A control theoretic paradigm for cell signaling networks: A simple complexity for a sensitive robustness

Current Opinion in Chemical Biology

Volumn 10, Issue 1, 2006, Pages 81-87

  Araujo, Robyn P ^a   Liotta, Lance A ^a  

^a GEORGE MASON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CANCER DIAGNOSIS; CELL FUNCTION; HEALTH; MATHEMATICAL MODEL; MOLECULAR BIOLOGY; MOLECULAR INTERACTION; MOLECULE; PATHOPHYSIOLOGY; PROTEIN ENGINEERING; PROTEIN FUNCTION; PROTEIN INTERACTION; PROTEIN PROCESSING; REVIEW; SIGNAL TRANSDUCTION; STOCHASTIC MODEL; SYSTEMS BIOLOGY;

CELL PHYSIOLOGY; DATABASES; HUMANS; MODELS, BIOLOGICAL; SIGNAL TRANSDUCTION;

EID: 32044471506     PISSN: 13675931     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cbpa.2006.01.002     Document Type: Review

References (49)

1. D. Figeys Novel approaches to map protein interactions Curr Opin Biotechnol 14 2003 119 125
2. Geho DH, Petricoin EF, Liotta LA, Araujo RP: Modeling of protein signaling networks in clinical proteomics. Cold Spring Harbor 2005, in press.
3. N.L. Anderson, A.D. Matheson, and S. Steiner Proteomics: applications in basic and applied biology Curr Opin Biotechnol 11 2000 408 412
4. V. Espina, A.I. Mehta, M.E. Winters, V.S. Calvert, J.D. Wulfkuhle, E.F. Petricoin, and L.A. Liotta Protein microarrays: molecular profiling technologies for clinical specimens Proteomics 3 2003 2091 2100
5. J. Wulfkuhle, V. Espina, L.A. Liotta, and E.F. Petricoin Genomic and proteomic technologies for individualisation and improvement of cancer treatment Eur J Cancer 40 2004 2623 2632
6. L.A. Liotta, V. Espina, A.I. Mehta, V.S. Calvert, K.P. Rosenblatt, D.H. Geho, P.J. Munson, L. Young, J.D. Wulfkuhle, and E.F. Petricoin Protein microarrays: meeting analytical challenges for clinical applications Cancer Cell 3 2003 317 325
7. S.J. Vayttaden, S.M. Ajay, and U.S. Bhalla A spectrum of models of signaling pathways ChemBioChem 5 2004 1365 1374
8. J.J. Tyson, K.C. Chen, and B. Novak Network dynamics and cell physiology Nat Rev Mol Cell Biol 2 2001 908 916
9. R. Speer, J.D. Wulfkuhle, L.A. Liotta, and E.F. Petricoin Reverse-phase protein microarrays for tissue-based analysis Curr Opin Mol Ther 7 2005 240 245
10. B. Vogelstein, and K.W. Kinzler Cancer genes and the pathways they control Nat Med 10 2004 789 799
11. E.C. Kohn, Y. Lu, H. Wang, Q. Yu, S. Yu, H. Hall, D.L. Smith, F. Meric-Bernstam, and G.B. Mills Molecular therapeutics: promise and challenges Semin Oncol 31 2004 39 53
12. W. Kolch, M. Calder, and D. Gilbert When kinases meet mathematics: the systems biology of MAPK signalling FEBS Lett 579 2005 1891 1895 This is an excellent review of the kinetics of the MAPK cascade. The authors present a thorough treatment of the control strategies germane to this pathway, considering the roles of motifs such as multi-tiered cascades, multi-site phosphorylations, feedback loops, scaffolds and localization.
13. N.J. Eungdamrong, and R. Iyengar Modeling cell signaling networks Biol Cell 96 2004 355 362
14. O. Wolkenhauer, M. Ullah, W. Kolch, and K-H. Cho Modeling and simulation of intracellular dynamics: choosing an appropriate framework IEEE Trans Nanobioscience 3 2004 200 207 This interesting article compares the two main classes of mathematical modeling approaches within the domain of systems biology, namely stochastic simulations or deterministic rate equation models. The authors present a critical discussion of some of the arguments for and against these two approaches, and highlight various subtle differences and relationships that have been ignored in prior discussions on the subject in the literature.
15. N.J. Eungdamrong, and R. Iyengar Computational approaches for modeling regulatory cellular networks Trends Cell Biol 14 2004 661 669
16. M. Arita Scale-freeness and biological networks J Biochem (Tokyo) 138 2005 1 4
17. N.V. Dokholyan The architecture of the protein domain universe Gene 347 2005 199 206
18. A-L. Barabasi, and Z.N. Oltvai Network biology: understanding the cell's functional organization Nat Rev Genet 5 2004 101 113
19. A-L. Barabasi, and E. Bonabeau Scale-free networks Sci Am 288 2003 60 69
20. A. Chatterjee, K. Mayawala, J.S. Edwards, and D.G. Vlachos Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method Bioinformatics 21 2005 2136 2137
21. L. Salwinski, and D. Eisenberg In Silico simulation of biological network dynamics Nat Biotechnol 22 2004 1017 1019
22. S.A. Johnson, and T. Hunter Kinomics: methods for deciphering the kinome Nat Methods 2 2005 17 25
23. B.N. Kholodenko, A. Kiyatkin, F.J. Bruggeman, E.D. Sontag, H.V. Westeroff, and J.B. Hoek Untangling the wires: a strategy to trace functional interactions in signaling and gene networks Proc Natl Acad Sci USA 99 2002 12841 12846
24. J.A. Papin, T. Hunter, B.O. Palsson, and S. Subramaniam Reconstruction of cellular signalling networks and analysis of their properties Nat Rev Mol Cell Biol 6 2005 99 111
25. B.M. Slepchenko, J.C. Schaff, I. Macara, and L.M. Loew Quantitative cell biology with the virtual cell Trends Cell Biol 13 2003 570 576
26. T. Lipniacki, P. Paszek, A.R. Brasier, B. Luxon, and M. Kimmel Mathematical model of NF-kappaB regulatory module J Theor Biol 228 2004 195 215
27. U.S. Bhalla, and R. Iyengar Emergent properties of networks of biological signaling pathways Science 283 1999 381 387
28. M.H.V. Van Regenmortel Reductionism and complexity in molecular biology EMBO Rep 5 2004 1016 1020
29. O. Wolkenhauer, S.N. Sreenath, P. Wellstead, M. Ullah, and K-H. Cho A systems- and signal-oriented approach to intracellular dynamics Biochem Soc Trans 33 2005 507 515 These authors present some general principles of feedback regulation and control to highlight the centrality of feedback to molecular biology. A set of dynamic models is developed for some representative cell signaling processes, including feedback regulation of the Ras-Raf-Mek-Erk pathway
30. O. Wolkenhauer, M. Ullah, P. Wellstead, and K-H. Cho The dynamic systems approach to control and regulation of intracellular networks FEBS Lett 579 2005 1846 1853
31. A.D. Weston, and L. Hood Systems biology, proteomics, and the future of health care: toward predictive, preventative and personalized medicine J Proteome Res 3 2004 179 196
32. C.J. Tomlin, and J.D. Axelrod Understanding biology by reverse engineering the control Proc Natl Acad Sci USA 102 2005 4219 4220
33. Araujo RP, Petricoin EF, Liotta LA: Illuminating the cancer cell's control circuitry: paving the way to individualized therapeutic strategies. Curr Signal Transduct Ther 2006, in press. These authors introduce the concept of the 'intelligent' functioning of the cell being evoked at the intersection of two pivotal paradoxes - 'simplicity within complexity' and 'sensitivity within robustness'. A mathematical model of a 'feedback funambulism' is presented emphasizing a 'minimalist modularization' approach to network modeling. The concept of 'control-oriented therapeutic strategies' is presented, outlining why a control theoretic paradigm is the key to developing effective treatment strategies against diseases of signal transduction such as cancer.
34. A.R. Reynolds, C. Tischer, P.J. Verveer, O. Rocks, and PI.H. Bastiaens EGFR activation coupled to inhibition of tyrosine phosphatases causes lateral signal propagation Nat Cell Biol 5 2003 447 453
35. J.D. Jordan, E.M. Landau, and R. Iyengar Signaling networks: the origins of cellular multitasking Cell 103 2000 193 200
36. S. Bornholdt Less is more in modeling large genetic networks Science 310 2005 449 451
37. J.E. Ferrell Tripping the switch fantastic: how a protein kinase cascade can convert graded inputs into switch-like outputs Trends Biochem Sci 21 1996 460 466
38. A. Goldbeter, and D.E. Koshland An amplified sensitivity arising from covalent modification in biological systems Proc Natl Acad Sci USA 78 1981 6840 6844
39. H.M. Sauro, and B.N. Kholodenko Quantitative analysis of signaling networks Prog Biophys Mol Biol 86 2004 5 43
40. J.J. Tyson, K.C. Chen, and B. Novak Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell Curr Opin Cell Biol 15 2003 221 231 This is an outstanding review of all the major control archetypes germane to the regulation of gene and protein networks, illustrating how physiological regulatory systems may be understood in mathematical terms. Some composite control motifs are also presented, including activator-inhibitor oscillators and substrate-depletion oscillators. The article provides a captivating glimpse into the parallels between the control of cell signaling networks and electrical circuits and other modern engineering systems.
41. D. Angeli, J.E. Ferrell, and E.D. Sontag Detection of multistability, bifurcations and hysteresis in a large class of biological positive-feedback systems Proc Natl Acad Sci USA 101 2004 1822 1827
42. W. Xiong, and J.E. Ferrell A positive-feedback-based bistable 'memory module' that governs a cell fate decision Nature 426 2003 460 465
43. J.E. Ferrell, and W. Xiong Bistability in cell signaling: how to make continuous processes discontinuous, and reversible processes irreversible Chaos 11 2001 227 236
44. S.R. Neves, and R. Iyengar Modeling of signaling networks BioEssays 24 2002 1110 1117
45. S. Crosson, P.T. McGrath, C. Stephens, H.H. McAdams, and L. Shapiro Conserved modular design of an oxygen sensory/signaling network with species-specific output Proc Natl Acad Sci USA 102 2005 8018 8023
46. A.W. Rives, and T. Galitski Modular organization of cellular networks Proc Natl Acad Sci USA 100 2003 1128 1133
47. O. Brandman, J.E. Ferrell, R. Li, and T. Meyer Interlinked fast and slow positive feedback loops drive reliable cell decisions Science 310 2005 496 498 These authors catalogue many signaling pathways that rely on a multiplicity of positive feedback loops rather than a single positive feedback loop. They go on to propose a simple mathematical model to address the question: why combine two switches when a single one will carry out exactly the same function, namely to create a bistable switch? The model reveals the fascinating finding that the combination of the two loops can make switching faster while reducing signal noise: a slow loop fosters robustness of the alternative steady states, while a fast loop enhances the speed of switching between the two states.
48. Q. Chang, Y. Li, M.F. White, J.A. Fletcher, and S. Xiao Constitutive activation of insulin receptor substrate 1 is a frequent event in human tumors: therapeutic implications Cancer Res 62 2002 6035 6038
49. P. Gual, Y. Le Marchand-Brustel, and J-F. Tanti Positive and negative regulation of insulin signaling through IRS-1 phosphorylation Biochimie 87 2005 99 109