Please wait a minute...
Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

Postal Subscription Code 80-971

Quant. Biol.    2019, Vol. 7 Issue (2) : 110-121    https://doi.org/10.1007/s40484-018-0162-5
RESEARCH ARTICLE
Regulation by competition: a hidden layer of gene regulatory network
Lei Wei1,2, Ye Yuan1,2, Tao Hu1,2, Shuailin Li3, Tianrun Cheng1,2, Jinzhi Lei4, Zhen Xie1,2, Michael Q. Zhang1,2,5,6, Xiaowo Wang1,2()
1. Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
2. Beijing National Research Center for Information Science and Technology, Beijing 100084, China
3. School of Life Sciences, Tsinghua University, Beijing 100084, China
4. Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing 100084, China
5. Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
6. Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
 Download: PDF(1428 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Background: Molecular competition brings about trade-offs of shared limited resources among the cellular components, and thus introduces a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. Several molecular competition scenarios have been observed recently, but there is still a lack of systematic quantitative understanding to reveal the essence of molecular competition.

Methods: Here, by abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically integrate experimental evidences in these processes and analyzed general properties shared behind them from steady-state behavior to dynamic responses.

Results: We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets.

Conclusions: This work uncovered the complexity and generality of molecular competition effect as a hidden layer of gene regulatory network, and therefore provided a unified insight and a theoretical framework to understand and employ competition in both natural and synthetic systems.

Keywords systems biology      computational modeling      molecular competition regulation      synthetic biology      network motif     
Corresponding Author(s): Xiaowo Wang   
Online First Date: 25 January 2019    Issue Date: 30 May 2019
 Cite this article:   
Lei Wei,Ye Yuan,Tao Hu, et al. Regulation by competition: a hidden layer of gene regulatory network[J]. Quant. Biol., 2019, 7(2): 110-121.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-018-0162-5
https://academic.hep.com.cn/qb/EN/Y2019/V7/I2/110
Fig.1  The coarse-gained competition motif model.
Fig.2  Steady state behaviors of competition systems.
Fig.3  Dynamic properties of competition systems.
Fig.4  Regulator allocation for multi-target competition.
Regulation between targets Influences on regulator-target response
Steady-state behavior Threshold behavior
Negative linear dependence
Regulator allocation
Shaping dose-response curves
Dynamic responses Correlated fluctuation Response time modulation
Noise modification
Tab.1  Properties of regulation by competition
1 G.Hardin, (1960) The competitive exclusion principle. Science, 131, 1292–1297
https://doi.org/10.1126/science.131.3409.1292. pmid: 14399717
2 T. W.Schoener, (1983) Field experiments on interspecific competition. Am. Nat., 122, 240–285
https://doi.org/10.1086/284133.
3 J. H.Connell, (1983) On the prevalence and relative importance of interspecific competition: evidence from field experiments. Am. Nat., 122, 661–696
https://doi.org/10.1086/284165.
4 M. H.Zwietering, , I.Jongenburger, , F. M.Rombouts, and K.van ’t Riet, (1990) Modeling of the bacterial growth curve. Appl. Environ. Microbiol., 56, 1875–1881
pmid: 16348228.
5 D. I.Bolnick, (2004) Can intraspecific competition drive disruptive selection? An experimental test in natural populations of sticklebacks. Evolution, 58, 608–618
https://doi.org/10.1111/j.0014-3820.2004.tb01683.x. pmid: 15119444
6 R.Svanbäck, and D. I.Bolnick, (2007) Intraspecific competition drives increased resource use diversity within a natural population. Proc. Biol. Sci., 274, 839–844
https://doi.org/10.1098/rspb.2006.0198. pmid: 17251094
7 A.Khare, and G. Shaulsky, (2006) First among equals: competition between genetically identical cells. Nat. Rev. Genet., 7, 577–583
https://doi.org/10.1038/nrg1875. pmid: 16702983
8 L. A.Johnston, (2009) Competitive interactions between cells: death, growth, and geography. Science, 324, 1679–1682
https://doi.org/10.1126/science.1163862. pmid: 19556501
9 A. K.Laird, (1964) Dynamics of tumor growth. Br. J. Cancer, 18, 490–502
https://doi.org/10.1038/bjc.1964.55. pmid: 14219541
10 C. H.Chang, , J. Qiu, , D.O’Sullivan, , M. D.Buck, , T.Noguchi, , J. D.Curtis, , Q.Chen, , M.Gindin, , M. M.Gubin, , G. J.van der Windt, , et al.et al. (2015) Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell, 162, 1229–1241
https://doi.org/10.1016/j.cell.2015.08.016. pmid: 26321679
11 M.Scott, , C. W. Gunderson, , E. M.Mateescu, , Z.Zhang, and T.Hwa, (2010) Interdependence of cell growth and gene expression: origins and consequences. Science, 330, 1099–1102
https://doi.org/10.1126/science.1192588. pmid: 21097934
12 A. Y.Weiße, , D. A.Oyarzún, , V.Danos, and P. S.Swain, (2015) Mechanistic links between cellular trade-offs, gene expression, and growth. Proc. Natl. Acad. Sci. USA, 112, E1038–E1047
https://doi.org/10.1073/pnas.1416533112. pmid: 25695966
13 S.Hui, , J. M. Silverman, , S. S.Chen, , D. W.Erickson, , M.Basan, , J.Wang, , T.Hwa, and J. R.Williamson, (2015) Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol. Syst. Biol., 11, e784
https://doi.org/10.15252/msb.20145697. pmid: 25678603
14 R. C.Brewster, , F. M.Weinert, , H. G.Garcia, , D.Song, , M.Rydenfelt, and R.Phillips, (2014) The transcription factor titration effect dictates level of gene expression. Cell, 156, 1312–1323
https://doi.org/10.1016/j.cell.2014.02.022. pmid: 24612990
15 A. A.Sigova, , B. J.Abraham, , X.Ji, , B. Molinie, , N. M.Hannett, , Y. E.Guo, , M.Jangi, , C. C.Giallourakis, , P. A.Sharp, and R. A.Young, (2015) Transcription factor trapping by RNA in gene regulatory elements. Science, 350, 978–981
https://doi.org/10.1126/science.aad3346. pmid: 26516199
16 Q.Zheng, , C. Bao, , W.Guo, , S.Li, , J. Chen, , B.Chen, , Y.Luo, , D.Lyu, , Y.Li, , G. Shi, , et al.et al. (2016) Circular RNA profiling reveals an abundant circHIPK3 that regulates cell growth by sponging multiple miRNAs. Nat. Commun., 7, 11215
https://doi.org/10.1038/ncomms11215. pmid: 27050392
17 M.Cesana, , D. Cacchiarelli, , I.Legnini, , T.Santini, , O.Sthandier, , M.Chinappi, , A.Tramontano, and I.Bozzoni, (2011) A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell, 147, 358–369
https://doi.org/10.1016/j.cell.2011.09.028. pmid: 22000014
18 P.Sumazin, , X. Yang, , H. S.Chiu, , W. J.Chung, , A.Iyer, , D.Llobet-Navas, , P.Rajbhandari, , M.Bansal, , P.Guarnieri, , J.Silva, , et al.et al. (2011) An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell, 147, 370–381
https://doi.org/10.1016/j.cell.2011.09.041. pmid: 22000015
19 S.Saha, , C. A. Weber, , M.Nousch, , O.Adame-Arana, , C.Hoege, , M. Y.Hein, , E.Osborne-Nishimura, , J.Mahamid, , M.Jahnel, , L.Jawerth, , et al. (2016) Polar positioning of phase-separated liquid compartments in cells regulated by an mRNA competition mechanism. Cell 166, 1572–1584
20 H.-S.Chiu, , S. Somvanshi, , E.Patel, , T.-W.Chen, , V. P.Singh, , B.Zorman, , S. L.Patil, , Y.Pan, , S. S.Chatterjee, , A. K.Sood, , et al.et al. (2018) Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context. Cell Reports, 23, 297–312.e12
https://doi.org/10.1016/j.celrep.2018.03.064. pmid: 29617668
21 S.Cardinale, and A. P.Arkin, (2012) Contextualizing context for synthetic biology‒identifying causes of failure of synthetic biological systems. Biotechnol. J., 7, 856–866
https://doi.org/10.1002/biot.201200085. pmid: 22649052
22 G.Wu, , Q. Yan, , J. A.Jones, , Y. J.Tang, , S. S.Fong, and M. A. G.Koffas, (2016) Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol., 34, 652–664
https://doi.org/10.1016/j.tibtech.2016.02.010. pmid: 26996613
23 Y.Qian, , H. H. Huang, , J. I.Jiménez, and D.Del Vecchio, (2017) Resource competition shapes the response of genetic circuits. ACS Synth. Biol., 6, 1263–1272
https://doi.org/10.1021/acssynbio.6b00361. pmid: 28350160
24 P.Jiang, , A. C. Ventura, , E. D.Sontag, , S. D.Merajver, , A. J.Ninfa, and D.Del Vecchio, (2011) Load-induced modulation of signal transduction networks. Sci. Signal., 4, ra67
https://doi.org/10.1126/scisignal.2002152. pmid: 21990429
25 S.Jayanthi, , K. S. Nilgiriwala, and D.Del Vecchio, (2013) Retroactivity controls the temporal dynamics of gene transcription. ACS Synth. Biol., 2, 431–441
https://doi.org/10.1021/sb300098w. pmid: 23654274
26 U.Ala, , F. A. Karreth, , C.Bosia, , A.Pagnani, , R.Taulli, , V.Léopold, , Y.Tay, , P.Provero, , R.Zecchina, and P. P.Pandolfi, (2013) Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments. Proc. Natl. Acad. Sci. USA, 110, 7154–7159
https://doi.org/10.1073/pnas.1222509110. pmid: 23536298
27 H.-S.Chiu, , M. R. Martínez, , E. V.Komissarova, , D.Llobet-Navas, , M.Bansal, , E. O.Paull, , J.Silva, , X.Yang, , P.Sumazin, and A.Califano, (2018) The number of titrated microRNA species dictates ceRNA regulation. Nucleic Acids Res., 46, 4354–4369
https://doi.org/10.1093/nar/gky286. pmid: 29684207
28 Y.Yuan, , B. Liu, , P.Xie, , M. Q.Zhang, , Y.Li, , Z. Xie, and X.Wang, (2015) Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc. Natl. Acad. Sci. USA, 112, 3158–3163
https://doi.org/10.1073/pnas.1413896112. pmid: 25713348
29 T. E.Gorochowski, , I.Avcilar-Kucukgoze, , R. A.Bovenberg, , J. A.Roubos, and Z.Ignatova, (2016) A minimal model of ribosome allocation dynamics captures trade-offs in expression between endogenous and synthetic genes. ACS Synth. Biol., 5, 710–720
https://doi.org/10.1021/acssynbio.6b00040. pmid: 27112032
30 W. H.Mather, , N. A.Cookson, , J.Hasty, , L. S.Tsimring, and R. J.Williams, (2010) Correlation resonance generated by coupled enzymatic processing. Biophys. J., 99, 3172–3181
https://doi.org/10.1016/j.bpj.2010.09.057. pmid: 21081064
31 N. A.Cookson, , W. H.Mather, , T.Danino, , O.Mondragón-Palomino, , R. J.Williams, , L. S.Tsimring, and J.Hasty, (2014) Queueing up for enzymatic processing: correlated signaling through coupled degradation. Mol. Syst. Biol., 7, 561
https://doi.org/10.1038/msb.2011.94. pmid: 22186735
32 A.Gyorgy, , J. I. Jiménez, , J.Yazbek, , H. H.Huang, , H.Chung, , R.Weiss, and D.Del Vecchio, (2015) Isocost lines describe the cellular economy of genetic circuits. Biophys. J., 109, 639–646
https://doi.org/10.1016/j.bpj.2015.06.034. pmid: 26244745
33 M.Carbonell-Ballestero, , E.Garcia-Ramallo, , R.Montañez, , C.Rodriguez-Caso, and J.Macía, (2016) Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm’s law. Nucleic Acids Res., 44, 496–507
https://doi.org/10.1093/nar/gkv1280. pmid: 26656950
34 N. E.Buchler, and M.Louis, (2008) Molecular titration and ultrasensitivity in regulatory networks. J. Mol. Biol., 384, 1106–1119
https://doi.org/10.1016/j.jmb.2008.09.079. pmid: 18938177
35 S.Mukherji, , M. S. Ebert, , G. X. Y.Zheng, , J. S.Tsang, , P. A.Sharp, and A.van Oudenaarden, (2011) MicroRNAs can generate thresholds in target gene expression. Nat. Genet., 43, 854–859
https://doi.org/10.1038/ng.905. pmid: 21857679
36 T.Quarton, , K. Ehrhardt, , J.Lee, , S.Kannan, , Y.Li, , L. Ma, , & L.Bleris, (2018) Mapping the operational landscape of microRNAs in synthetic gene circuits. NPJ Syst. Biol. Appl.,
https://doi.org/10.1038/s41540-017-0043-y
37 T. H.Lee, and N.Maheshri, (2012) A regulatory role for repeated decoy transcription factor binding sites in target gene expression. Mol. Syst. Biol., 8, 576
https://doi.org/10.1038/msb.2012.7. pmid: 22453733
38 M.Jens, and N. Rajewsky, (2015) Competition between target sites of regulators shapes post-transcriptional gene regulation. Nat. Rev. Genet., 16, 113–126
https://doi.org/10.1038/nrg3853. pmid: 25488579
39 Y.Yuan, , X. Ren, , Z.Xie, and X.Wang, (2016) A quantitative understanding of microRNA-mediated competing endogenous RNA regulation. Quant. Biol., 4, 47–57
https://doi.org/10.1007/s40484-016-0062-5.
40 D.Mishra, , P. M. Rivera, , A.Lin, , D.Del Vecchio, and R.Weiss, (2014) A load driver device for engineering modularity in biological networks. Nat. Biotechnol., 32, 1268–1275
https://doi.org/10.1038/nbt.3044. pmid: 25419739
41 A.Burger, , A. M. Walczak, and P. G.Wolynes, (2010) Abduction and asylum in the lives of transcription factors. Proc. Natl. Acad. Sci. USA, 107, 4016–4021
https://doi.org/10.1073/pnas.0915138107. pmid: 20160109
42 W. J.Blake, , M. KAErn, , C. R.Cantor, and J. J.Collins, (2003) Noise in eukaryotic gene expression. Nature, 422, 633–637
https://doi.org/10.1038/nature01546. pmid: 12687005
43 M.Chen, , L. Wang, , C. C.Liu, and Q.Nie, (2013) Noise attenuation in the ON and OFF states of biological switches. ACS Synth. Biol., 2, 587–593
https://doi.org/10.1021/sb400044g. pmid: 23768065
44 E.Schrödinger, (1944) What is Life? Cambridge: Cambridge University Press
45 J. M.Schmiedel, , S. L.Klemm, , Y.Zheng, , A.Sahay, , N.Blüthgen, , D. S.Marks, and A.van Oudenaarden, (2015) MicroRNA control of protein expression noise. Science, 348, 128–132
https://doi.org/10.1126/science.aaa1738. pmid: 25838385
46 C.Bosia, , F. Sgrò, , L.Conti, , C.Baldassi, , D.Brusa, , F.Cavallo, , F. D.Cunto, , E.Turco, , A.Pagnani, and R.Zecchina, (2017) RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells. Genome Biol., 18, 37
https://doi.org/10.1186/s13059-017-1162-x. pmid: 28219439
47 W. H.Mather, , J.Hasty, , L. S.Tsimring, and R. J.Williams, (2013) Translational cross talk in gene networks. Biophys. J., 104, 2564–2572
https://doi.org/10.1016/j.bpj.2013.04.049. pmid: 23746529
48 T.-C.Chou, and P.Talaly, (1977) A simple generalized equation for the analysis of multiple inhibitions of Michaelis-Menten kinetic systems. J. Biol. Chem., 252, 6438–6442
pmid: 893418.
49 L.Bintu, , N. E. Buchler, , H. G.Garcia, , U.Gerland, , T.Hwa, , J.Kondev, and R.Phillips, (2005) Transcriptional regulation by the numbers: models. Curr. Opin. Genet. Dev., 15, 116–124
https://doi.org/10.1016/j.gde.2005.02.007. pmid: 15797194
50 P.Xie, , Y. Liu, , Y.Li, , M. Q.Zhang, and X.Wang, (2014) MIROR: a method for cell-type specific microRNA occupancy rate prediction. Mol. Biosyst., 10, 1377–1384
https://doi.org/10.1039/C3MB70610A. pmid: 24668253
51 Y.Rondelez, (2012) Competition for catalytic resources alters biological network dynamics. Phys. Rev. Lett., 108, 018102
https://doi.org/10.1103/PhysRevLett.108.018102. pmid: 22304295
52 J.Kim, , J. Hopfield, and E.Winfree, (2004) Neural network computation by in vitro transcriptional circuits. In Advances in Neural Information Processing Systems 17, 681–688. Vancouver, Canada
53 A. J.Genot, , T. Fujii, and Y.Rondelez, (2013) Scaling down DNA circuits with competitive neural networks. J. R. Soc. Interface, 10, 20130212
https://doi.org/10.1098/rsif.2013.0212. pmid: 23760296
54 M. R.Lakin, and D.Stefanovic, (2016) Supervised learning in adaptive DNA strand displacement networks. ACS Synth. Biol., 5, 885–897
https://doi.org/10.1021/acssynbio.6b00009. pmid: 27111037
55 H.Seitz, (2009) Redefining microRNA targets. Curr. Biol., 19, 870–873
https://doi.org/10.1016/j.cub.2009.03.059. pmid: 19375315
56 J. A.Brophy, and C. A.Voigt, (2014) Principles of genetic circuit design. Nat. Methods, 11, 508–520
https://doi.org/10.1038/nmeth.2926. pmid: 24781324
57 L.Potvin-Trottier, , N. D.Lord, , G.Vinnicombe, and J.Paulsson, (2016) Synchronous long-term oscillations in a synthetic gene circuit. Nature, 538, 514–517
https://doi.org/10.1038/nature19841. pmid: 27732583
58 C.Liao, , A. E. Blanchard, and T.Lu, (2017) An integrative circuit-host modelling framework for predicting synthetic gene network behaviours. Nat. Microbiol., 2, 1658–1666
https://doi.org/10.1038/s41564-017-0022-5. pmid: 28947816
59 O. S.Venturelli, , M.Tei, , S.Bauer, , L. J. G.Chan, , C. J.Petzold, and A. P.Arkin, (2017) Programming mRNA decay to modulate synthetic circuit resource allocation. Nat. Commun., 8, 15128
https://doi.org/10.1038/ncomms15128. pmid: 28443619
60 L.Nissim, , M. R. Wu, , E.Pery, , A.Binder-Nissim, , H. I.Suzuki, , D.Stupp, , C.Wehrspaun, , Y.Tabach, , P. A.Sharp, , and T. K.Lu, (2017) Synthetic RNA-based immunomodulatory gene circuits for cancer immunotherapy. Cell 171,1138–1150e1115
https://doi.org/https://doi.org/10.1016/j.cell.2017.09.049
[1] QB-18162-OF-WXW_suppl_1 Download
[1] Baizhu Chen, Zhuojun Dai. Combination of versatile platforms for the development of synthetic biology[J]. Quant. Biol., 2020, 8(1): 4-10.
[2] Guanghui Li, Jiawei Luo, Zheng Xiao, Cheng Liang. MTMO: an efficient network-centric algorithm for subtree counting and enumeration[J]. Quant. Biol., 2018, 6(2): 142-154.
[3] Meiyan Wang, Yuanhuan Yu, Jiawei Shao, Boon Chin Heng, Haifeng Ye. Engineering synthetic optogenetic networks for biomedical applications[J]. Quant. Biol., 2017, 5(2): 111-123.
[4] Guanyu Wang. Global quantitative biology can illuminate ontological connections between diseases[J]. Quant. Biol., 2017, 5(2): 191-198.
[5] David J Menn, Ri-Qi Su, Xiao Wang. Control of synthetic gene networks and its applications[J]. Quant. Biol., 2017, 5(2): 124-135.
[6] Keith C. Heyde, MaryJoe K. Rice, Sung-Ho Paek, Felicia Y. Scott, Ruihua Zhang, Warren C. Ruder. Modeling information exchange between living and artificial cells[J]. Quant. Biol., 2017, 5(1): 76-89.
[7] Mehdi Sadeghpour, Alan Veliz-Cuba, Gábor Orosz, Krešimir Josić, Matthew R. Bennett. Bistability and oscillations in co-repressive synthetic microbial consortia[J]. Quant. Biol., 2017, 5(1): 55-66.
[8] Russell Brown, Andreas Lengeling, Baojun Wang. Phage engineering: how advances in molecular biology and synthetic biology are being utilized to enhance the therapeutic potential of bacteriophages[J]. Quant. Biol., 2017, 5(1): 42-54.
[9] Marc Turcotte. Delineating the respective impacts of stochastic curl- and grad-forces in a family of idealized core genetic commitment circuits[J]. Quant. Biol., 2016, 4(2): 69-83.
[10] Amal Katrib, William Hsu, Alex Bui, Yi Xing. “RADIOTRANSCRIPTOMICS”: A synergy of imaging and transcriptomics in clinical assessment[J]. Quant. Biol., 2016, 4(1): 1-12.
[11] Hongguang Xi, Marc Turcotte. Parameter asymmetry and time-scale separation in core genetic commitment circuits[J]. Quant. Biol., 2015, 3(1): 19-45.
[12] Derek Eidum,Kanishk Asthana,Samir Unni,Michael Deng,Lingchong You. Construction, visualization, and analysis of biological network models in Dynetica[J]. Quant. Biol., 2014, 2(4): 142-150.
[13] Haoqian Zhang, Ying Sheng, Qianzhu Wu, Ao Liu, Yuheng Lu, Zhenzhen Yin, Yuansheng Cao, Weiqian Zeng, Qi Ouyang. Rational design of a biosensor circuit with semi-log dose-response function in Escherichia coli[J]. Quant. Biol., 2013, 1(3): 209-220.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed