A qualitative analysis of ubiquitous regulatory motifs in Saccharomyces cerevisiae genetic networks. (English) Zbl 07263948

Summary: This work examines bistability and multistability within a Recurrent Neural Network model (RNN) for a 2-node and 3-node system under many different regulation scenarios. We determine parameter regions where there is bistability, multistability, or other stable modes in the expression states of the systems described by this network model. Our results show that although bistability can be generated with autoregulation it is also the case that both autorepression or no autoregulation can yield bistability as long as a sigmoidal behavior is present. Additionally, our results show the importance of considering more than a single connection when inferring a network as the observed biological result is averaged over many outcomes, which has implications for many algorithms that infer gene regulatory networks using the RNN models.


92Dxx Genetics and population dynamics
92Cxx Physiological, cellular and medical topics
93Bxx Controllability, observability, and system structure
62Pxx Applications of statistics


Full Text: DOI


[1] Apgar, J. F.; Witmer, D. K.; White, F. M.; Tidor, B., Sloppy models, parameter uncertainty, and the role of experimental design, Mol Biosyst, 6, 1890-1900 (2010)
[2] Bandyopadhyay, S.; et al., Rewiring of genetic networks in response to dna damage, Science, 330, 1385-1389 (2010)
[3] Blasi, M. F.; Casorelli, I.; Colosimo, A.; Blasi, F. S.; Bignami, M.; Giuliania, A., A recursive network approach can identify constitutive regulatory circuits in gene expression data, Physica A, 348, 349-370 (2005)
[4] Casey, F. P.; Baird, D.; Feng, Q.; Gutenkunst, R. N.; Waterfall, J. J.; Myers, C. R., Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model, IET Syst Biol, 1, 3, 190-202 (2007)
[5] Chou, I.; Voit, E. O., Recent developments in parameter estimation and structure identification of biochemical and genomic systems, Math Biosci, 219, 57-83 (2009) · Zbl 1168.92019
[6] Dhooge, A.; Govaerts, W.; Kuznetsov, Y. A.; Mestrom, W.; Riet, A.; Sautois, B., Matcont and cl matcont: continuation toolboxes in matlab (2006), Universiteit Gent, Belgium and Utrecht University: Universiteit Gent, Belgium and Utrecht University The Netherlands
[7] Dhooge, A.; Govaerts, W.; Kuznetsov, Y. A., Matcont: a Matlab package for numerical bifurcation analysis of odes, ACM Trans Math Softw (TOMS), 29, 2, 141-164 (2003) · Zbl 1070.65574
[8] Engl, H. W.; Flamm, C.; Kügler, P.; Lu, J.; Müller, S.; Schuster, P., Inverse problems in system biology, Inverse Probl, 25, 123014 (2009) · Zbl 1193.34001
[9] Garcıa-Martınez, J.; Aranda, A.; Pérez-Ortın, J. E., Genomic run-on evaluates transcription rates for all yeast genes and identifies gene regulatory mechanisms, Mol Cell, 15, 2, 303-313 (2004)
[10] Gardner, T.; Cantor, C.; Collins, J., Construction of a genetic toggle switch in escherichia coli, Nature, 403, 339-342 (2000)
[11] Grigull, J.; Mnaimneh, S.; Pootoolal, J.; Robinson, M. D.; Hughes, T. R., Genome-wide analysis of mrna stability using transcription inhibitors and microarrays reveals posttranscriptional control of ribosome biogenesis factors, Mol Cell Biol, 24, 12, 5534-5547 (2004)
[12] Guantes, R.; Poyatos, J. F., Multistable decision switches for flexible control of epigenetic differentiation, PLoS Comput Biol, 4, e1000235 (2008)
[13] Gutenkunst, R. N.; Waterfall, J. J.; Casey, F. P.; Brown, K. S.; Myers, C. R.; Sethna, J. P., Universally sloppy parameter sensitivities in systems biology models, PLoS Comput Biol, 3, e189 (2007)
[14] Holstege, F. C.; Jennings, E. G.; Wyrick, J. J.; Lee, T. I.; Hengartner, C. J.; Green, M. R., Dissecting the regulatory circuitry of a eukaryotic genome, Cell, 95, 5, 717-728 (1998)
[15] Ingram, P. J.; Stumpf, M. P.; Stark, J., Network motifs: structure does not determine function, BMC Genom, 7, 108 (2006)
[16] Jeong, J.; Berman, P., On cycles in the transcription network of saccharomyces cerevisiae, BMC Syst Biol, 2, 12 (2008)
[17] Kikuchi, S.; Tominaga, D.; Arita, M.; Takahashi, K.; Tomita, M., Dynamic modeling of genetic networks using genetic algorithm and s-system, Bioinformatics, 19, 643-650 (2003)
[18] Kolda, T. G.; Lewis, R. M.; Torczon, V., Optimization by direct search: new perspectives on some classical and modern methods, SIAM Rev, 45, 3, 385-482 (2003) · Zbl 1059.90146
[19] Lamb, T.; Mitchell, A., The transcription factor rim101p governs ion tolerance and cell differentiation by direct repression of the regulatory genes nrg1 and smp1 in saccharomyces cerevisiae, Mol Cell Biol, 677-686 (2003)
[20] Lee, T. I.; Rinaldi, N. J.; Robert, F.; Odom, D. T.; Bar-Joseph, Z.; Gerber, G. K., Transcriptional regulatory networks in saccharomyces cerevisiae, Science, 298, 5594, 799-804 (2002)
[21] Lee, W.-P.; Tzou, W.-S., Computational methods for discovering gene networks from expression data, Brief Bioinformatics, 10, 408-423 (2009)
[22] Lee, W.-P.; Yang, K.-C., Applying intelligent computing techniques to modeling biological networks from expression data, Geno Prot Bioinfo, 6, 111-120 (2008)
[23] Lee, W.-P.; Yang, K.-C., A clustering-based approach for inferring recurrent neural networks as gene regulatory networks, Neurocomputing, 71, 600-610 (2008)
[24] Liu, L.-Z.; Wu, F.-X.; Zhang, W.-J., Inference of biological s-system using the separable estimation method and the genetic algorithm, IEEE/ACM Trans Comput Biol Bioinform (TCBB), 9, 955-965 (2012)
[25] Lubeck, E.; Long, C., Single-cell systems biology by super-resolution imaging and combinatorial labeling, Nat Methods, 9, 743-750 (2012)
[26] Miller, C.; Schwalb, B.; Maier, K.; Schulz, D.; Dümcke, S.; Zacher, B., Dynamic transcriptome analysis measures rates of mrna synthesis and decay in yeast, Mol Syst Biol, 7, 1 (2011)
[27] Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U., Network motifs: simple building blocks of complex networks, Science, 298, 5594, 824-827 (2002)
[28] Monod, J.; Jacob, F., General conclusions: teleonomic mechanisms in cellular metabolism, growth, and differentiation, Cold Spring Harb Symp Quant Biol, 26, 389-401 (1961)
[29] Oltvai, Z. N.; Barabasi, A.-L., Life’s complexity pyramid, Science, 298, 763-764 (2002)
[30] Ozbudak, E.; Thattai, M.; Lim, N.; Shraiman, B.; van Oudenaarden, A., Multistability in the lactose utilization network of escherichia coli, Nature, 427, 737-740 (2004)
[31] Pan, X.; Heitman, J., Sok2 regulates yeast pseudohyphal differentiation via a transcription factor cascade that regulates cell-cell adhesion, Mol Cell Biol, 20, 83648372 (2000)
[32] Savageau, M., Biochemical system analysis: a study of function and design in molecular biology (1976), Addison-Wesley
[33] Stathopoulos, A.; Cyert, M., Calcineurin acts through the crz1/tcn1-encoded transcription factor to regulate gene expression in yeast, Genes Dev, 11, 3432-3444 (1997)
[34] Swain, M. T.; Mandel, J. J.; Dubitzky, W., Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks, BMC Bioinform, 11, 459 (2010)
[35] Tan, C.; Marguet, P.; You, L., Emergent bistability by a growth-modulating positive feedback circuit, Nat Chem Biol, 5, 11, 842-848 (2009)
[36] To, C. C.; Vohradsky, J., Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network, FASEB J Res Commun, 24, 3468-3478 (2010)
[37] Vachova, L.; Devaux, F.; Kucerova, H.; Ricicova, M.; Jacq, C.; Palkova, Z., Sok2p transcription factor is involved in adaptive program relevant for long term survival of saccharomyces cerevisiae colonies, J Biol Chem, 279, 37973-37981 (2004)
[38] Ventura, A. C.; Jiang, P.; Van Wassenhove, L.; Del Vecchio, D.; Merajver, S. D.; Ninfa, A. J., Signaling properties of a covalent modification cycle are altered by a downstream target, Proc Natl Acad Sci, 107, 22, 10032-10037 (2010)
[39] Vohradska, E.; Vohradsky, J., Virtual mutagenesis of the yeast cyclins genetic network reveals complex dynamics of transcriptional control networks, PLoS ONE, 6, e18827 (2011)
[40] Vohradsky, J., Neural network model of gene expression, FASEB J, 15, 846-854 (2001)
[41] Vu, T. T.; Vohradsky, J., Genexp - a genetic network simulation enviroment, Bioinform Appl Note, 18, 1400-1401 (2002)
[42] Vu, T. T.; Vohradsky, J., Inference of active transcriptional networks by integration of gene expression kinetics modeling and multisource data, Genomics, 93, 426-433 (2009)
[43] Vu, T.; Vohradsky, J., Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of saccharomyces cerevisiae, Nucleic Acids Res, 35, 279-287 (2007)
[44] Wang, Y.; Liu, C. L.; Storey, J. D.; Tibshirani, R. J.; Herschlag, D.; Brown, P. O., Precision and functional specificity in mrna decay, Proc Natl Acad Sci, 99, 9, 5860-5865 (2002)
[45] Yoshimoto, H.; Saltsman, K.; Gasch, A.; Li, H.; Ogawa, N.; Botstein, D., Genome-wide analysis of gene expression regulated by the calcineurin/crz1p signaling pathway in saccharomyces cerevisiae, J Biol Chem, 277, 31079-31088 (2002)
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