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Networks Phenomena

by: Mrs. Damaris Hyatt

Networks Phenomena CSE 40768

Mrs. Damaris Hyatt
GPA 3.79


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This 0 page Class Notes was uploaded by Mrs. Damaris Hyatt on Sunday November 1, 2015. The Class Notes belongs to CSE 40768 at University of Notre Dame taught by Staff in Fall. Since its upload, it has received 24 views. For similar materials see /class/232759/cse-40768-university-of-notre-dame in Computer Science and Engineering at University of Notre Dame.

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Date Created: 11/01/15
Cl concentrations in the Sajama ice core and to a number of other pedological and geomorpho logical features indicative of longterm dry cli mates 8 11714 18 This decline in human activity around the Altiplano paleolakes is seen 39 most caves with ear y and late occupations separated by largely sterile midHolocene sed iments However a few sites including the caves of Tulan6 an Tulan68 show that people did not completely disappear from the area All of the sites of sporadic occupatio are located near wetlands in valleys near large springs or where lakes turned into wet 1 ds an subsistence resources were locally still available despite a generally arid climate Archaeological data from surrounding ar eas suggest that the Silencio Arqueologico applies best to the most arid areas of the central Andes where aridity thresholds for early societies were critical In contrast a g north of 2008 such as Salar Huasco and Peru 21 In northwest Argentina the Silen cio Arqueologico is found in fo f the six are associated with ephemeral streams 24 T e outhern limi 39 C 39 e d northwest Argentina has yet to be explored References and Notes T Dillehay Science 245 1436 1989 D J Meluer er al Am Anriq 62 659 1997 3 T F Lynch C M Stevenson Quar Res 37 117 1992 D H Sandweiss er al Science 281 1830 1998 L Nu ez M Grosjean Cartajena in Inrerhemixpherr ic Climate Linkages V Markgraf Ed Academic Press San Diego CA 2001 pp 1057117 M A Geyh M Grosjean L Nunez U Schotterer Quar Res 52 143 1999 7 J L Betancourt C Latorre J A Rech J Quade K Rylander Science 289 1542 2000 M Grosjean er al Global Planer Change 28 35 001 5 1115 m 9 C Latorre J L Betancourt K A Rylander J Quade Geol Soc Am Bull 114 349 2002 Charcoal in layers containing triangular points has been MC dated atTuinar1 Tuinar5 Tambillor1 San Lorenzor1 and Tuyajtor1 between 13000 an 9000 cal yr BP table S1 and fig S1 P A Baker er al Science 291 640 2001 G O Seluer S Cross P Baker R Dunbar S Friu Geology 26 167 1998 L C Thompson er al Science 282 1858 1998 001 L0 8 10 3 Z 9 8 E g EL quotQ E n m N o N N Lo Lo Locoximyi M T Alberdi written communication J Fernandez er a Geoarchaeology 6 251 1991 The histogram of middens is processed from 9 39 an L Nu ez Cartajena B Messerli Quar Res 48 239 1997 The term Silencio N o umans at sites that are not suscepu39ble to cl mate hange u spring and river oases that drain large Pleistocene aquifers or at sites where wetlands were created during the arid middle Holocene such as Tulan767 Tulan768 and Laguna Miscanti REPORTS 21 M Aldenderfer Science 241 1828 1988 midrHolocene hiatus is found at Inca Cueva 4 Huachichocana 3 Pintocamayoc and Yavi whereas occupation continued at the oases of Susques and brada Seca L Nu ez er al Exrud Aracamenox 17 125 1999 D H Sandweiss K A Maasch D G Anderson Scir ence 283 499 1999 25 Grants from the National Geographic Society 58367 96 the Swiss National Science Foundation 21757073 and Fondo Nacional de Desarrollo Cienr NN bw t1fico y Tecnologico 1930022 and comments by J P radbuly B Meggers G Seluer and D Stanford are acknowledged Supporting Online Material wwwsciencemagorgcgicontentfull2985594821 DCl Figs S1 to S3 Tables S1 and S2 22 July 2002 accepted 9 September 2002 Network Motifs Simple Building Blocks of Complex Networks R Milo1 S ShenOrr1 S Itzkovitz1 N Kashtan1 D Chklovskii2 quot1 Complex networks are studied across many fields of science To uncover their structural design principles we defined network motifsquot pattern from biochemistry neurobiology ecology and engineering The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web Similar motifs were found in networks that perform information processing even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Cae norhabditis elegans Motifs may thus define universal classes of networks This approach may uncover the basic building blocks of most networks Many of the complex networks that occur in ture have been shown to share global statis tical features 1710 These include the small worl property 179 of short paths between any two nodes and highly clustered connec tions In addition in many natural networks there are a few nodes with many more connec tions than the average node has In these types 1Departments of Physics of Complex Systems and Molecular Cell Biolo Weizmann Institute of Sci ence Rehovot Israel 76100 2Cold Spring Harbor Lab oratory Cold Spring Harbor NY 11724 USA To whom correspondence should be addressed E mail urialon weizmannacil Fig 1 A Examples A of networks termed scalefree networks 4 o en between 2 and 3 To go beyond these global features would require an understanding of the basic structural elements particular to each class of networks 9 To do this we developed an algorithm for detecting network moti s recurring significant patterns of inter nn 39 ns A demiled application to a gene regulation network has been presented 11 Related methods were used to test hypotheses on social networks 12 13 Here we generalize this approach to virtually any type of connec tivity graph and find the striking appearance of e edges between nodes in some of t e et xgtYmpmsm I I I I39llll 39plllll MW genex gene y M I lull com um was X Y X D Y Qquot networks go from the scale 0 L transcription factor protein X binds regu latory DNA regions of a gene to regulate the production rate protein through cells neuron X is synaptically con nected to neuron Y Bzzheegt gtemgte to organisms feeds on Y B All 13 types of threenode connected subgraphs 25 OCTOBER 2002 VOL 298 SCIENCE wwwsciencemagorg classes and edges represent synaptic connec bifan and the biparallel Table 1 Two of these motifs feedforward loop and bifan were REPORTS also found in the transcriptional gene regulation networks This similarity inmotifs may point to a fundamental similarity in the design con straints of the two types of networks Both net works function to carry information from sen sory components sensory neuronstranscription factors regulated by biochemical signals to ef 826 Table 1 Network motifs found in biological and technological networks The numbers of nodes and edges for each network are shown For each motif the numbers of appearances in the real network Meal and in the randomized networks N and SD all values rounded 17 18 are shown The Pvalue of all motifs is P lt 001 as determined by comparison to 1000 randomized networks 100 in the case of the World Wide Web As a qualitative measure of statistical significance theZscore Nreal NandSD is shown NS not significant Shown are motifs that occur at least U 4 times with completely different sets of nodes The networks are as follows 18 transcription interactions between regulatory proteins and genes in the bacterium E coli 11 and the yeast S cerevisiae 20 synaptic connections between neurons in C elegans including neurons connected by at least five synapses 24 trophic interactions in ecological food webs 22 representing pelagic and benthic species Little Rock Lake birds fishes invertebrates Ythan Estuaiy primarily larger fishes Chesapeake Bay lizards St Martin Island primarily inverte brates Skipwith Pond pelagic lake species Bridge Brook Lake and diverse desert taxa Coachella Valley eleCtronic sequential logic circuits parsed from the ISCA589 benchmark set 7 25 where nodes represent logic gates and flipflops presented are all five partial scans of forwardlogic chips and three digital fractional multipliers in the benchmark set and World Wide Web hyperlinks between Web pages in a single domain 4 only threenode motifs are shown e multiplied by the power of 10 eg 146e6 146 x 106 Network Nodes Edges Nreal Nrahdi SD 2 score Nreal Nrahdi SD Z score Nreal Nrahdi SD Z score he regulation X Feedr X BH39an transcription W formrd Y limp W z W z E coir 42 519 40 713 10 203 47112 13 S caret1x102quot 685 1052 70 1114 14 1812 300140 41 X Feedr X BH39an X B17 V formrd u amp parallel Y In Y W quotp z W N z Z W aete m1 252 509 125 90110 3 7 127 55113 5 3 227 35 110 20 Fund webs X Three X 1317 W cha39 M N parallel Y Y v 1 If 2 W Little Rock 92 984 3219 3120150 21 7295 22201210 25 Ythan 83 391 1182 1020120 7 2 1357 30150 23 st M 205 4 9 450110 Ns 382 130120 12 Chesapeake 31 67 8 82 1 4 Ns 26 5 1 2 8 29 243 279 235112 36 181 80120 5 Sklpwlih 25 189 184 0 1 7 5 5 397 80 1 25 13 Brook 25 104 181 13017 74 267 3017 32 Electronic circuiu X Feed X Y Birfan Bir formrd logic chips V formrd Y Z paraliel 1 mi N u z W W z s15850 10383 14240 424 212 285 1040 111 1200 480 211 335 s38584 20717 34204 413 1013 120 1739 612 800 711 912 320 s38417 23843 33661 612 312 400 2404 111 2550 531 212 340 s9234 5844 8197 211 211 140 754 111 1050 209 111 200 s13207 8651 11831 403 211 225 4445 111 4950 264 211 200 ectrohic circuits X Threer x y BH39an XgtY Four digitaliractiunalmultipliers node ode 7 ieedback ieedback 11 2 loop 2 W z ew lo s208 122 189 10 111 9 4 111 38 5 111 5 s420 252 399 20 111 18 10 111 10 11 111 11 s838r 512 819 40 111 38 22 111 20 23 111 25 World Wide Web Feedback X Fully X Uplinked i wiLhtVim J N connecmd f mutual mutual t E 5 ya 5 dyads Y E 3 Z Y Z hd edu 325729 146e6 1 1e5 2e3 11e2 800 6 8e6 5e414e2 15000 1 2e6 1e41 2e2 5000 Has additional fourrnode motif XaZ WYgtZ W ZgtW Nma 150 Nmd 85 15 Z THas additional fourrnode mouf XgtY Z YgtZi ZgtW Nm 204 N d 80 202 6 The threernode pattern XaY Z YgtZi ZgtY also occurs significantly more than at random It is not a mou39f by the present definition because it does not appear with comp etely distinct sets of nodes more than U 39 itional fourrnode motif XaYi YgtZ w 21x wax Mm 91 500 70 z 6 Has two additional threernode motifs XaY z YgtZi ZgtY N p 3e5 N d 14e3 6e1 z 6000 and xer Z YgtZ NW 5e5 N d 9e4 i 5e3 Z 250 fectors motor neuronsstructural genes The feedforward loop motif common to both types of networks may play a functional role in infor mation processing One possible function of this circuit is to activate output only if the input signal is persistent and to allow a rapid deacti vation when the input goes off 11 Indeed many of the input nodes in the neural feedfor ward loops are sensory neurons which may require this type of information processing to reject transient input uctuations that are inherent in a variable or noisy environment We also studied several technological net analyzed the ISCASS9 benchmark set of sequential logic electronic circuits 7 25 The nodes in these circuits represent logic gates and ip ops These nodes are linked by direct ed edges We found that the motifs separate the circuits into classes that correspond to the cir cuits functional description In Table l we present two classes consistin of five forward logic chips and three digiml fractional multipli ers The digital fractional multipliers share e motifs including three and fournode feedback loops The forward logic chips share the feed forward loop bi fan and bi parallel motifs which are similar to the motifs found in the genetic and neuronal informationprocessing networks We found a different set of motifs in a network of directed hyperlinks between World Wide Web pages within a single domain 4 The World Wide Web motifs may re ect a design aimed at short paths between related pages Application of our approach to nondi rected networks shows distinct sets of motifs in networks of protein interactions and Internet router connections 18 None of the network motifs shared by the food webs matched the motifs found in the gene regulation networks or the World Wide Web Only one of the food web consensus motifs also appeared in the neuronal network Different motif sets were found in electronic circuits with different functions This suggests that motifs can define broad classes of networks each with E erated each type of network for example food webs evolve to allow a ow of energy from the bottom to the top of food chains whereas gene regulation and neuron networks evolve to pro cess information Information processing seems to give rise to significantly different structures than does energy ow We further characterized the statistical sig nificance of the motifs as a function of network size by considering pieces of various sizes subnetworks of the full network The concen tration of motifs in the subnetworks is about the same as that in the full network Fig 3 In contrast the concentration of the corresponding subgraphs in the randomized versions of the subnetworks decreases s y wrth size In analogy with statistical physics the number of appearances of each motif in the real networks 25 OCTOBER 2002 VOL298 SCIENCE www5ciencemagorg appears to be an extensive variable ie one that grows linearly with the system size These variables are nonextensive in the ran omized networks The existence of such variables may be a unifying property of evolved or designed systems The decrease of the concentration C with randomized network size S Fig 3 qual itatively agrees with exact results 2 26 on ErdosRenyi random graphs random graphs that preserve only the n edges of the real network in which C N US In general the larger the network is the more signi cant the motifs tend to become This trend can also be seen in Table l by comparing networks of different sizes The network motif detection algorithm appears to be effective even for rather small networks on the order of 100 edges This is because three or fournode sub graphs occur in large numbers even in small tworks Furthermore our approach is not sensitive to data errors for example the sets of rearrangement of 20 of the edges at ran In informationprocessing networks the motifs may have specific functions as elemen arise because of the special constraints which the network has evolved 27 It is of value to detect and understand network motifs in order to gain insight into their dynamical behavior and to define classes of networks and readily generalized to an e o ne inc uding those with multiple colors of edges or nodes It would be fascinating to see what types of motifs occur in other networks and to undersmnd the processes that yield given motifs during network evolution References and Notes S H Strogau Narure 410 268 2001 B Bollobas Random Glaphs Academic London 1985 D Watts S Strogau Narure 393 440 1998 AL Barabasi R Albert Science 286 509 1999 Newman Proc Narl d Sci USA 98 404 SAPS NH 2001 H Jeong B Tombor R Albert Z N Oltvai A L Barabasi Narure 407 651 2000 7 R F C n o C Janssen R V Sole Pigs Rev E 64 045119 2001 8 R F Cancho R V Sole Proc R Soc London Ser B 268 2261 2001 L Amaral A Scala M Barthelemy H Stanley Proc Natl Acad Sci USA 97 11149 2000 B Huberman L Adamic Nature 401 131 1999 S ShenrOrr R M39lo S Mangan U Alon Nature Genet 31 64 2002 P Holand S Leinhardt in Sociological Merhodoly ogy D Heise Ed JosseyrBass San Francisco 1975 pp 1745 m Lo 23 w S Wasserman K Faust Social Nerwork Analysis Cambridge Univ Press New York 1994 N Guelzim S Bottani P Bourgine F Kepes Nature Gener 31 60 2002 ewman S Strogau D Watts Phys Rev E 64 6118 2001 S Maslov K Sneppen Science 296 910 2002 The randomized networks used for detecting mreer node mou39fs preserve the numbers of incoming outgor ma L I in Va REPORTS s for each node The randomized networks used nucu39ng mese randomized network ensembles are der scribed 18 Additional information is available at wwwweizmannacilmcb UriAlon 18 Methods are available as supporting material on Scir ence Online 19 D Thieffly A M Huerm F PerleRueda J Colladoe Vides Bioessays 20 433 1998 C Cost zo er al Nucleic Acids Res 29 75 2001 21 J Cohen F Briand C Newman Communig Food Webs Dara and Theoly Springer Berlin 1990 R Williams N Martinez Nature 404 180 2000 Pi Lawton J Cohen Nature 350 669 1991 J White F Southgate J Thomson S Brenner Philos Trans R Soc London Ser B 314 1 1986 F Brglez D Bryan K Kozminski Proc IEEEInr Symp Circuirs Sysr 1929 1989 26 In ErdosrRenyi randomized networks with a fixed connectivity the concentration of a subgraph with n nodes and k edges scales with network size as C NSWkquot thus C N 1S for the feedforward loop of Fig 3 wheren k 1 in which C should not vanish at l 3 The sole exception in Table arge S is the threerchain pattern in food webs where n 3 and k 2 N 1 D Callaway J Hopcroft J Kleinberg M Newman S Strogau Phys Rev E 64 041902 2001 N 9 discussions We thank J ColladorVides N tinez R Govindan R Durbin L Amaral S Maslov and K Sneppen for kindly prov as well as D Alon F Domany M Flowiu I Kanter O Hobart M Naor D Mukamel A Murra Reigl M Surette K Sneppen P R Cancho iding data Quake R Raz M Sternberg F Winfree and all members of our lab We thank S Maslov and K Sneppen for valuable Mare y S for comments We ha k C Center for Physics for their hospitality during part of this work We acknowledge support from Israel Science Foundation the Hum ence Program and the Minerva Foundation Supporting Online Maten 39al wwwsciencernagorgcgicontentfull2985594824DC1 Methods Table S1 1 May 2002 accepted 10 September 2002 Progression of Vertebrate Limb Development Through SHHMediated Counteraction of GLI3 Pascal te Welscher1 Aim e Zuniga1 Sanne Kuijper2 Thijs Drenth1 Hans J Goedemans1 Frits Meijlink2 Rolf Zeller1 Distal limb development and specification of digit identities in tetrapods are J L under the control of Hedgehog SH H is the r omc 39 quot r 1 rquot39 region In the posterior limb bud Ectopic anterior SHH signaling induces digit dupli cations and has been suspected as a major cause underlying congenital mal formations that result in digit polydactyly Here we report that the polydactyly of Gli3 deficient mice arises independently of SHH signaling Disruption of one or both Gli3 alleles in mouse embwos lacking Shh progressively restores limb distal 39 r fnrmahnn I l signaling counteracts GUSmediated repression of key regulator genes cell HH survival and distal progression of limb bud development The Hedgehog Hh signaling pathway con trols many key developmental processes dur 39 animal embryogenesis I In Drosophila embryos all known functions of Hh si nalin N by Cubitus interruptus Ci 2 mologs of h and Ci have been identified in higher vertebrates In particular Sonic Hedgehog SHH and the Ci homolog GLI3 are required for vertebrate limb development 37 6 GLI3 acts first during the initiation of 1Department of Developmental Biology Faculty of Biology Utrecht University Padualaan 8 NL3584 CH Utrecht Netherlands 2Hubrecht Laboratorium Upp salalaan 8 NL 3584 CT Utrecht Netherlands To whom correspondence should be addressed E mail 9 quot L39 39 limb bud development and before the activa tion of SHH signaling in posterior restriction of the basic helixloophelix transcription factor dHAND ND in turn prevents Gli3 expression from spreading posteriorly Fig 1A panel 1 7 In addition GLI3 restricts the SHHindependent early expres sion of 5 H0xD genes and Gremlin to the posterior mesench e 8 Subse dHAND functio n con two signaling centers Fig 1A panel 2 the polarizing region an instructive organizer lo cated in the posterior limb bud mesenchyrne and the apical ectodernial ridge AER SHH signaling by the polarizing region in combi nation with bone morphogenetic proteins www5ciencemagorg SCIENCE VOL 298 25 OCTOBER 2002 the an Frontier Scir


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