MEIGO: An open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Egea, Jose A ^a   Henriques, David ^b,c   Cokelaer, Thomas ^c   Villaverde, Alejandro F ^b   MacNamara, Aidan ^c   Danciu, Diana Patricia ^c   Banga, Julio R ^b   Saez Rodriguez, Julio ^c  

^a TECHNICAL UNIVERSITY OF CARTAGENA   (Spain)

^b INSTITUTO DE INVESTIGACIONES MARINAS   (Spain)

^c WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; BIOINFORMATICS; BIOLOGY; GLOBAL OPTIMIZATION; HEURISTIC ALGORITHMS; INFERENCE ENGINES; INTEGER PROGRAMMING; PROBLEM SOLVING;

CONTINUOUS NONLINEAR PROGRAMMING; GLOBAL OPTIMIZATION METHOD; MATLAB OPTIMIZATION TOOLBOX; MIXED-INTEGER PROGRAMMING; OPEN-SOURCE PLATFORMS; OPEN-SOURCE SOFTWARES; STATE-OF-THE-ART METHODS; VARIABLE NEIGHBORHOOD SEARCH;

OPTIMIZATION;

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGY; COMPUTER PROGRAM; METABOLIC ENGINEERING; METHODOLOGY; PROTEOMICS; SYSTEMS BIOLOGY;

ALGORITHMS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; METABOLIC ENGINEERING; PROTEOMICS; SOFTWARE; SYSTEMS BIOLOGY;

EID: 84900846604     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-136     Document Type: Article

References (45)

1. Greenberg HJ, Hart WE, Lancia G. Opportunities for combinatorial optimization in computational biology. Informs J Computing 2004, 16(3):211-231.
2. Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armañanzas R, Santafé G, Pérez A, Robles V. Machine learning in bioinformatics. Brief Bioinform 2006, 7:86-112. 10.1093/bib/bbk007, 16761367.
3. Festa P. On some optimization problems in molecular biology. Math Biosci 2007, 207(2):219-234. 10.1016/j.mbs.2006.11.012, 17512558.
4. Banga JR. Optimization in computational systems biology. BMC Syst Biol 2008, 2:47. 10.1186/1752-0509-2-47, 2435524, 18507829.
5. Sun J, Garibaldi JM, Hodgman C. Parameter estimation using Metaheuristics in systems biology: a comprehensive review. IEEE/ACM Trans Comput Biol Bioinform 2012, 9:185-202.
6. Marchisio M. Stelling J: Computational design tools for synthetic biology. Curr Opin Biotechnol 2009, 20(4):479-85. 10.1016/j.copbio.2009.08.007, 19758796.
7. Banga JR., Balsa-Canto E Parameter estimation and optimal experimental design. Essays Biochem 2008, 45:195. 10.1042/BSE0450195, 18793133, Balsa-Canto E.
8. Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 2003, 13(11):2467-2474. 10.1101/gr.1262503, 403766, 14559783.
9. Ashyraliyev M, Fomekong-Nanfack Y, Kaandorp JA, Blom JG. Systems biology: parameter estimation for biochemical models. FEBS J 2008, 276(4):886-902.
10. Villaverde AF, Egea JA, Banga JR. A cooperative strategy for parameter estimation in large scale systems biology models. BMC Syst Biol 2012, 6:75. 10.1186/1752-0509-6-75, 3512509, 22727112.
11. Terfve C, Cokelaer T, MacNamara A, Henriques D, Goncalves E, Morris MK, van Iersel M, Lauffenburger DA, Saez-Rodriguez J. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol 2012, 6:133+. 10.1186/1752-0509-6-133, 3605281, 23079107.
12. Schmidt H, Jirstrand M. Systems biology toolbox for MATLAB: a computational platform for research in systems biology. Bioinformatics 2006, 22(4):514-515. 10.1093/bioinformatics/bti799, 16317076.
13. Balsa-Canto E, Banga JR. AMIGO, a toolbox for advanced model identification in systems biology using global optimization. Bioinformatics 2011, 27(16):2311-2313. 10.1093/bioinformatics/btr370, 3150037, 21685047.
14. Maiwald T, Eberhardt O, Blumberg J. Mathematical modeling of biochemical systems with PottersWheel. Computational Modeling of Signaling Networks, Series: Methods in Molecular Biology, Volume 880 2012, 119-138. Betterton MD. New York: Humana Press, Liu X.
15. Balsa-Canto E, Alonso A, Banga JR. An iterative identification procedure for dynamic modeling of biochemical networks. BMC Syst Biol 2010, 4:11+. 10.1186/1752-0509-4-11, 2838808, 20163703.
16. Skanda D, Lebiedz D. An optimal experimental design approach to model discrimination in dynamic biochemical systems. Bioinformatics 2010, 26(7):939-945. 10.1093/bioinformatics/btq074, 20176580.
17. Yuraszeck TM, Neveu P, Rodriguez-Fernandez M, Robinson A, Kosik KS, Doyle FJ III. Vulnerabilities in the Tau network and the role of ultrasensitive points in Tau pathophysiology. PLoS Comput Biol 2010, 6(11):e1000997. 10.1371/journal.pcbi.1000997, 2978700, 21085645.
18. Jia G, Stephanopoulos G, Gunawan R. Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method. Bioinformatics 2011, 27(14):1964-1970. 10.1093/bioinformatics/btr293, 21558155.
19. Heldt F, Frensing T, Reichl U. Modeling the intracellular dynamics of influenza virus replication to understand the control of viral RNA synthesis. J Virol 2012, 86(15):7806-7817. 10.1128/JVI.00080-12, 3421648, 22593159.
20. Higuera C, Villaverde AF, Banga JR, Ross J, Morán F. Multi-criteria optimization of regulation in metabolic networks. PLoS ONE 2012, 7(7):e41122. 10.1371/journal.pone.0041122, 3406099, 22848435.
21. Jia G, Stephanopoulos G, Gunawan R. Incremental parameter estimation of kinetic metabolic network models. BMC Syst Biol 2012, 6:142+. 10.1186/1752-0509-6-142, 3568022, 23171810.
22. MacNamara A, Terfve C, Henriques D, Peñalver-Bernabé B, Saez-Rodriguez J. State-time spectrum of signal transduction logic models. Phys Biol 2012, 9(4):045003. 10.1088/1478-3975/9/4/045003, 22871648.
23. Sriram K, Rodriguez-Fernandez M., Doyle FJ III Modeling cortisol dynamics in the neuro-endocrine axis distinguishes normal, depression, and post-traumatic stress disorder (PTSD) in humans. PLoS Comput Biol 2012, 8(2):e1002379. 10.1371/journal.pcbi.1002379, 3280965, 22359492, Doyle FJ III.
24. Sriram K, Rodriguez-Fernandez M, Doyle FJ. A detailed modular analysis of heat-shock protein dynamics under acute and chronic stress and its implication in anxiety disorders. PLoS ONE 2012, 7(8):e42958+. 10.1371/journal.pone.0042958, 3425570, 22937003.
25. Freund S, Rath A, Barradas OP, Skerhutt E, Scholz S, Niklas J, Sandig V, Rose T, Heinzle E, Noll T, Pörtner R, Zeng AP, Reichl U. Batch-to-batch variability of two human designer cell lines - AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model. Eng Life Sci 2013, 00:1-13.
26. Francis F, García MR, Middleton RH. A single compartment model of pacemaking in dissasociated Substantia nigra neurons. J Comput Neurosci 2013, 35(3):295-316. 10.1007/s10827-013-0453-9, 23686304.
27. Egea JA, Schmidt H, Banga JR. A new tool for parameter estimation in nonlinear dynamic biological systems using global optimization. Poster 9th International Conference on Systems Biology, ICSB 2008. Goteborg (Sweden), 22-28 August 2008. Available at http://www.iim.csic.es/gingproc/Poster_ICSB2008.pdf.
28. Glover F, Laguna M., Martí R Fundamentals of scatter search and path relinking. Control Cybernet 2000, 39(3):653-684. Martí R.
29. Storn R, Price K. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optimization 1997, 11(4):341-359.
30. Egea JA, Martí R, Banga JR. An evolutionary method for complex-process optimization. Comput Oper Res 2010, 37(2):315-324.
31. Mladenović N, Hansen P. Variable neighborhood search. Comput Oper Res 1997, 24:1097-1100.
32. Hansen P, Mladenović N, Perez-Brito D. Variable neighborhood decomposition search. J Heuristics 2001, 7:335-350.
33. Eydgahi H, Chen WW, Muhlich JL, Vitkup D, Tsitsiklis JN, Sorger PK. Properties of cell death models calibrated and compared using Bayesian approaches. Mol Syst Biol 2013, 9:644. 3588908, 23385484.
34. Toulouse M, Crainic TG, Sanso B. Systemic behavior of cooperative search algorithms. Parallel Comput 2004, 30:57-79.
35. Hansen P, Mladenović N, Moreno-Pérez JA. Variable neighbourhood search: methods and applications. Ann Oper Res 2010, 175:367-407.
36. Karbowski A, Majchrowski M, Trojanek P. jPar-a simple, free and lightweight tool for parallelizing Matlab calculations on multicores and in clusters. 9th International Workshop on State-of-the-Art in Scientific and Parallel Computing (PARA 2008), May 13-16 2008, Trondheim (Norway).
37. Knaus J Developing parallel programs using snowfall. 2010, [http://cran.r-project.org/web/packages/snowfall/vignettes/snowfall.pdf], Knaus J.
38. Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, Klamt S, Sorger PK. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol 2009, 5:331. 2824489, 19953085.
39. Sandgren E. Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 1990, 112(2):223-229.
40. Harjunkoski I, Westerlund T, Pörn R, Skrifvars H. Different transformations for solving non-convex trim loss problems by MINLP. Eur J Oper Res 1998, 105:594-603.
41. Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 2003, 84(6):647-657. 10.1002/bit.10803, 14595777.
42. Klamt S, Saez-Rodriguez J, Lindquist J, Simeoni L, Gilles ED. A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics 2006, 7:56. 10.1186/1471-2105-7-56, 1458363, 16464248.
43. Mitsos A, Melas IN, Siminelakis P, Chairakaki AD, Saez-Rodriguez J, Alexopoulos LG. Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput Biol 2009, 5(12):e1000591. 10.1371/journal.pcbi.1000591, 2776985, 19997482.
44. Sharan R, Karp RM. Reconstructing boolean models of signaling. Proceedings of the 16th Annual International Conference on Research in Computational Molecular Biology, RECOMB'12 2012, 261-271. Berlin, Heidelberg: Springer-Verlag.
45. Guziolowski C, Videla S, Eduati F, Thiele S, Cokelaer T, Siegel A, Saez-Rodriguez J. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming. Bioinformatics 2013, 29(18):2320-2326. 10.1093/bioinformatics/btt393, 3753570, 23853063.