Abstract
Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a ‘top-down’ approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.
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References
Alon U (2006) An introduction to systems biology: design principles of biological circuits. CRC press, Florida
Bar-Joseph Z, Gitter A, Simon I (2012) Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet 13:552–564. doi:10.1038/nrg3244
Cao H, Romero-Campero FJ, Heeb S, Cámara M, Krasnogor N (2010) Evolving cell models for systems and synthetic biology. Syst Synth Biol 4:55–84. doi:10.1007/s11693-009-9050-7
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103. doi:10.1089/10665270252833208
Drennan B, Beer R (2006) Evolution of repressilators using a biologically-motivated model of gene expression. The MIT Press (Bradford Books), Cambridge
Dunlop MJ (2011) Engineering microbes for tolerance to next-generation biofuels. Biotechnol Biofuels 4:32. doi:10.1186/1754-6834-4-32
Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403:335–338. doi:10.1038/35002125
François P, Hakim V (2004) Design of genetic networks with specified functions by evolution in silico. Proc Natl Acad Sci USA 101:580–585. doi:10.1073/pnas.0304532101
François P, Hakim V (2005) Core genetic module: the mixed feedback loop. Phys Rev E 72(3):031908
Gilbert ES, Walker AW, Keasling JD (2003) A constructed microbial consortium for biodegradation of the organophosphorus insecticide parathion. Appl Microbiol Biotechnol 61:77–81. doi:10.1007/s00253-002-1203-5
Hansen N (2006) The CMA evolution strategy: a comparing review, vol 192. Springer, Berlin. doi:10.1007/3-540-32494-1_4
Khalil AS, Collins JJ (2010) Synthetic biology: applications come of age. Nat Rev Genet 11:367–379. doi:10.1038/nrg2775
Kim J-R, Yoon Y, Cho K-H (2008) Coupled feedback loops form dynamic motifs of cellular networks. Biophys J 94:359–365. doi:10.1529/biophysj.107.105106
Komiya K, Noman N, Iba H (2012) The search for robust topologies of oscillatory gene regulatory networks by evolutionary computation. ACM Press, New York. doi:10.1145/2330784.2330963
Levine JH, Lin Y, Elowitz MB (2013) Functional roles of pulsing in genetic circuits. Science 342:1193–1200. doi:10.1126/science.1239999
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. doi:10.1073/pnas.0913357107
Marbach D et al (2012) Wisdom of crowds for robust gene network inference. Nat Meth 9:796–804
Mukherji S, van Oudenaarden A (2009) Synthetic biology: understanding biological design from synthetic circuits. Nat Rev Genet 10:859–871. doi:10.1038/nrg2697
Noman N, Palafox L, Iba H (2013a) Evolving genetic networks for synthetic biology. New Gener Comput 31:71–88. doi:10.1007/s00354-013-0201-8
Noman N, Palafox L, Iba H (2013b) Reconstruction of gene regulatory networks from gene expression data using decoupled recurrent neural network model, vol 6. Springer, Berlin. doi:10.1007/978-4-431-54394-7_8
Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin
Purvis JE, Lahav G (2013) Encoding and decoding cellular information through signaling dynamics. Cell 152:945–956. doi:10.1016/j.cell.2013.02.005
Rajendran M, Ellington AD (2008) Selection of fluorescent aptamer beacons that light up in the presence of zinc. Anal Bioanal Chem 390:1067–1075. doi:10.1007/s00216-007-1735-8
Ruskin HJ, Crane M (2011) Stages of gene regulatory network inference: the evolutionary algorithm role. InTech, Rijeka
Sheinman M, Kafri Y (2012) How does the DNA sequence affect the Hill curve of transcriptional response? Phys Biol 9(5):056006
Smolen P, Baxter DA, Byrne JH (2000) Mathematical modeling of gene networks. Neuron 26:567–580. doi:10.1016/S0896-6273(00)81194-0
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359. doi:10.1023/A:1008202821328
Thomas SA, Jin Y (2012) Combining genetic oscillators and switches using evolutionary algorithms. IEEE, New York. doi:10.1109/CIBCB.2012.6217207
van Dorp M, Lannoo B, Carlon E (2013a) Evolutionary generation of small oscillating genetic networks, vol 7824. Springer, Berlin. doi:10.1007/978-3-642-37213-1_13
van Dorp M, Lannoo B, Carlon E (2013b) Generation of oscillating gene regulatory network motifs. Phys Rev E 88:012722. doi:10.1103/PhysRevE.88.012722
Wang L, Walker BL, Iannaccone S, Bhatt D, Kennedy PJ, Tse WT (2009) Bistable switches control memory and plasticity in cellular differentiation. Proc Natl Acad Sci. doi:10.1073/pnas.0806137106
Widder S, Macía J, Solé R (2009) Monomeric bistability and the role of autoloops in gene regulation. PLoS ONE 4:e5399. doi:10.1371/journal.pone.0005399
Acknowledgments
We thank J. Lindle for contributions to the initial development of the project and M. Dunlop for helpful comments on the manuscript.
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Garcia-Bernardo, J., Eppstein, M.J. Evolving modular genetic regulatory networks with a recursive, top-down approach. Syst Synth Biol 9, 179–189 (2015). https://doi.org/10.1007/s11693-015-9179-5
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DOI: https://doi.org/10.1007/s11693-015-9179-5