Parameter asymmetry and time-scale separation in core genetic commitment circuits

Hongguang Xi , Marc Turcotte

Quant. Biol. ›› 2015, Vol. 3 ›› Issue (1) : 19 -45.

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Quant. Biol. ›› 2015, Vol. 3 ›› Issue (1) : 19 -45. DOI: 10.1007/s40484-015-0042-1
RESEARCH ARTICLE
RESEARCH ARTICLE

Parameter asymmetry and time-scale separation in core genetic commitment circuits

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Abstract

Theory allows studying why Evolution might select core genetic commitment circuit topologies over alternatives. The nonlinear dynamics of the underlying gene regulation together with the unescapable subtle interplay of intrinsic biochemical noise impact the range of possible evolutionary choices. The question of why certain genetic regulation circuits might present robustness to phenotype-delivery breaking over others, is therefore of high interest. Here, the behavior of systematically more complex commitment circuits is studied, in the presence of intrinsic noise, with a focus on two aspects relevant to biology: parameter asymmetry and time-scale separation. We show that phenotype delivery is broken in simple two- and three-gene circuits. In the two-gene circuit, we show how stochastic potential wells of different depths break commitment. In the three-gene circuit, we show that the onset of oscillations breaks the commitment phenotype in a systematic way. Finally, we also show that higher dimensional circuits (four-gene and five-gene circuits) may be intrinsically more robust.

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systems biology / theoretical biology / gene regulation / nonlinear dynamics / stochasticity

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Hongguang Xi, Marc Turcotte. Parameter asymmetry and time-scale separation in core genetic commitment circuits. Quant. Biol., 2015, 3(1): 19-45 DOI:10.1007/s40484-015-0042-1

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References

[1]

Waddington, C. H. (1957) The Strategy of the Genes. London: Routledge

[2]

Ferrell, J. E. Jr. (2012) Bistability, bifurcations, and Waddington’s epigenetic landscape. Curr. Biol., 22, R458–R466

[3]

Strogatz, S. H. (1994) Nonlinear Dynamics and Chaos. Cambridge: Perseus Books Publishing

[4]

Jaeger, J., Monk, N. (2014) Bioattractors: Dynamical systems theory and the evolution of regulatory processes. J. Physiol., 592, 2267–2281

[5]

Çağatay, T., Turcotte, M., Elowitz, M. B., Garcia-Ojalvo, J. and Süel, G. M. (2009) Architecture-dependent noise discriminates functionally analogous differentiation circuits. Cell, 139, 512–522

[6]

Elowitz, M. B., Levine, A. J., Siggia, E. D. and Swain, P. S. (2002) Stochastic gene expression in a single cell. Science, 297, 1183–1186

[7]

Süel, G. M., Garcia-Ojalvo, J., Liberman, L. M. and Elowitz, M. B. (2006) An excitable gene regulatory circuit induces transient cellular differentiation. Nature, 440, 545–550

[8]

Süel, G. M., Kulkarni, R. P., Dworkin, J., Garcia-Ojalvo, J. and Elowitz, M. B. (2007) Tunability and noise dependence in differentiation dynamics. Science, 315, 1716–1719

[9]

Thattai, M. and van Oudenaarden, A. (2004) Stochastic gene expression in fluctuating environments. Genetics, 167, 523–530

[10]

Turcotte, M., Garcia-Ojalvo, J. and Süel, G. M. (2008) A genetic timer through noise-induced stabilization of an unstable state. Proc. Natl. Acad. Sci. USA, 105, 15732–15737

[11]

Xi, H., Duan, L. and Turcotte, M. (2013) Point-cycle bistability and stochasticity in a regulatory circuit for Bacillus subtilis competence. Math. Biosci., 244, 135–147

[12]

Xi, H., Yang, Z. and Turcotte, M. (2013) Subtle interplay of stochasticity and deterministic dynamics pervades an evolutionary plausible genetic circuit for Bacillus subtilis competence. Math. Biosci., 246, 148–163

[13]

Li, C., Wang, E. and Wang, J. (2011) Landscape and flux decomposition for exploring global natures of non-equilibrium dynamical systems under intrinsic statistical fluctuations. Chem. Phys. Lett., 505, 75–80

[14]

Li, C., Wang, E. and Wang, J. (2011) Landscape, flux, correlation, resonance, coherence, stability, and key network wirings of stochastic circadian oscillation. Biophys. J., 101, 1335–1344

[15]

Li, C., Wang, E. and Wang, J. (2012) Landscape topography determines global stability and robustness of a metabolic network. ACS Synth Biol, 1, 229–239

[16]

Li, C. and Wang, J. (2013) Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation. J. R. Soc. Interface, 10, 20130787

[17]

Li, C. and Wang, J. (2014) Landscape and flux reveal a new global view and physical quantification of mammalian cell cycle. Proc. Natl. Acad. Sci. USA, 111, 14130–14135

[18]

Li, C. and Wang, J. (2014) Quantifying the underlying landscape and paths of cancer. J. R. Soc. Interface, 11, 20140774

[19]

Wang, J., Zhang, K., Xu, L. and Wang, E. (2011) Quantifying the Waddington landscape and biological paths for development and differentiation. Proc. Natl. Acad. Sci. USA, 108, 8257–8262

[20]

Wu, W. and Wang, J. (2013) Landscape framework and global stability for stochastic reaction diffusion and general spatially extended systems with intrinsic fluctuations. J. Phys. Chem. B, 117, 12908–12934

[21]

Wu, W. and Wang, J. (2013) Potential and flux field landscape theory. I. Global stability and dynamics of spatially dependent non-equilibrium systems. J. Chem. Phys., 139, 121920

[22]

Xu, L., Zhang, F., Zhang, K., Wang, E. and Wang, J. (2014) The potential and flux landscape theory of ecology. PLoS One, 9, e86746

[23]

Zhang F., Xu L., Zhang K., Wang E., Wang J., (2012) The potential and flux landscape theory of evolution. J. Chem. Phys., 137, 065102

[24]

Beard, D. A. D., Babson, E., Curtis, E. and Qian, H. (2004) Thermodynamic constraints for biochemical networks. J. Theor. Biol., 228, 327–333

[25]

Beard, D. A. and Qian H. (2008) Chemical Biophysics, Cambridge: Cambridge University Press

[26]

Qian, H. and Cooper, J. A. (2008) Temporal cooperativity and sensitivity amplification in biological signal transduction. Biochemistry, 47, 2211–2220

[27]

Qian, H. (2007) Phosphorylation energy hypothesis: open chemical systems and their biological functions. Annu. Rev. Phys. Chem., 58, 113–142

[28]

Qian, H. and Beard, D. A. (2005) Thermodynamics of stoichiometric biochemical networks in living systems far from equilibrium. Biophys. Chem., 114, 213–220

[29]

Qian, H., Beard, D. A. and Liang, S. D. (2003) Stoichiometric network theory for nonequilibrium biochemical systems. Eur. J. Biochem., 270, 415–421

[30]

Ma, W., Trusina, A., El-Samad, H., Lim, W. A. and Tang, C. (2009) Defining network topologies that can achieve biochemical adaptation. Cell, 138, 760–773

[31]

Zhang, J., Yuan, Z., Li, H. X. and Zhou, T. (2010) Architecture-dependent robustness and bistability in a class of genetic circuits. Biophys. J., 99, 1034–1042

[32]

Snoussi, E. H. (1998) Necessary Conditions for Multistationarity and Stable Periodicity. J. Biol. Syst., 06, 3–9

[33]

Gardner, T. S. and Faith, J. J. (2005) Reverse-engineering transcription control networks. Phys. Life Rev., 2, 65–88

[34]

Chickarmane, V., Troein, C., Nuber, U. A., Sauro, H. M. and Peterson, C. (2006) Transcriptional dynamics of the embryonic stem cell switch. PLoS Comput. Biol., 2, e123

[35]

Gillespie, D. T. (1976) A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions. J. Comput. Phys., 22, 403–434

[36]

Gillespie, D. T. (1977) Exact Stochastic Simulation of Coupled Chemical Reactions. J. Phys. Chem., 81, 2340–2361

[37]

Gillespie Markov Processes, D. T.An Introduction for Physical Scientists, Academic Press, 1991

[38]

Gillespie, D. T. (2007) Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem., 58, 35–55

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