Network motif

Network motifs are connectivity-patterns (sub-graphs) that occur much more often than they do in random networks. Most networks studied in biology, ecology and other fields have been found to show a small set of network motifs; surprisingly, in most cases the networks seem to be largely composed of these network motifs, occurring again and again. Each type of network seems to display its own set of characteristic motifs (ecological networks have different motifs than gene regulation networks, etc.). These small circuits can be considered as simple building blocks from which the network is composed. This idea was first presented by Uri Alon and his group[1][2] when network motifs were discovered in the gene regulation (transcription) network of the bacteria E. coli[3] and then in a large set of natural networks.[4] In following work, the network motifs found in E. coli were discovered in the transcription networks of other bacteria[5] as well as yeast[4][6] and higher organisms.[7][8][9] A distinct set of network motifs were identified in other types of biological networks such as neuronal networks and protein interaction networks.[10][11][12]

Contents

Network motifs in gene regulation networks

Much experimental work has been devoted to understanding network motifs in gene regulatory networks. These networks control which genes are expressed in the cell in response to biological signals. The network is defined such that genes are nodes, and directed edges represent the control of one gene by a transcription factor (regulatory protein that binds DNA) encoded by another gene. Thus, network motifs are patterns of genes regulating each others transcription rate. When analyzing transcription networks, it is seen that the same network motifs appear again and again in diverse organisms from bacteria to human. The transcription network of E. coli and yeast, for example, is made of three main motif families, that make up almost the entire network. The leading hypothesis is that the network motif were independently selected by evolutionary processes in a converging manner,[13][14] since the creation or elimination of regulatory interactions is fast on evolutionary time scale, relative to the rate at which genes change,[13][14][15] Furthermore, experiments on the dynamics generated by network motifs in living cells indicate that they have characteristic dynamical functions. This suggests that the network motif serve as building blocks in gene regulatory networks that are beneficial to the organism.

The function of network motifs

The functions associated with common network motifs in transcription networks were explored and demonstrated by several research projects both theoretically and experimentally. Below are some of the most common network motifs and their associated function.

Negative auto-regulation (NAR)

Schematic representation of an auto-regulation motif

One of simplest and most abundant network motifs in E. coli is negative auto-regulation in which a transcription factor (TF) represses its own transcription. This motif was shown to perform two important functions. The first function is response acceleration. NAR was shown to speed-up the response to signals both theoretically and experimentally. This was first shown in a synthetic transcription network[16] and later on in the natural context in the SOS DNA repair system of E .coli.[17] The second function is increased stability of the auto-regulated gene product concentration against stochastic noise, thus reducing variations in protein levels between different cells.[18][19]

Positive auto-regulation (PAR)

Positive auto-regulation (PAR) occurs when a transcription factor enhances its own rate of production. Opposite to the NAR motif this motif slows the response time compared to simple regulation.[20] In the case of a strong PAR the motif may lead to a bimodal distribution of protein levels in cell populations.[21]

Feed-forward loops (FFL)

Schematic representation of a Feed-forward motif

This motif is commonly found in many gene systems and organisms. The motif consists of three genes and three regulatory interactions. The target gene Z is regulated by 2 TFs X and Y and in addition TF Y is also regulated TF X . Since each of the regulatory interactions may either be positive or negative there are possibly eight types of FFL motifs.[22] Two of those eight types: the coherent type 1 FFL (C1-FFL) (where all interactions are positive) and the incoherent type 1 FFL (I1-FFL) (X activates Z and also activates Y which represses Z) are found much more frequently in the transcription network of E. coli and yeast than the other six types.[22][23] In addition to the structure of the circuitry the way in which the signals from X and Y are integrated by the Z promoter should also be considered. In most of the cases the FFL is either an AND gate (X and Y are required for Z activation) or OR gate (either X or Y are sufficient for Z activation) but other input function are also possible.

Coherent type 1 FFL (C1-FFL)

The C1-FFL with an AND gate was shown to have a function of a ‘sign-sensitive delay’ element and a persistence detector both theoretically [22] and experimentally[24] with the arabinose system of E. coli. This means that this motif can provide pulse filtration in which short pulses of signal will not generate a response but persistent signals will generate a response after short delay. The shut off of the output when a persistent pulse is ended will be fast. The opposite behavior emerges in the case of a sum gate with fast response and delayed shut off as was demonstrated in the flagella system of E. coli.[25]

Incoherent type 1 FFL (I1-FFL)

The I1-FFL is a pulse generator and response accelerator. The two signal pathways of the I1-FFL acts in opposite directions where one pathway activates Z and the other represses it. When the repression is complete this leads to a pulse-like dynamics. It was also demonstrated experimentally that the I1-FFL can serve as response accelerator in a way which is similar to the NAR motif. The difference is that the I1-FFL can speed-up the response of any gene and not necessarily a transcription factor gene.[26] Recently additional function was assigned to the I1-FFL network motif: it was shown both theoretically and experimentally that the I1-FFL can generate non-monotonic input function in both a synthetic [27] and native systems.[28]

Multi-output FFLs

In some cases the same regulators X and Y regulate several Z genes of the same system. By adjusting the strength of the interactions this motif was shown to determine the temporal order of gene activation. This was demonstrated experimentally in the flagella system of E. coli.[29]

Single-input modules (SIM)

This motif occurs when a single regulator regulates a set of genes with no additional regulation. This is useful when the genes are cooperatively carrying out a specific function and therefore always need to be activated in a synchronized manner. By adjusting the strength of the interactions it can create temporal expression program of the genes it regulates.[30]

In the literature, Multiple-input modules (MIM) arose as a generalization of SIM. However, the precise definitions of SIM and MIM have been a source of inconsistency. There are attempts to provide orthogonal definitions for canonical motifs in biological networks and algorithms to enumerate them, especially SIM, MIM and Bi-Fan (2x2 MIM).[31]

Dense overlapping regulons (DOR)

This motif occurs in the case that several regulators combinatorially control a set of genes with diverse regulatory combinations. This motif was found in E. coli in various systems such as carbon utilization, anaerobic growth, stress response and others.[1][2][6] In order to better understand the function of this motif one has to obtain more information about the way the multiple inputs are integrated by the genes. Kaplan et al.[32] has mapped the input functions of the sugar utilization genes in E. coli, showing diverse shapes.

Activity motifs

An interesting generalization of the network-motifs, activity motifs are over occurring patterns that can be found when nodes and edges in the network are annotated with quantitative features. For instance, when edges in a metabolic pathways are annotated with the magnitude or timing of the corresponding gene expression, some patterns are over occurring given the underlying network structure.[33]

Criticism

An assumption (sometimes more sometimes less implicit) behind the preservation of a topological sub-structure is that it is of a particular functional importance. This assumption has recently been questioned. Some authors have argued that motifs, like bi-fan motifs, might show a variety depending on the network context, and therefore,[34] structure of the motif does not necessarily determine function. Network structure certainly does not always indicate function; this is an idea that has been around for some time, for an example see the Sin operon.[35]

Most analyses of motif function are carried out looking at the motif operating in isolation. Recent research[36] provides good evidence that network context, i.e. the connections of the motif to the rest of the network, are too important to draw inferences on function from local structure only — the cited paper also reviews the criticisms and alternative explanations for the observed data. Yet another recent work suggests that certain topological features of biological networks naturally give rise to the common appearance of canonical motifs, thereby questioning whether frequencies of occurrences are reasonable evidence that the structures of motifs are selected for their functional contribution to the operation of networks.[37]

References

  1. ^ a b Alon, U. (2006). An Introduction to Systems Biology: Design Principles of Biological Circuits. Boca Raton: CRC. 
  2. ^ a b Alon U (June 2007). "Network motifs: theory and experimental approaches". Nat. Rev. Genet. 8 (6): 450–61. doi:10.1038/nrg2102. PMID 17510665. 
  3. ^ Shen-Orr SS, Milo R, Mangan S, Alon U (May 2002). "Network motifs in the transcriptional regulation network of Escherichia coli". Nat. Genet. 31 (1): 64–8. doi:10.1038/ng881. PMID 11967538. 
  4. ^ a b Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (October 2002). "Network motifs: simple building blocks of complex networks". Science 298 (5594): 824–7. doi:10.1126/science.298.5594.824. PMID 12399590. http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=12399590. 
  5. ^ Eichenberger P, Fujita M, Jensen ST, et al. (October 2004). "The program of gene transcription for a single differentiating cell type during sporulation in Bacillus subtilis". PLoS Biol. 2 (10): e328. doi:10.1371/journal.pbio.0020328. PMC 517825. PMID 15383836. http://dx.plos.org/10.1371/journal.pbio.0020328. 
  6. ^ a b Lee TI, Rinaldi NJ, Robert F, et al. (October 2002). "Transcriptional regulatory networks in Saccharomyces cerevisiae". Science 298 (5594): 799–804. doi:10.1126/science.1075090. PMID 12399584. http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=12399584. 
  7. ^ Odom DT, Zizlsperger N, Gordon DB, et al. (February 2004). "Control of pancreas and liver gene expression by HNF transcription factors". Science 303 (5662): 1378–81. doi:10.1126/science.1089769. PMC 3012624. PMID 14988562. http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=14988562. 
  8. ^ Boyer LA, Lee TI, Cole MF, et al. (September 2005). "Core transcriptional regulatory circuitry in human embryonic stem cells". Cell 122 (6): 947–56. doi:10.1016/j.cell.2005.08.020. PMC 3006442. PMID 16153702. http://linkinghub.elsevier.com/retrieve/pii/S0092-8674(05)00825-1. 
  9. ^ Iranfar N, Fuller D, Loomis WF (February 2006). "Transcriptional regulation of post-aggregation genes in Dictyostelium by a feed-forward loop involving GBF and LagC". Dev. Biol. 290 (2): 460–9. doi:10.1016/j.ydbio.2005.11.035. PMID 16386729. http://linkinghub.elsevier.com/retrieve/pii/S0012-1606(05)00860-2. 
  10. ^ Milo R, Itzkovitz S, Kashtan N, et al. (March 2004). "Superfamilies of evolved and designed networks". Science 303 (5663): 1538–42. doi:10.1126/science.1089167. PMID 15001784. http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=15001784. 
  11. ^ Ma'ayan A, Jenkins SL, Neves S, et al. (August 2005). "Formation of regulatory patterns during signal propagation in a Mammalian cellular network". Science 309 (5737): 1078–83. doi:10.1126/science.1108876. PMC 3032439. PMID 16099987. http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=16099987. 
  12. ^ Ptacek J, Devgan G, Michaud G, et al. (December 2005). "Global analysis of protein phosphorylation in yeast". Nature 438 (7068): 679–84. doi:10.1038/nature04187. PMID 16319894. 
  13. ^ a b Babu MM, Luscombe NM, Aravind L, Gerstein M, Teichmann SA (June 2004). "Structure and evolution of transcriptional regulatory networks". Curr. Opin. Struct. Biol. 14 (3): 283–91. doi:10.1016/j.sbi.2004.05.004. PMID 15193307. http://linkinghub.elsevier.com/retrieve/pii/S0959440X04000788. 
  14. ^ a b Conant GC, Wagner A (July 2003). "Convergent evolution of gene circuits". Nat. Genet. 34 (3): 264–6. doi:10.1038/ng1181. PMID 12819781. 
  15. ^ Dekel E, Alon U (July 2005). "Optimality and evolutionary tuning of the expression level of a protein". Nature 436 (7050): 588–92. doi:10.1038/nature03842. PMID 16049495. 
  16. ^ Rosenfeld N, Elowitz MB, Alon U (November 2002). "Negative autoregulation speeds the response times of transcription networks". J. Mol. Biol. 323 (5): 785–93. doi:10.1016/S0022-2836(02)00994-4. PMID 12417193. http://linkinghub.elsevier.com/retrieve/pii/S0022283602009944. 
  17. ^ Camas FM, Blázquez J, Poyatos JF (August 2006). "Autogenous and nonautogenous control of response in a genetic network". Proc. Natl. Acad. Sci. U.S.A. 103 (34): 12718–23. doi:10.1073/pnas.0602119103. PMC 1568915. PMID 16908855. http://www.pnas.org/cgi/pmidlookup?view=long&pmid=16908855. 
  18. ^ Becskei A, Serrano L (June 2000). "Engineering stability in gene networks by autoregulation". Nature 405 (6786): 590–3. doi:10.1038/35014651. PMID 10850721. 
  19. ^ Dublanche Y, Michalodimitrakis K, Kümmerer N, Foglierini M, Serrano L (2006). "Noise in transcription negative feedback loops: simulation and experimental analysis". Mol. Syst. Biol. 2 (1): 41. doi:10.1038/msb4100081. PMC 1681513. PMID 16883354. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1681513. 
  20. ^ Maeda YT, Sano M (June 2006). "Regulatory dynamics of synthetic gene networks with positive feedback". J. Mol. Biol. 359 (4): 1107–24. doi:10.1016/j.jmb.2006.03.064. PMID 16701695. http://linkinghub.elsevier.com/retrieve/pii/S0022-2836(06)00426-8. 
  21. ^ Becskei A, Séraphin B, Serrano L (May 2001). "Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion". EMBO J. 20 (10): 2528–35. doi:10.1093/emboj/20.10.2528. PMC 125456. PMID 11350942. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=125456. 
  22. ^ a b c Mangan S, Alon U (October 2003). "Structure and function of the feed-forward loop network motif". Proc. Natl. Acad. Sci. U.S.A. 100 (21): 11980–5. doi:10.1073/pnas.2133841100. PMC 218699. PMID 14530388. http://www.pnas.org/cgi/pmidlookup?view=long&pmid=14530388. 
  23. ^ Ma HW, Kumar B, Ditges U, Gunzer F, Buer J, Zeng AP (2004). "An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs". Nucleic Acids Res. 32 (22): 6643–9. doi:10.1093/nar/gkh1009. PMC 545451. PMID 15604458. http://nar.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=15604458. 
  24. ^ Mangan S, Zaslaver A, Alon U (November 2003). "The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks". J. Mol. Biol. 334 (2): 197–204. doi:10.1016/j.jmb.2003.09.049. PMID 14607112. http://linkinghub.elsevier.com/retrieve/pii/S0022283603012038. 
  25. ^ Kalir S, Mangan S, Alon U (2005). "A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coli". Mol. Syst. Biol. 1 (1): 2005.0006. doi:10.1038/msb4100010. PMC 1681456. PMID 16729041. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1681456. 
  26. ^ Mangan S, Itzkovitz S, Zaslaver A, Alon U (March 2006). "The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli". J. Mol. Biol. 356 (5): 1073–81. doi:10.1016/j.jmb.2005.12.003. PMID 16406067. http://linkinghub.elsevier.com/retrieve/pii/S0022-2836(05)01557-3. 
  27. ^ Entus R, Aufderheide B, Sauro HM (August 2007). "Design and implementation of three incoherent feed-forward motif based biological concentration sensors". Syst Synth Biol 1 (3): 119–28. doi:10.1007/s11693-007-9008-6. PMC 2398716. PMID 19003446. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2398716. 
  28. ^ Kaplan S, Bren A, Dekel E, Alon U (2008). "The incoherent feed-forward loop can generate non-monotonic input functions for genes". Mol. Syst. Biol. 4 (1): 203. doi:10.1038/msb.2008.43. PMC 2516365. PMID 18628744. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2516365. 
  29. ^ Kalir S, McClure J, Pabbaraju K, et al. (June 2001). "Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria". Science 292 (5524): 2080–3. doi:10.1126/science.1058758. PMID 11408658. http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=11408658. 
  30. ^ Zaslaver A, Mayo AE, Rosenberg R, et al. (May 2004). "Just-in-time transcription program in metabolic pathways". Nat. Genet. 36 (5): 486–91. doi:10.1038/ng1348. PMID 15107854. 
  31. ^ Konagurthu AS, Lesk AM. (2008). "Single and Multiple Input Modules in regulatory networks". Proteins 73 (2): 320–324. 
  32. ^ Kaplan S, Bren A, Zaslaver A, Dekel E, Alon U (March 2008). "Diverse two-dimensional input functions control bacterial sugar genes". Mol. Cell 29 (6): 786–92. doi:10.1016/j.molcel.2008.01.021. PMC 2366073. PMID 18374652. http://linkinghub.elsevier.com/retrieve/pii/S1097-2765(08)00163-9. 
  33. ^ Chechik G, Oh E, Rando O, Weissman J, Regev A, Koller D (November 2008). "Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network". Nat. Biotechnol. 26 (11): 1251–9. doi:10.1038/nbt.1499. PMC 2651818. PMID 18953355. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2651818. 
  34. ^ Ingram PJ, Stumpf MP, Stark J (2006). "Network motifs: structure does not determine function". BMC Genomics 7: 108. doi:10.1186/1471-2164-7-108. PMC 1488845. PMID 16677373. http://www.biomedcentral.com/1471-2164/7/108. 
  35. ^ Voigt CA, Wolf DM, Arkin AP (March 2005). "The Bacillus subtilis sin operon: an evolvable network motif". Genetics 169 (3): 1187–202. doi:10.1534/genetics.104.031955. PMC 1449569. PMID 15466432. http://www.genetics.org/cgi/pmidlookup?view=long&pmid=15466432. 
  36. ^ Knabe JF, Nehaniv CL, Schilstra MJ (2008). "Do motifs reflect evolved function?—No convergent evolution of genetic regulatory network subgraph topologies". BioSystems 94 (1-2): 68–74. doi:10.1016/j.biosystems.2008.05.012. PMID 18611431. http://linkinghub.elsevier.com/retrieve/pii/S0303-2647(08)00128-7. 
  37. ^ Konagurthu AS, Lesk AM (2008). "On the origin of distribution patterns of motifs in biological networks". BMC Syst Biol 2: 73. doi:10.1186/1752-0509-2-73. PMC 2538512. PMID 18700017. http://www.biomedcentral.com/1752-0509/2/73. 

External links


Wikimedia Foundation. 2010.

Look at other dictionaries:

  • Motif — See also: Motive Motif may refer to the following: In creative work: Motif (music), a perceivable or salient recurring fragment or succession of notes Motif (narrative), any recurring element in a story that has symbolic significance Motif… …   Wikipedia

  • MOTIF — (oder MOTIF) ist eine Programmbibliothek, mit der u. a. grafische Benutzerschnittstellen (GUIs) unter dem X Window System auf Unix und anderen POSIX artigen Systemen entwickelt werden können. Seit Version 2.1 unterstützt Motif Unicode, was dazu… …   Deutsch Wikipedia

  • Mega Man Battle Network — The Mega Man Battle Network series is one of Capcom s Mega Man series and debuted in 2001 on the Game Boy Advance. It is a spin off series based on the original Mega Man. In Japan, as of the release of Rockman EXE Transmission in 2003, the series …   Wikipedia

  • Mega Man Battle Network (series) — The Mega Man Battle Network series is one of Capcom s Mega Man series and debuted in 2001 on the Game Boy Advance. It is a spin off series based on the original Mega Man. In Japan, as of the release of Rockman EXE Transmission in 2003, the series …   Wikipedia

  • Artificial Neural Network — Réseau de neurones Pour les articles homonymes, voir Réseau. Vue simplifiée d un réseau artificiel de neurones Un réseau de neurones artificiel est un modèle de c …   Wikipédia en Français

  • Neuronal network — Réseau de neurones Pour les articles homonymes, voir Réseau. Neurosciences …   Wikipédia en Français

  • GMA Network Logos — The GMA Network, as the television station in the Philippines (then known as Loreto F. de Hemedes Inc. [ [http://www.gmanetwork.com/about GMA Network Corporate Information] , GMANetwork.com] afterward renamed to Republic Broadcasting System [RBS] …   Wikipedia

  • Yamaha Motif — The Yamaha Motif is a series of music workstations, first released by Yamaha Corporation in August 2001. Other workstations in the same class are the Korg Triton, Roland Fantom G and Alesis Fusion. Contents 1 Product lineup …   Wikipedia

  • The CW Television Network — Type Broadcast television network …   Wikipedia

  • Starz (TV network) — Infobox Network network name = Starz network slogan = Are you ready? country = United States network type = Cable network (movies) available = National owner = Starz Entertainment (Liberty Media) launch date = February 1, 1994 website =… …   Wikipedia

Share the article and excerpts

Direct link
Do a right-click on the link above
and select “Copy Link”

We are using cookies for the best presentation of our site. Continuing to use this site, you agree with this.