Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

In silico evolution of functional modules in biochemical networks

In silico evolution of functional modules in biochemical networks

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Understanding the large reaction networks found in biological systems is a daunting task. One approach is to divide a network into more manageable smaller modules, thus simplifying the problem. This is a common strategy used in engineering. However, the process of identifying biological modules is still in its infancy and very little is understood about the range and capabilities of motif structures found in biological modules. In order to delineate these modules, a library of functional motifs has been generated via in silico evolution techniques. On the basis of their functional forms, networks were evolved from four broad areas: oscillators, bistable switches, homeostatic systems and frequency filters. Some of these motifs were constructed from simple mass action kinetics, others were based on Michaelis–Menten kinetics as found in protein/protein networks and the remainder were based on Hill equations as found in gene/protein interaction networks. The purpose of the study is to explore the capabilities of different network architectures and the rich variety of functional forms that can be generated. Ultimately, the library may be used to delineate functional motifs in real biological networks.

References

    1. 1)
      • T.S. Gardner , C.R. Cantor , J.J. Collins . Construction of a genetic toggle switch in Escherichia coli. Nature , 339 - 342
    2. 2)
      • C.V. Rao , D.M. Wolf , A.P. Arkin . Control, exploitation and tolerance of intracellular noise. Nature , 231 - 237
    3. 3)
      • M. Shea , G. Ackers . The OR control system of bacteriophage lambda. A physical–chemical model for gene-regulation. J. Mol. Biol. , 211 - 230
    4. 4)
      • E.J. Doedel , H.B. Keller , J.P. Kernevez . Numerical analysis and control of bifurcation problems, Part I. Int. J. Bifurcation Chaos , 3 , 493 - 520
    5. 5)
      • H.M. Sauro , J.-H.S. Hofmeyr , J.M. Rohwer , J.L. Snoep . Jarnac: a system for interactive metabolic analysis, Animating the Cellular Map 9th International BioThermoKinetics Meeting.
    6. 6)
      • J.E. Ferrell . Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr. Opin. Chem. Biol. , 140 - 148
    7. 7)
      • J.M.T. Thompson , H.B. Stewart . (1986) Nonlinear dynamics and chaos.
    8. 8)
      • H.M. Sauro , B. Ingalls . Conservation analysis in biochemical networks: computational issues for software writers. Biophys. Chem. , 1 , 1 - 15
    9. 9)
      • L.H. Hartwell , J.J. Hopfield , S. Leibler , A.W. Murray . From molecular to modular cell biology. Nature , 47 - 52
    10. 10)
    11. 11)
    12. 12)
      • A. Deckard , H.M. Sauro . Preliminary studies on the in silico evolution of biochemical networks. Chem. BioChem. , 10 , 1423 - 1431
    13. 13)
      • P. Francois , V. Hakim . Design of genetic networks with specified functions by evolution in silico. PNAS , 2 , 580 - 585
    14. 14)
      • T. Wilhelm , R. Heinrich . Smallest chemical reaction system with Hopf bifurcation. J. Math. Chem. , 1 - 14
    15. 15)
      • J.W. Stucki . Stability analysis of biochemical systems – a practical guide. Prog. Biophys. Mol. Biol. , 2 , 99 - 187
    16. 16)
      • B.N. Kholodenko , A. Klyatkin , F.J. Bruggeman , E. Sontag , H.V. Westerhoff , J.B. Hoek . Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. PNAS , 12841 - 12846
    17. 17)
      • B.P. Ingalls . A frequency domain approach to sensitivity analysis of biochemical systems. J. Phys. Chem. , 1143 - 1152
    18. 18)
      • B.N. Kholodenko . Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem. , 6 , 1583 - 1588
    19. 19)
      • W.J. Blake , M. Kaern , C.R. Cantor , J.J. Collins . Noise in eukaryotic gene expression. Nature , 633 - 637
    20. 20)
      • V. Chickarmane , S.R. Paladugu , F. Bergmann , H.M. Sauro . Bifurcation discovery tool. Bioinformatics , 18 , 3688 - 3690
    21. 21)
      • J.M. Bower , H. Bolouri . (2001) Computational modeling of genetic and biochemical networks.
    22. 22)
      • T.M. Yi , Y. Huang , M.I. Simon , J. Doyle . Robust perfect adaptation in bacterial chemotaxis through integral feedback control. PNAS , 4649 - 4653
    23. 23)
      • W.J. Blake , F.J. Isaacs . Synthetic biology evolves. Trends Biotechnol. , 7 , 321 - 324
    24. 24)
      • M. Abramowitz , I.A. Stegun . (1964) Handbook of mathematical functions, Applied Mathematics Series.
    25. 25)
    26. 26)
    27. 27)
      • F. Sagues , I.R. Epstein . Nonlinear chemical dynamics. Dalton Trans. , 1201 - 1217
    28. 28)
      • G.F. Franklin , J.D. Powell , A. Emami-Naeini . (1991) Feedback control of dynamic systems.
    29. 29)
      • H.H. McAdams , A. Arkin . It's a noisy business! Genetic regulation at the nanomolar scale. Trends Genet. , 65 - 69
    30. 30)
      • D.M. Wolf , A.P. Arkin . Motifs, modules and games in bacteria. Curr. Opin. Microbiol. , 125 - 134
    31. 31)
      • A. Goldbeter . (1996) Biochemical oscillations and cellular rhythms.
    32. 32)
    33. 33)
      • H.M. Sauro , M. Hucka , A. Finney , C. Wellock , C. Bolouri , J. Doyle , H. Kitano . Next generation simulation tools: the systems biology workbench and biospice integration. OMICS , 4 , 355 - 372
    34. 34)
      • Y.A. Kuznetsov . (1995) Elements of applied bifurcation theory.
    35. 35)
      • E. Anderson , Z. Bai , C. Bischof , S. Blackford , J. Demmel , J. Dongarra , J. Du Croz , A. Greenbaum , S. Hammarling , A. McKenney , D. Sorensen . (1999) LAPACK users guide.
    36. 36)
      • D.E. Goldberg . (1999) Genetic algorithms in search, optimization and machine learning.
    37. 37)
      • J.F. Hervagault , S. Canu . Bistability and irreversible transitions in a simple substrate cycle. J. Theor. Biol. , 439 - 449
    38. 38)
      • J.J. Tyson , K.C. Chen , B. Novak . Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell. Biol. , 2 , 221 - 231
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-syb_20050096
Loading

Related content

content/journals/10.1049/ip-syb_20050096
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address