Implementation Considerations, Not Topological Differences, Are the Main Determinants of Noise Suppression Properties in Feedback and Incoherent Feedforward Circuits

PLoS Computational Biology

Volumn 12, Issue 6, 2016, Pages

  Buzi, Gentian ^a   Khammash, Mustafa ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

TIMING CIRCUITS; TOPOLOGY;

CELLULAR ENVIRONMENT; CONTROL-THEORETIC APPROACH; FEED-FORWARD ARCHITECTURES; FEED-FORWARD TOPOLOGY; FEEDBACK TOPOLOGY; FEEDFORWARD CIRCUITS; NOISE SUPPRESSION; PROPERTY; SYSTEM USE; UNCERTAINTY;

FEEDBACK;

CHEMICAL REACTION; EXPERIMENTAL STUDY; NOISE REDUCTION; SPECIES; STOCHASTIC MODEL; THEORETICAL MODEL; UNCERTAINTY; ANIMAL; BIOLOGICAL MODEL; CELL FUNCTION; COMPUTER SIMULATION; FEEDBACK SYSTEM; HUMAN; METABOLISM; PHYSIOLOGY; SIGNAL NOISE RATIO; SIGNAL TRANSDUCTION; STATISTICAL MODEL;

PROTEOME;

ANIMALS; CELL PHYSIOLOGICAL PHENOMENA; COMPUTER SIMULATION; FEEDBACK, PHYSIOLOGICAL; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PROTEOME; SIGNAL TRANSDUCTION; SIGNAL-TO-NOISE RATIO;

EID: 84978827605     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004958     Document Type: Article

References (25)

1. Fraser HB, Hirsh AE, Giaever G, Kumm J, Eisen MB, Noise Minimization in Eukaryotic Gene Expression. PLoS Biol. 200404;2(6):e137. doi: 10.1371/journal.pbio.002013715124029
2. Shen-Orr SS, Milo R, Mangan S, Alon U, Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics. 2002;31(1):64–68. doi: 10.1038/ng88111967538
3. Shoval O, Goentoro L, Hart Y, Mayo A, Sontag E, Alon U, Fold-change detection and scalar symmetry of sensory input fields. Proceedings of the National Academy of Sciences. 2010;107(36):15995–16000. Available from: http://www.pnas.org/content/107/36/15995.abstract. doi: 10.1073/pnas.1002352107
4. Tsang J, Zhu J, van Oudenaarden A, MicroRNA-mediated Feedback and Feedforward Loops are Recurrent Network Motifs in Mammals. Molecular Cell. 2007;26(5):753–767. doi: 10.1016/j.molcel.2007.05.01817560377
5. Strovas TJ, Rosenberg AB, Kuypers BE, Muscat RA, Seelig G, MicroRNA-Based Single-Gene Circuits Buffer Protein Synthesis Rates against Perturbations. Acs Synthetic Biology. 2014;3(5):324–331. doi: 10.1021/sb400186724847681
6. Bleris L, Xie Z, Glass D, Adadey A, Sontag E, Benenson Y, Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Molecular Systems Biology. 2011;7. doi: 10.1038/msb.2011.4921811230
7. Osella M, Bosia C, Cora D, Caselle M, The Role of Incoherent MicroRNA-Mediated Feedforward Loops in Noise Buffering. Plos Computational Biology. 2011;7(3). doi: 10.1371/journal.pcbi.100110121423718
8. Becskei A, Serrano L, Engineering stability in gene networks by autoregulation. Nature. 2000;405:590–593. Available from: http://www.nature.com/nature/journal/v405/n6786/abs/405590a0.html. doi: 10.1038/3501465110850721
9. Singh A, Hespanha JP, Optimal feedback strength for noise suppression in autoregulatory gene networks. Biophysical Journal. 2009May;96(10):4013–4023. doi: 10.1016/j.bpj.2009.02.06419450473
10. Shimoga V, White JT, Li Y, Sontag E, Bleris L, Synthetic mammalian transgene negative autoregulation. Molecular Systems Biology. 2013;9(1). doi: 10.1038/msb.2013.2723736683
11. Nevozhay D, Adams RM, Murphy KF, Josic K, Balazsi G, Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(13):5123–5128. doi: 10.1073/pnas.080990110619279212
12. Voliotis M, Bowsher CG, The magnitude and colour of noise in genetic negative feedback systems. Nucleic Acids Research. 2012;40(15):7084–7095. doi: 10.1093/nar/gks38522581772
13. Hermsen R, Okano H, You C, Werner N, Hwa T, A growth-rate composition formula for the growth of E.~coli on co-utilized carbon substrates. Molecular Systems Biology. 2015;11(4). doi: 10.15252/msb.2014553725862745
14. Sontag ED, Remarks on feedforward circuits, adaptation, and pulse memory. Systems Biology, IET. 2010;4(1):39–51. doi: 10.1049/iet-syb.2008.0171
15. Sontag ED, Adaptation and regulation with signal detection implies internal model. Systems & Control Letters. 2003;50(2):119–126. doi: 10.1016/S0167-6911(03)00136-1
16. Buzi G, Lander AD, Khammash M, Cell lineage branching as a strategy for proliferative control. BMC biology. 2015;13(1):13. doi: 10.1186/s12915-015-0122-825857410
17. Chandra FA, Buzi G, Doyle JC, Glycolytic Oscillations and Limits on Robust Efficiency. Science. 2011;333(6039):187–192. Available from: http://www.sciencemag.org/content/333/6039/187.abstract. doi: 10.1126/science.120070521737735
18. Hersen P, McClean MN, Mahadevan L, Ramanathan S, Signal processing by the HOG MAP kinase pathway. Proceedings of the National Academy of Sciences. 2008;105(20):7165–7170. doi: 10.1073/pnas.0710770105
19. Kwakernaak H, Sivan R, Linear optimal control systems. Wiley-interscienceNew York. 1972;
20. Munsky B, Khammash M, The finite state projection algorithm for the solution of the chemical master equation. Journal of Chemical Physics. 2006;124(4). doi: 10.1063/1.214588216460146
21. Hespanha J. Moment closure for biochemical networks. In: Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on; 2008. p. 142–147.
22. Singh A, Hespanha JP, Approximate Moment Dynamics for Chemically Reacting Systems. IEEE Trans on Automat Contr. 2011Feb;56(2):414–418. doi: 10.1109/TAC.2010.2088631
23. Elf J, Ehrenberg M, Fast evaluation of fluctuations in biochemical networks with the linear noise approximation. Genome Research. 2003;13:2475–2484. doi: 10.1101/gr.119650314597656
24. Gillespie DT, A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys. 1976;22(4):403–434. doi: 10.1016/0021-9991(76)90041-3
25. Sheppard PW, Rathinam M, Khammash M, SPSens: a software package for stochastic parameter sensitivity analysis of biochemical reaction networks. Bioinformatics. 2013;29(1):140–142. Available from: http://bioinformatics.oxfordjournals.org/content/29/1/140.abstract. doi: 10.1093/bioinformatics/bts64223104889