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Cell Biology: Networks , Regulation and Pathways

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Encyclopedia of Complexity and Systems Science

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Abbreviations

Dynamical system:

is a set of components the properties of which (e. g. their quantity, activity level etc.) change in time because the components interact among themselves and are also influenced by external forces.

Network node:

is a constituent component of the network, in biological networks most often identified with a molecular species.

Interaction:

is a connection between network nodes; in biological networks an interaction means that two nodes chemically react, regulate each other, or effectively influence each other's activities. Interactions are mostly pairwise, but can be higher-order as well; they can be directed or undirected, and are usually characterized by an interaction strength.

Network:

is a system of interacting nodes. A network can be represented mathematically as a graph, where vertices denote the nodes and edges denote the interactions. Biological networks often are understood to be dynamical systems as well, because the activities of network nodes evolve in time due to the graph of interactions.

Network state:

is the vector of activities of all nodes that fully characterizes the network at any point in time; since a biological network is a dynamical system, this state generally changes through time according to a set of dynamical equations.

Biological function :

refers to the role that a specific network plays in the life of the organism; the network can be viewed as existing to perform a task that enables the cell to survive and reproduce, such as the detection or transduction of a specific chemical signal.

Pathway:

is a subset of nodes and interactions in a network along which information or energy and matter flow in a directed fashion; pathways can be coupled through interactions or unwanted cross-talk.

Curse of dimensionality:

is the rapid increase of complexity encountered when analyzing or experimentally observing network states, as more and more network nodes are added. If there are N network nodes each of which only has two states (for example on and off), the number of states that the network can be in grows as 2N.

Design principle :

is an (assumed) constraint on the network architecture, stating that a biological network, in addition to performing a certain function, implements that function in a particular way, usually to maximize or minimize some further objective measure, for instance robustness, information transmission, or designability.

Bibliography

  1. Acar M, Becksei A, van Oudenaarden A (2005) Enhancement of cellular memory by reducing stochastic transitions. Nature 435:228–232

    ADS  Google Scholar 

  2. Ackers GK, Johnson AD, Shea MA (1982) Quantitative model for gene regulation by lambda phage repressor. Proc Natl Acad Sci (USA) 79(4):1129–33

    ADS  Google Scholar 

  3. Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–82

    ADS  Google Scholar 

  4. Aldana M, Cluzel P (2003) A natural class of robust networks. Proc Natl Acad Sci (USA) 100:8710–4

    ADS  Google Scholar 

  5. Alm E, Arkin AP (2003) Biological networks. Curr Opin Struct Biol 13(2):193–202

    Google Scholar 

  6. Alon U, Surette MG, Barkai N, Leibler S (1999) Robustness in bacterial chemotaxis. Nature 397(6715):168–71

    ADS  Google Scholar 

  7. Arkin A, Ross J, McAdams HH (1998) Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected escherichia coli cells. Genetics 149(4):1633–48

    Google Scholar 

  8. Arnosti DN, Kulkarni MM (2005) Transcriptional enhancers: Intelligent enhanceosomes or flexible billboards? J Cell Biochem 94(5):890–8

    Google Scholar 

  9. Bar-Even A, Paulsson J, Maheshri N, Carmi M, O’Shea E, Pilpel Y, Barkai N (2006) Noise in protein expression scales with natural protein abundance. Nat Genet 38(6):636–43

    Google Scholar 

  10. Barabási AL (2002) Linked: The New Science of Networks. Perseus Publishing, Cambridge

    Google Scholar 

  11. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–12

    MathSciNet  ADS  Google Scholar 

  12. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–13

    Google Scholar 

  13. Barkai N, Leibler S (1997) Robustness in simple biochemical networks. Nature 387(6636):913–7

    ADS  Google Scholar 

  14. Baylor DA, Lamb TD, Yau KW (1979) Responses of retinal rods to single photons. J Physiol (Lond) 288:613–634

    Google Scholar 

  15. Becskei A, Serrano L (2000) Engineering stability in gene networks by autoregulation. Nature 405(6786):590–3

    ADS  Google Scholar 

  16. Berg HC (1975) Chemotaxis in bacteria. Annu Rev Biophys Bioeng 4(00):119–36

    Google Scholar 

  17. Berg HC, Purcell EM (1977) Physics of chemoreception. Biophys J 20(2):193–219

    Google Scholar 

  18. Bergmann S, Sandler O, Sberro H, Shnider S, Schejter E, Shilo BZ, Barkai N (2007) Pre-steady-state decoding of the bicoid morphogen gradient. PLoS Biol 5(2):e46

    Google Scholar 

  19. Bethe HA (1935) Statistical theory of superlattices. Proc R Soc London Ser A 150:552–575

    ADS  Google Scholar 

  20. Bialek W (1987) Physical limits to sensation and perception. Annu Rev Biophys Biophys Chem 16:455–78

    Google Scholar 

  21. Bialek W (2001) Stability and noise in biochemical switches. Adv Neur Info Proc Syst 13:103

    Google Scholar 

  22. Bialek W, Setayeshgar S (2005) Physical limits to biochemical signaling. Proc Natl Acad Sci (USA) 102(29):10040–5

    ADS  Google Scholar 

  23. Bialek W, Setayeshgar S (2006) Cooperativity, sensitivity and noise in biochemical signaling. arXiv.org:q-bio.MN/0601001

    Google Scholar 

  24. Bintu L, Buchler NE, Garcia HG, Gerland U, Hwa T, Kondev J, Kuhlman T, Phillips R (2005a) Transcriptional regulation by the numbers: applications. Curr Opin Genet Dev 15(2):125–35

    Google Scholar 

  25. Bintu L, Buchler NE, Garcia HG, Gerland U, Hwa T, Kondev J, Phillips R (2005b) Transcriptional regulation by the numbers: models. Curr Opin Genet Dev 15(2):116–24

    Google Scholar 

  26. Blake WJ, Kaern M, Cantor CR, Collins JJ (2003) Noise in eukaryotic gene expression. Nature 422(6932):633–7

    ADS  Google Scholar 

  27. Block SM, Segall JE, Berg HC (1983) Adaptation kinetics in bacterial chemotaxis. J Bacteriol 154(1):312–23

    Google Scholar 

  28. Bray D (1995) Protein molecules as computational elements in living cells. Nature 376(6538):307–12

    ADS  Google Scholar 

  29. Bray D, Bourret RB, Simon MI (1993) Computer simulation of the phosphorylation cascade controlling bacterial chemotaxis. Mol Biol Cell 4(5):469–82

    Google Scholar 

  30. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21(1 Suppl):33–7

    Google Scholar 

  31. Buchler NE, Gerland U, Hwa T (2003) On schemes of combinatorial transcription logic. Proc Natl Acad Sci (USA) 100(9):5136–41

    ADS  Google Scholar 

  32. Chang L, Karin M (2001) Mammalian map kinase signalling cascades. Nature 410(6824):37–40

    ADS  Google Scholar 

  33. Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ (2000) Kinetic analysis of a molecular model of the budding yeast cell cycle. Mol Biol Cell 11(1):369–91

    Google Scholar 

  34. von Dassow G, Meir E, Munro EM, Odell GM (2000) The segment polarity network is a robust developmental module. Nature 406(6792):188–92

    ADS  Google Scholar 

  35. Dekel E, Alon U (2005) Optimality and evolutionary tuning of the expression level of a protein. Nature 436(7050):588–92

    ADS  Google Scholar 

  36. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci (USA) 95(25):14863–8

    ADS  Google Scholar 

  37. Elemento O, Slonim N, Tavazoie S (2007) A universal framework for regulatory element discovery across all genomes and data types. Mol Cell 28(2):337–50

    Google Scholar 

  38. Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403(6767):335–8

    ADS  Google Scholar 

  39. Elowitz MB, Levine AJ, Siggia ED, Swain PS (2002) Stochastic gene expression in a single cell. Science 297(5584):1183–6

    ADS  Google Scholar 

  40. Falke JJ, Bass RB, Butler SL, Chervitz SA, Danielson MA (1997) The two-component signaling pathway of bacterial chemotaxis: a molecular view of signal transduction by receptors, kinases, and adaptation enzymes. Annu Rev Cell Dev Biol 13:457–512

    Google Scholar 

  41. Francois P, Hakim V (2004) Design of genetic networks with specified functions by evolution in silico. Proc Natl Acad Sci (USA) 101(2):580–5

    ADS  Google Scholar 

  42. Francois P, Hakim V, Siggia ED (2007) Deriving structure from evolution: metazoan segmentation. Mol Syst Bio 3: Article 154

    Google Scholar 

  43. Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303(5659):799–805

    ADS  Google Scholar 

  44. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767):339–42

    ADS  Google Scholar 

  45. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM, Remor M, Hofert C, Schelder M, Brajenovic M, Ruffner H, Merino A, Klein K, Hudak M, Dickson D, Rudi T, Gnau V, Bauch A, Bastuck S, Huhse B, Leutwein C, Heurtier MA, Copley RR, Edelmann A, Querfurth E, Rybin V, Drewes G, Raida M, Bouwmeester T, Bork P, Seraphin B, Kuster B, Neubauer G, Superti-Furga G (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415(6868):141–7

    ADS  Google Scholar 

  46. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O’Shea EK, Weissman JS (2003) Global analysis of protein expression in yeast. Nature 425(6959):737–41

    Google Scholar 

  47. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361

    Google Scholar 

  48. Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58:35–55

    ADS  Google Scholar 

  49. Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, Vijayadamodar G, Pochart P, Machineni H, Welsh M, Kong Y, Zerhusen B, Malcolm R, Varrone Z, Collis A, Minto M, Burgess S, McDaniel L, Stimpson E, Spriggs F, Williams J, Neurath K, Ioime N, Agee M, Voss E, Furtak K, Renzulli R, Aanensen N, Carrolla S, Bickelhaupt E, Lazovatsky Y, DaSilva A, Zhong J, Stanyon CA, Finley J R L, White KP, Braverman M, Jarvie T, Gold S, Leach M, Knight J, Shimkets RA, McKenna MP, Chant J, Rothb erg JM (2003) A protein interaction map of Drosophila melanogaster. Science (5651):1727–36

    Google Scholar 

  50. Golding I, Paulsson J, Zawilski SM, Cox EC (2005) Real-time kinetics of gene activity in individual bacteria. Cell 123(6):1025–36

    Google Scholar 

  51. Goldman MS, Golowasch J, Marder E, Abbott LF (2001) Global structure robustness and modulation of neural models. J Neurosci 21:5229–5238

    Google Scholar 

  52. Gonze D, Halloy J, Goldbeter A (2002) Robustness of circadian rhythms with respect to molecular noise. Proc Natl Acad Sci (USA) 99(2):673–8

    ADS  Google Scholar 

  53. Goulian M (2004) Robust control in bacterial regulatory circuits. Curr Opin Microbiol 7(2):198–202

    Google Scholar 

  54. Gregor T, Tank DW, Wieschaus EF, Bialek W (2007a) Probing the limits to positional information. Cell 130(1):153–64

    Google Scholar 

  55. Gregor T, Wieschaus EF, McGregor AP, Bialek W, Tank DW (2007b) Stability and nuclear dynamics of the bicoid morphogen gradient. Cell 130(1):141–52

    Google Scholar 

  56. Guet CC, Elowitz MB, Hsing W, Leibler S (2002) Combinatorial synthesis of genetic networks. Science 296(5572):1466–70

    ADS  Google Scholar 

  57. Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJ, Cusick ME, Roth FP, Vidal M (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430(6995):88–93

    ADS  Google Scholar 

  58. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402(6761 Suppl):C47–52

    Google Scholar 

  59. Hasty J, McMillen D, Isaacs F, Collins JJ (2001) Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet 2(4):268–79

    Google Scholar 

  60. Heinrich R, Schuster S (1996) The Regulation of Cellular Systems. Chapman and Hall, New York

    Google Scholar 

  61. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, Millar A, Taylor P, Bennett K, Boutilier K, Yang L, Wolting C, Donaldson I, Schandorff S, Shewnarane J, Vo M, Taggart J, Goudreault M, Muskat B, Alfarano C, Dewar D, Lin Z, Michalickova K, Willems AR, Sassi H, Nielsen PA, Rasmussen KJ, Andersen JR, Johansen LE, Hansen LH, Jespersen H, Podtelejnikov A, Nielsen E, Crawford J, Poulsen V, Sorensen BD, Matthiesen J, Hendrickson RC, Gleeson F, Pawson T, Moran MF, Durocher D, Mann M, Hogue CW, Figeys D, Tyers M (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415(6868):180–3

    ADS  Google Scholar 

  62. Hofman J, Wiggins C (2007) A bayesian approach to network modularity. arXiv.org:07093512

    Google Scholar 

  63. Hooshangi S, Thiberge S, Weiss R (2005) Ultrasensitivity and noise propagation in a synthetic transcriptional cascade. Proc Natl Acad Sci (USA) 102(10):3581–6

    ADS  Google Scholar 

  64. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci (USA) 79(8):2554–8

    MathSciNet  ADS  Google Scholar 

  65. Huang KC, Meir Y, Wingreen NS (2003) Dynamic structures in Escherichia coli: spontaneous formation of mine rings and mind polar zones. Proc Natl Acad Sci (USA) 100(22):12724–8

    ADS  Google Scholar 

  66. Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420(6912):186–9

    ADS  Google Scholar 

  67. Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N (2002) Revealing modular organization in the yeast transcriptional network. Nat Genet 31(4):370–7

    Google Scholar 

  68. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci (USA) 98(8):4569–74

    ADS  Google Scholar 

  69. Jacob F, Monod J (1961) Genetic regulatory mechanisms in the synthesis of proteins. J Mol Biol 3:318–56

    Google Scholar 

  70. Jansen R, Gerstein M (2004) Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr Opin Microbiol 7(5):535–45

    Google Scholar 

  71. Jaynes ET (1957) Information theory and statistical mechanics. Phys Rev 106:62–79

    MathSciNet  ADS  Google Scholar 

  72. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL (2000) The large-scale organization of metabolic networks. Nature 407(6804):651–4

    ADS  Google Scholar 

  73. Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411(6833):41–2

    ADS  Google Scholar 

  74. Jordan JD, Landau EM, Iyengar R (2000) Signaling networks: the origins of cellular multitasking. Cell 103(2):193–200

    Google Scholar 

  75. Kadonaga JT (2004) Regulation of RNA polymerase II transcription by sequence-specific DNA binding factors. Cell 116(2):247–57

    Google Scholar 

  76. van Kampen NG (2007) Stochastic Processes in Physics and Chemistry. Elsevier, Amsterdam

    Google Scholar 

  77. Kanehisa M, Goto S, Kawashima S, Nakaya A (2002) The KEGG databases at genomenet. Nucleic Acids Res 30(1):42–6

    Google Scholar 

  78. Karp PD, Riley M, Saier M, Paulsen IT, Collado-Vides J, Paley SM, Pellegrini-Toole A, Bonavides C, Gama-Castro S (2002) The ecocyc database. Nucleic Acids Res 30(1):56–8

    Google Scholar 

  79. Kashtan N, Alon U (2005) Spontaneous evolution of modularity and network motifs. Proc Natl Acad Sci (USA) 102(39):13773–8

    ADS  Google Scholar 

  80. Kauffman SA (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22(3):437–67

    Google Scholar 

  81. Keller EF (2005) Revisiting "scale-free" networks. Bioessays 27(10):1060–8

    Google Scholar 

  82. Kirschner M, Gerhart J (1998) Evolvability. Proc Natl Acad Sci (USA) 95(15):8420–7

    ADS  Google Scholar 

  83. Kolch W (2000) Meaningful relationships: the regulation of the ras/raf/mek/erk pathway by protein interactions. Biochem J 351 Pt 2:289–305

    Google Scholar 

  84. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, Li J, Pu S, Datta N, Tikuisis AP, Punna T, Peregrin-Alvarez JM, Shales M, Zhang X, Davey M, Robinson MD, Paccanaro A, Bray JE, Sheung A, Beattie B, Richards DP, Canadien V, Lalev A, Mena F, Wong P, Starostine A, Canete MM, Vlasblom J, Wu S, Orsi C, Collins SR, Chandran S, Haw R, Rilstone JJ, Gandi K, Thompson NJ, Musso G, St Onge P, Ghanny S, Lam MH, Butland G, Altaf-Ul AM, Kanaya S, Shilatifard A, O’Shea E, Weissman JS, Ingles CJ, Hughes TR, Parkinson J, Gerstein M, Wodak SJ, Emili A, Greenblatt JF (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440(7084):637–43

    Google Scholar 

  85. Kuhlman T, Zhang Z, Saier J M H, Hwa T (2007) Combinatorial transcriptional control of the lactose operon of Escherichia coli. Proc Natl Acad Sci (USA) 104(14):6043–8

    ADS  Google Scholar 

  86. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594):799–804

    ADS  Google Scholar 

  87. Leloup JC, Goldbeter A (2003) Toward a detailed computational model for the mammalian circadian clock. Proc Natl Acad Sci (USA) 100(12):7051–6

    ADS  Google Scholar 

  88. LeMasson G, Marder E, Abbott LF (1993) Activity-dependent regulation of conductances in model neurons. Science 259:1915–1917

    ADS  Google Scholar 

  89. Levine M, Davidson EH (2005) Gene regulatory networks for development. Proc Natl Acad Sci (USA) 102(14):4936–42

    ADS  Google Scholar 

  90. Lezon TR, Banavar JR, Cieplak M, Maritan A, Federoff NV (2006) Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns. Proc Natl Acad Sci (USA) 103:19033–19038

    ADS  Google Scholar 

  91. Li F, Long T, Lu Y, Ouyang Q, Tang C (2004) The yeast cell-cycle network is robustly designed. Proc Natl Acad Sci (USA) 101(14):4781–6

    ADS  Google Scholar 

  92. Libby E, Perkins TJ, Swain PS (2007) Noisy information processing through transcriptional regulation. Proc Natl Acad Sci (USA) 104(17):7151–6

    ADS  Google Scholar 

  93. Marder E, Bucher D (2006) Variability, compensation and homeostasis in neuron and network function. Nature Rev Neurosci 7:563–574

    Google Scholar 

  94. Markowetz F, Spang R (2007) Inferring cellular networks – a review. BMC Bioinformatics 8: 6-S5

    Google Scholar 

  95. Martin DE, Hall MN (2005) The expanding tor signaling network. Curr Opin Cell Biol 17(2):158–66

    Google Scholar 

  96. Maslov S, Sneppen K (2002) Specificity and stability in topology of protein networks. Science 296(5569):910–3

    ADS  Google Scholar 

  97. McAdams HH, Arkin A (1997) Stochastic mechanisms in gene expression. Proc Natl Acad Sci (USA) 94(3):814–9

    ADS  Google Scholar 

  98. McAdams HH, Arkin A (1999) It’s a noisy business! Genetic regulation at the nanomolar scale. Trends Genet 15(2):65–9

    Google Scholar 

  99. McAdams HH, Shapiro L (1995) Circuit simulation of genetic networks. Science 269(5224):650–6

    ADS  Google Scholar 

  100. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P (2002) Comparative assessment of large‐scale data sets of protein-protein interactions. Nature 417(6887):399–403

    ADS  Google Scholar 

  101. Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, Ayzenshtat I, Sheffer M, Alon U (2004) Superfamilies of evolved and designed networks. Science 303(5663):1538–42

    ADS  Google Scholar 

  102. Nakajima M, Imai K, Ito H, Nishiwaki T, Murayama Y, Iwasaki H, Oyama T, Kondo T (2005) Reconstitution of circadian oscillation of cyanobacterial kaic phophorylation in vitro. Science 308:414–415

    ADS  Google Scholar 

  103. Nasmyth K (1996) At the heart of the budding yeast cell cycle. Trends Genet 12(10):405–12

    Google Scholar 

  104. Newman M, Watts D, Barabási AL (2006) The Structure and Dynamics of Networks. Princeton University Press, Princeton

    Google Scholar 

  105. Nielsen UB, Cardone MH, Sinskey AJ, MacBeath G, Sorger PK (2003) Profiling receptor tyrosine kinase activation by using ab microarrays. Proc Natl Acad Sci (USA) 100(16):9330–5

    ADS  Google Scholar 

  106. Nochomovitz YD, Li H (2006) Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output. Proc Natl Acad Sci (USA) 103(11):4180–5

    ADS  Google Scholar 

  107. Novak B, Tyson JJ (1997) Modeling the control of DNA replication in fission yeast. Proc Natl Acad Sci (USA) 94(17):9147–52

    ADS  Google Scholar 

  108. Nurse P (2001) Cyclin dependent kinases and cell cycle control. Les Prix Nobel

    Google Scholar 

  109. Ozbudak EM, Thattai M, Kurtser I, Grossman AD, van Oudenaarden A (2002) Regulation of noise in the expression of a single gene. Nat Genet 31(1):69–73

    Google Scholar 

  110. Papin JA, Hunter T, Palsson BO, Subramaniam S (2005) Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol 6(2):99–111

    Google Scholar 

  111. Paulsson J (2004) Summing up the noise in gene networks. Nature 427(6973):415–8

    ADS  Google Scholar 

  112. Pedraza JM, van Oudenaarden A (2005) Noise propagation in gene networks. Science 307(5717):1965–9

    ADS  Google Scholar 

  113. Perez OD, Nolan GP (2002) Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry. Nat Biotechnol 20(2):155–62

    Google Scholar 

  114. Ptashne M (2001) Genes and Signals. CSHL Press, Cold Spring Harbor, USA

    Google Scholar 

  115. Ptashne M (2004) A Genetic Switch: Phage lambda Revisited. CSHL Press

    Google Scholar 

  116. Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S (2006) Stochastic mRNA synthesis in mammalian cells. PLoS Biol 4(10):e309

    Google Scholar 

  117. Ramanathan S, Detwiler PB, Sengupta AM, Shraiman BI (2005) G-protein-coupled enzyme cascades have intrinsic properties that improve signal localization and fidelity. Biophys J 88(5):3063–71

    Google Scholar 

  118. Rao CV, Kirby JR, Arkin AP (2004) Design and diversity in bacterial chemotaxis: a comparative study in Escherichia coli and Bacillus subtilis. PLoS Biol 2(2):E49

    Google Scholar 

  119. Raser JM, O’Shea EK (2005) Noise in gene expression: origins, consequences, and control. Science 309(5743):2010–3

    Google Scholar 

  120. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–5

    ADS  Google Scholar 

  121. Rieke F, Baylor DA (1998) Single photon detection by rod cells of the retina. Rev Mod Phys 70:1027–1036

    ADS  Google Scholar 

  122. Roma DM, O’Flanagan R, Ruckenstein AE, Sengupta AM (2005) Optimal path to epigenetic swithcing. Phys Rev E 71:011902

    Google Scholar 

  123. Rosenfeld N, Alon U (2003) Response delays and the structure of transcription networks. J Mol Biol 329(4):645–54

    Google Scholar 

  124. Rosenfeld N, Young JW, Alon U, Swain PS, Elowitz MB (2005) Gene regulation at the single-cell level. Science 307(5717):1962–5

    ADS  Google Scholar 

  125. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M (2005) Towards a proteome-scale map of the human protein–protein interaction network. Nature 437(7062):1173–8

    ADS  Google Scholar 

  126. Rust MJ, Markson JS, Lane WS, Fisher DS, O’Shea EK (2007) Ordered phosphorylation giverns oscillation of a three-protein circadian clock. Science 318:809–812

    Google Scholar 

  127. Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721):523–9

    Google Scholar 

  128. Sanchez L, Thieffry D (2001) A logical analysis of the Drosophila gap-gene system. J Theor Biol 211(2):115–41

    Google Scholar 

  129. Sasai M, Wolynes PG (2003) Stochastic gene expression as a many-body problem. Proc Natl Acad Sci (USA) 100(5):2374–9

    ADS  Google Scholar 

  130. Schneidman E, Still S, Berry II MJ, Bialek W (2003) Network information and connected correlations. Phys Rev Lett 91(23):238701

    ADS  Google Scholar 

  131. Schneidman E, Berry II MJ, Segev R, Bialek W (2006) Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440(7087):1007–12

    ADS  Google Scholar 

  132. Schroeder MD, Pearce M, Fak J, Fan H, Unnerstall U, Emberly E, Rajewsky N, Siggia ED, Gaul U (2004) Transcriptional control in the segmentation gene network of Drosophila. PLoS Biol 2(9):E271

    Google Scholar 

  133. Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34(2):166–76

    Google Scholar 

  134. Setty Y, Mayo AE, Surette MG, Alon U (2003) Detailed map of a cis-regulatory input function. Proc Natl Acad Sci (USA) 100(13):7702–7

    ADS  Google Scholar 

  135. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423 & 623–656

    Google Scholar 

  136. Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31(1):64–8

    Google Scholar 

  137. Shlens J, Field GD, Gauthier JL, Grivich MI, Petrusca D, Sher A, Litke AM, Chichilnisky EJ (2006) The structure of multi-neuron firing patterns in primate retina. J Neurosci 26(32):8254–66

    Google Scholar 

  138. Sigal A, Milo R, Cohen A, Geva-Zatorsky N, Klein Y, Liron Y, Rosenfeld N, Danon T, Perzov N, Alon U (2006) Variability and memory of protein levels in human cells. Nature 444(7119):643–6

    ADS  Google Scholar 

  139. Siggia ED (2005) Computational methods for transcriptional regulation. Curr Opin Genet Dev 15(2):214–21

    MathSciNet  Google Scholar 

  140. Slonim N, Atwal GS, Tkačik G, Bialek W (2005) Information-based clustering. Proc Natl Acad Sci (USA) 102(51):18297–302

    Google Scholar 

  141. Slonim N, Elemento O, Tavazoie S (2006) Ab initio genotype-phenotype association reveals intrinsic modularity in genetic networks. Mol Syst Biol 2 (2006) 0005

    Google Scholar 

  142. Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci (USA) 100(21):12123–8

    ADS  Google Scholar 

  143. Stelling J, Gilles ED, Doyle 3rd FJ (2004) Robustness properties of circadian clock architectures. Proc Natl Acad Sci (USA) 101(36):13210–5

    ADS  Google Scholar 

  144. Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–76

    ADS  Google Scholar 

  145. Süel GM, Garcia-Ojalvo J, Liberman L, Elowitz MB (2006) An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440:545–550

    Google Scholar 

  146. Swain PS (2004) Efficient attenuation of stochasticity in gene expression through post-transcriptional control. J Mol Biol 344(4):965–76

    MathSciNet  Google Scholar 

  147. Swain PS, Elowitz MB, Siggia ED (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci (USA) 99(20):12795–800

    ADS  Google Scholar 

  148. Tanay A, Regev A, Shamir R (2005) Conservation and evolvability in regulatory networks: the evolution of ribosomal regulation in yeast. Proc Natl Acad Sci (USA) 102(20):7203–8

    ADS  Google Scholar 

  149. Thattai M, van Oudenaarden A (2001) Intrinsic noise in gene regulatory networks. Proc Natl Acad Sci (USA) 98(15):8614–9

    ADS  Google Scholar 

  150. Thomas R (1973) Boolean formalization of genetic control circuits. J Theor Biol 42(3):563–85

    Google Scholar 

  151. Tkačik G (2007) Information Flow in Biological Networks. Dissertation, Princeton University, Princeton

    Google Scholar 

  152. Tkačik G, Bialek W (2007) Diffusion, dimensionality and noise in transcriptional regulation. arXiv.org:07121852 [q-bio.MN]

    Google Scholar 

  153. Tkačik G, Schneidman E, Berry II MJ, Bialek W (2006) Ising models for networks of real neurons. arXiv.org:q-bio.NC/0611072

    Google Scholar 

  154. Tkačik G, Callan Jr CG, Bialek W (2008) Information capacity of genetic regulatory elements. Phys Rev E 78:011910. arXiv.org:0709.4209. [q-bioMN]

    Google Scholar 

  155. Tkačik G, Callan Jr CG, Bialek W (2008) Information flow and optimization in transcriptional regulation. Proc Natl Acad Sci 105(34):12265–12270. arXiv.org:0705.0313. [q-bio.MN]

    Google Scholar 

  156. Tkačik G, Gregor T, Bialek W (2008) The role of input noise in transcriptional regulation. PLoS One 3, e2774 arXiv.org:q-bioMN/0701002

    Google Scholar 

  157. Tomita J, Nakajima M, Kondo T, Iwasaki H (2005) No transcription‐translation feedback in circadian rhythm of kaic phosphorylation. Science 307:251–254

    ADS  Google Scholar 

  158. Tucker CL, Gera JF, Uetz P (2001) Towards an understanding of complex protein networks. Trends Cell Biol 11(3):102–6

    Google Scholar 

  159. Tyson JJ, Chen K, Novak B (2001) Network dynamics and cell physiology. Nat Rev Mol Cell Biol 2(12):908–16

    Google Scholar 

  160. Tyson JJ, Chen KC, Novak B (2003) Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15(2):221–31

    Google Scholar 

  161. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403(6770):623–7

    ADS  Google Scholar 

  162. de Visser JA, Hermisson J, Wagner GP, Ancel Meyers L, Bagheri-Chaichian H, Blanchard JL, Chao L, Cheverud JM, Elena SF, Fontana W, Gibson G, Hansen TF, Krakauer D, Lewontin RC, Ofria C, Rice SH, von Dassow G, Wagner A, Whitlock MC (2003) Perspective: Evolution and detection of genetic robustness. Evolution Int J Org Evolution 57(9):1959–72

    Google Scholar 

  163. Wagner A, Fell DA (2001) The small world inside large metabolic networks. Proc Biol Sci 268(1478):1803–10

    Google Scholar 

  164. Walczak AM, Sasai M, Wolynes PG (2005) Self-consistent proteomic field theory of stochastic gene switches. Biophys J 88(2):828–50

    Google Scholar 

  165. Waters CM, Bassler BL (2005) Quorum sensing: cell-to-cell communication in bacteria. Annu Rev Cell Dev Biol 21:319–46

    Google Scholar 

  166. Watson JD, Baker TA, Beli SP, Gann A, Levine M, Losick R (2003) Molecular Biology of the Gene: 5th edn. Benjamin Cummings, Menlo Park

    Google Scholar 

  167. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–2

    ADS  Google Scholar 

  168. Weinberger LS, Shenk T (2007) An hiv feedback resistor: auto-regulatory circuit deactivator and noise buffer. PLoS Biol 5(1):e9

    Google Scholar 

  169. Yeger-Lotem E, Sattath S, Kashtan N, Itzkovitz S, Milo R, Pinter RY, Alon U, Margalit H (2004) Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction. Proc Natl Acad Sci (USA) 101(16):5934–5939

    ADS  Google Scholar 

  170. Yokobayashi Y, Weiss R, Arnold FH (2002) Directed evolution of a genetic circuit. Proc Nat Acad Sci (USA) 99(26):16587–91

    ADS  Google Scholar 

  171. Young MW, Kay SA (2001) Time zones: a comparative genetics of circadian clocks. Nat Rev Genet 2(9):702–15

    Google Scholar 

  172. Zaslaver A, Mayo AE, Rosenberg R, Bashkin P, Sberro H, Tsalyuk M, Surette MG, Alon U (2004) Just-in-time transcription program in metabolic pathways. Nat Genet 36(5):486–91

    Google Scholar 

  173. Ziv E, Koytcheff R, Middendorf M, Wiggins C (2005a) Systematic identification of statistically significant network measures. Phys Rev E 71:016110

    ADS  Google Scholar 

  174. Ziv E, Middendorf M, Wiggins C (2005b) Information theoretic approach to network modularity. Phys Rev E 71:046117

    MathSciNet  ADS  Google Scholar 

  175. Ziv E, Nemenman I, Wiggins CH (2006) Optimal signal processing in small stochastic biochemical networks. arXiv.org:q-bio/0612041

    Google Scholar 

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Acknowledgments

We thank our colleagues and collaborators who have helped us learn about these issues: MJ Berry, CG Callan, T Gregor, JB Kinney, P Mehta, SE Palmer, E Schneidman, JJ Hopfield, T Mora, S Setayeshgar, N Slonim, GJ Stephens, DW Tank, N Tishby, A Walczak, EF Wieschaus, CH Wiggins and NS Wingreen. Our work was supported in part by NIH grants P50 GM071508 and R01 GM077599, by NSF Grants IIS–0613435 and PHY–0650617, by the Swartz Foundation, and by the Burroughs Wellcome Fund.

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  1. Joseph Henry Laboratories of Physics, Lewis–Sigler Institute for Integrative Genomics, Princeton University, Princeton, USA

    Gašper Tkačik & William Bialek

  2. Princeton Center for Theoretical Physics, Princeton University, Princeton, USA

    William Bialek

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  1. Gašper Tkačik
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  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Tkačik, G., Bialek, W. (2009). Cell Biology: Networks , Regulation and Pathways . In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_48

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