, G. Altan-Bonnet, and G. Stolovitzky, "Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges," PLoS ONE, vol. 5, no. 2, e9202 (epub), 2010.
6. I.-C. Chou and E.O. Voit, "Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems," Math. Biosciences, vol. 219, no. 2, pp. 57-83, 2009.
7. H.V. Westerhoff, A. Kolodkin, R. Conradie, S.J. Wilkinson, F.J. Bruggeman, K. Krab, J.H. van Schuppen, H. Hardin, B.M. Bakker, M.J. Moné, K.N. Rybakova, M. Eijken, H.J. van Leeuwen, and J.L. Snoep, "Systems Biology toward Life in Silico: Mathematics of the Control of Living Cells," J. Math. Biology, vol. 58, nos. 1/2, pp. 7-34, Jan. 2009.
8. S. Kimura, S. Nakayama, and M. Hatakeyama, "Genetic Network Inference as a Series of Discrimination Tasks," Bioinformatics, vol. 25, no. 7, pp. 918-925, 2009.
9. R. Xu, D. Wunsch II, and R. Frank, "Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 681-692, Oct.-Dec. 2007. (Pubitemid 350125889)
10. M. Arnone and E. Davidson, "The Hardwiring of Development: Organization and Function of Genomic Regulatory Systems," Development, vol. 124, pp. 1851-1864, 1997. (Pubitemid 27270255)
11. A.-L. Barabasi and R. Albert, "Emergence of Scaling in Random Networks," Science, vol. 286, no. 5439, pp. 509-512, Oct. 1999.
12. E. Ravasz, A.L. Somera, D.A. Mongru, Z.N. Oltvai, and A.L. Barabasi, "Hierarchical Organization of Modularity in Metabolic Networks," Science, vol. 297, no. 5586, pp. 1551-1555, Aug. 2002. (Pubitemid 34971002)
13. P. Le Phillip, A. Bahl, and L.H. Ungar, "Using Prior Knowledge to Improve Genetic Network Reconstruction from Microarray Data," Silico Biology, vol. 4, no. 3, pp. 335-353, 2004. (Pubitemid 39600480)
14. P. D'Haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, "Linear Modeling Of mRNA Expression Levels during CNS Development and Injury," Proc. Pacific Symp. Biocomputing, pp. 41-52, 1999.
15. T.S. Gardner, D.di Bernardo, D. Lorenz, and J.J. Collins, "Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling," Science, vol. 301, no. 5629, pp. 102-105, July 2003. (Pubitemid 36801571)
16. M. Bansal, G.D. Gatta, and D. di Bernardo, "Inference of Gene Regulatory Networks and Compound Mode of Action from Time Course Gene Expression Profiles," Bioinformatics, vol. 22, no. 7, pp. 815-822, 2006.
17. D. Nam, S.H. Yoon, and J.F. Kim, "Ensemble Learning of Genetic Networks from Time-Series Expression Data," Bioinformatics, vol. 23, no. 23, pp. 3225-3231, 2007. (Pubitemid 350187526)
18. S. Kimura, K. Ide, A. Kashihara, M. Kano, M. Hatakeyama, R. Masui, N. Nakagawa, S. Yokoyama, S. Kuramitsu, and A. Konagaya, "Inference of S-System Models of Genetic Networks Using a Cooperative Coevolutionary Algorithm," Bioinformatics, vol. 21, no. 7, pp. 1154-1163, 2005. (Pubitemid 40467940)
19. R. Xu, G.K. Venayagamoorthy, and D.C. Wunsch II, "Modeling of Gene Regulatory Networks with Hybrid Differential Evolution and Particle Swarm Optimization," Neural Networks, vol. 20, no. 8, pp. 917-927, 2007. (Pubitemid 47513434)
20. P.-K. Liu and F.-S. Wang, "Inference of Biochemical Network Models in S-System Using Multiobjective Optimization Approach," Bioinformatics, vol. 24, no. 8, pp. 1085-1092, 2008. (Pubitemid 351514059)
21. C.M. Chen, C. Lee, C.L. Chuang, C.C. Wang, and G. Shieh, "Inferring Genetic Interactions via a Nonlinear Model and an Optimization Algorithm," BMC Systems Biology, vol. 4, no. 1, article no. 16, 2010.
22. R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," J. Royal Statistical Soc. (Series B), vol. 58, pp. 267-288, 1996.
23. H. Zou and T. Hastie, "Regularization and Variable Selection via the Elastic Net," J. Royal Statistical Soc. Series B, vol. 67, no. 2, pp. 301-320, 2005. (Pubitemid 40465877)
24. F. Ferrazzi, P. Sebastiani, M.F. Ramoni, and R. Bellazzi, "Bayesian Approaches to Reverse Engineer Cellular Systems: A Simulation Study on Nonlinear Gaussian Networks," BMC Bioinformatics, vol. 8, Suppl. 5, S2 (epub) 2007.
25. G.-W. Weber, O. Defterli, S.Z. Alparslan Gök, and E. Kropat, "Modeling, Inference and Optimization of Regulatory Networks Based on Time Series Data," European J. Operational Research, vol. 211, pp. 1-14, 2011.
26. R. Albert, "Scale-Free Networks in Cell Biology," J. Cell Science, vol. 118, pp. 4947-4957, 2005. (Pubitemid 41672427)
27. D. Marbach, C. Mattiussi, and D. Floreano, "Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks," Annals of the New York Academy of Sciences, vol. 1158, pp. 234-245, 2009.
28. D. Marbach, C. Mattiussi, and D. Floreano, "Combining Multiple Results of a Reverse Engineering Algorithm: Application to the DREAM Five Gene Network Challenge," Ann. New York Academy of Sciences, vol. 1158, pp. 102-113, 2009.
29. T. Jones and S. Forrest, "Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms," Proc. Sixth Int'l Conf. Genetic Algorithms, pp. 184-192, 1995.
30. T. Jones, "Evolutionary Algorithms, Fitness Landscapes and Search," Working Papers 95-05-048, Santa Fe Inst., May 1995.
31. F. Sambo, M. Montes de Oca, B. Di Camillo, and T. Stü tzle, "On the Difficulty of Inferring Gene Regulatory Networks: A Study of the Fitness Landscape Generated by Relative Squared Error," Proc. Ninth Int'l Conf. Artificial Evolution, P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, and E. Lutton, eds., pp. 74-85, 2010.
32. M.J.D. Powell, "The NEWUOA Software for Unconstrained Optimization," Large-Scale Nonlinear Optimization, ser. Nonconvex Optimization and Its Applications, vol. 83, pp. 255-297, SpringerVerlag, 2006.
33. N. Hansen, "The CMA Evolution Strategy: A Comparing Review," Towards a New Evolutionary Computation, Advances on Estimation of Distribution Algorithms, pp. 75-102, Springer, 2006. (Pubitemid 44856937)
34. B. Di Camillo, G. Toffolo, and C. Cobelli, "A Gene Network Simulator to Assess Reverse Engineering Algorithms," Ann. New York Academy of Sciences, vol. 1158, no. 1, pp. 125-142, 2009.
35. B. Di Camillo, M. Falda, G. Toffolo, and C. Cobelli, "SimBioNeT: A Simulator of Biological Network Topology," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 592-600, Mar./Apr. 2012.
36. J.F. Kolen and S.C. Kremer, A Field Guide to Dynamical Recurrent Networks. IEEE Press, 2001.
37. B.A. Pearlmutter, "Dynamic Recurrent Neural Networks," Technical Report CMU-CS-90-196, Carnegie Mellon Univ., Pittsburgh, PA, 1990.
38. T.T. Vu and J. Vohradsky, "Nonlinear Differential Equation Model for Quantification of Transcriptional Regulation Applied to Microarray Data of Saccharomyces Cerevisiae," Nucleic Acids Research, vol. 35, no. 1, pp. 279-287, Jan. 2007. (Pubitemid 46189852)
39. J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence. Morgan Kaufmann, 2001.
40. E. Fehlberg, "Low-Order Classical Runge-Kutta Formulas with Step Size Control and Their Application to Some Heat Transfer Problems," Technical Report 315, NASA, 1969.
41. F. He, R. Balling, and A.-P. Zeng, "Reverse Engineering and Verification of Gene Networks: Principles, Assumptions, and Limitations of Present Methods and Future Perspectives," J. Biotechnology, vol. 144, no. 3, pp. 190-203, 2009.
42. Y. Wang, Y. Ma, and R.J. Carroll, "Variance Estimation in the Analysis of Microarray Data," J. Royal Statistical Soc.: Series B (Statistical Methodology), vol. 71, no. 2, pp. 425-445, 2009.
43. M. Anderle, S. Roy, H. Lin, C. Becker, and K. Joho, "Quantifying Reproducibility for Differential Proteomics: Noise Analysis for Protein Liquid Chromatography-Mass Spectrometry of Human Serum," Bioinformatics, vol. 20, no. 18, pp. 3575-3582, 2004. (Pubitemid 40136793)
44. N. Noman and I. Iba, "Reverse Engineering Genetic Networks Using Evolutionary Computation," Genome Informatics, vol. 16, no. 2, pp. 205-214, 2005.
45. C. Spieth, R. Worzischek, F. Streichert, J. Supper, N. Speer, and A. Zell, "Comparing Evolutionary Algorithms on the Problem of Network Inference," Proc. Genetic and Evolutionary Computation Conf. (GECCO '06), M. Cattolico, ed., pp. 305-306, 2006. (Pubitemid 44611333)
46. A. Auger, N. Hansen, J.M. Perez Zerpa, R. Ros, and M. Schoenauer, "Empirical Comparisons of Several Derivative Free Optimization Algorithms," Acte du 9ime colloque nat'l en calcul des structures, May 2009.
47. A.R. Conn, N.I.M. Gould, and P.L. Toint, Trust-Region Methods. ser. MPS-SIAM Series in Optimization. SIAM, 2000.
48. H.H. Hoos and T. Stützle, Stochastic Local Search : Foundations & Applications, The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, Sept. 2004.