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PLOS ONE 2013

Attractor Metabolic Networks

DOI: 10.1371/journal.pone.0058284

Ildefonso M. De la Fuente, Jesus M. Cortes, David A. Pelta, Juan Veguillas

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Abstract:

Background The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. Methodology/Principal Findings In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. Conclusions/Significance We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed.

References

[1]	Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL (2000) The large-scale organization of metabolic networks. Nature 407: 651–654.
[2]	Sear RP (2005) The cytoplasm of living cells: a functional mixture of thousands of components. J. Phys.: Condens. Matter 17: S3587–S3595.
[3]	Pang CN, Krycer JR, Lek A, Wilkins MR (2008) Are protein complexes made of cores, modules and attachments? Proteomics 8 (3): 425–34.
[4]	Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, et al. (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415: 141–147.
[5]	Bobik TA (2006) Polyhedral organelles compartmenting bacterial metabolic processes. Appl. Microbiol. Biotechnol. 70: 517–525.
[6]	Yeates TO, Kerfeld CA, Heinhorst S, Cannon GC, Shively JM (2008) Protein-based organelles in bacteria: Carboxysomes and related microcompartments. Nat. Rev. Microbiol. 6: 681–691.
[7]	Sutter M, Boehringer D, Gutmann S, Günther S, Prangishvili D, et al. (2008) Structural basis of enzyme encapsulation into a bacterial nanocompartment. Nat. Struct. Mol. Biol. 15: 939–947.
[8]	Glick BS (2007) Let there be order. Nature Cell Biol. 9: 130–132.
[9]	De la Fuente IM (2010) Quantitative Analysis of Cellular Metabolic Dissipative, Self-Organized Structures. Int. J. Mol. Sci. 11(9): 3540–3599.
[10]	De la Fuente IM (2013) Metabolic Dissipative Structures. In: Systems Biology of Metabolic and signaling Networks: Energy, Mass and Information Transfer (eds. MA Aon, V Saks & U Schlattner). Springer Books. New York. (in press)
[11]	De La Fuente IM, Benítez N, Santamaría A, Veguillas J, Aguirregabiria JM (1999) Persistence in metabolic nets. Bull. Mathemat. Biol. 61: 573–595.
[12]	De La Fuente IM, Martínez L, Pérez-Samartín AL, Ormaetxea L, Amezaga C, et al.. (2008) Global Self-organization of the cellular metabolic structure. Plos One 3: e3100, 1–19.
[13]	Nelson LD, Cox MM (2004) Lehninger Principles of Biochemistry. Publisher: W. H. Freeman.
[14]	De la Fuente IM, Cortes J, Perez-Pinilla M, Ruiz-Rodriguez V, Veguillas J (2011) The metabolic core and catalytic switches are fundamental elements in the self-regulation of the Systemic Metabolic Structure of Cells. Plos One 6: e2722.
[15]	De La Fuente IM, Vadillo F, Pérez-Samartín AL, Pérez-Pinilla M-B, Bidaurrazaga J, et al. (2010) Global self-regulations of the cellular metabolic structure. PlosOne. 5, e9484 1–e9484: 15.
[16]	Almaas E, Oltvai ZN, Barabasi AL (2005) The activity reaction core and plasticity of metabolic networks. PLoS Comput Biol 1(7): e68, 0557–563.
[17]	Almaas E (2007) Biological impacts and context of network theory. J. Exp. Biol. 210: 1548–1558.
[18]	Mogilevskaya E, Demin O, Goryanin I (2006) Kinetic Model of Mitochondrial Krebs Cycle: Unraveling the Mechanism of Salicylate Hepatotoxic Effects.Journal of Biological Physics. 32: 245–271.
[19]	Yang CR, Shapiro BE, Hung SP, Mjolsness ED, Hatfield GW (2005) A mathematical model for the branched chain amino acid biosynthetic pathways of Escherichia coli K12. J Biol Chem. 280: 11224–32.
[20]	Korzeniewski B, Zoladz JA (2001) A model of oxidative phosphorylation in mammalian skeletal muscle. Biophys Chem 92: 17–34.
[21]	Bier M, Teusink B, Kholodenko BN, Westerhoff HV (1996) Control analysis of glycolytic oscillations. Biophysical Chemistry 62: 15–24.
[22]	Kass L, Bray WO (1996) Kinetic model for phototransduction and G-protein enzyme cascade: understanding quantal bumps during inhibition of CaM-KII or PP2B. Journal of Photochemistry and Photobiology B: Biology 35: 105– I 13.
[23]	Gonze D, Halloy J, Goldbeter A (2004) Stochastic models for circadian oscillations: Emergence of a biological rhythm. Int J. Quantum Chem. 98: 228–238.
[24]	Tyson JJ (1991) Modeling the cell division cycle: cdc2 and cyclin interactions. PNAS 88: 7328–7332.
[25]	De la Fuente IM, Martínez L, Veguillas J (1995) Dynamic Behavior in Glycolytic Oscillations with Phase Shifts. Biosystems 35: 1–13.
[26]	De la Fuente IM, Cortes JM (2012) Quantitative Analysis of the Effective Functional Structure in Yeast Glycolysis. PLoS ONE 7(2): e30162.
[27]	De la Fuente IM, Martínez L, Veguillas J, Aguirregabiria JM (1996) Quasiperiodicity Route to Chaos in a Biochemical System. Biophys. J. 71: 2375–2379.
[28]	De la Fuente IM, Martínez L, Veguillas J, Aguirregabiria JM (1998) Coexistence of multiple periodic and chaotic regimes in biochemical oscillations. Acta Biotheoretica 46: 37–51.
[29]	De la Fuente IM, Martínez L, Veguillas J (1996) Intermittency Route to Chaos in a Biochemical System. Biosystems 39: 87–92.
[30]	De la Fuente IM (1999) Diversity of temporal self-organized behaviors in a biochemical system. BioSystems 50: 83–97.
[31]	De la Fuente IM, Martínez L, Aguirregabiria JM, Veguillas J (1998) R/S analysis in strange attractors. Fractals 6(2): 95–100.
[32]	De la Fuente IM, Martínez L, Benitez N, Veguillas J, Aguirregabiria JM (1998) Persistent behavior in a phase-shift sequence of periodical biochemical oscillations. Bulletin of Mathematical Biology. 60(4): 689–702.
[33]	De la Fuente IM, Martínez L, Aguirregabiria JM, Veguillas J, Iriarte M (1999) Long-range correlations in the phase-shifts of numerical simulations of biochemical oscillations and in experimental cardiac rhythms. Journal of Biological Systems 7(2): 113–130.
[34]	Goldbeter A (2007) Biological rhythms as temporal dissipative structures. Advances in Chemical Physics 135: 253–295.
[35]	Almaas E, Kovacs B, Vicsek T, Oltvai ZN, Barabási AL (2004) Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427: 839–843.
[36]	De La Fuente IM, Vadillo F, Pérez-Pinilla M-B, Vera-López A, Veguillas J (2009) The number of catalytic elements is crucial for the emergence of metabolic cores. PlosOne 4, e7510 1–e7510: 11.
[37]	Viswanathan GM, Buldyrev SV, Havlin S, Stanley HE (1997) Quantification of DNA patchiness using long-range correlation measures. Biophys J 72: 866–875.
[38]	Eke A, Herman P, Kocsis L, Kozak LR (2002) Fractal characterization of complexity in temporal physiological signals. Physiol Meas 23: R1–R38.
[39]	Goldberger AL, Amaral LA, Hausdorff JM, Ivanov PCH, Peng CK, et al. (2002) Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 99: 2466–2472.
[40]	Ramanujan VK, Biener G, Herman B (2006) Scaling Behavior in Mitochondrial Redox Fluctuations. Biophys J 90: L70–L72.
[41]	Allegrini P, Buiatti M, Grigolini P, West BJ (1988) Fractal brownian motion as non stationary process: An alternative paradigm for DNA sequences. Phys Rev E 57: 4558–4562.
[42]	Audit B, Vaillant C, Arné A, D’Aubenton-Caraf Y, Thermes C (2004) Wavelet Analysis of DNA Bending Profiles reveals Structural Constraints on the Evolution of Genomic Sequences. Journal of Biological Physics 30: 33–81.
[43]	Kazachenko VN, Astashev ME, Grinevic AA (2007) Multifractal analysis of K+ channel activity.Biochemistry (Moscow). 2: 169–175.
[44]	Aon MA, Roussel MR, Cortassa S, O’Rourke B, Murray DB, et al.. (2008) The scale-free dynamics of eukaryotic cells. Plos One 3: e3624, 1–12.
[45]	Mahasweta D, Gebber GL, Barman SM, Lewis CD (2003) Fractal Properties of Sympathetic Nerve Discharge. J Neurophysiol 89: 833–840.
[46]	De la Fuente IM, Pérez-Samartín A, Martínez L, García MA, Vera-López A (2006) Long-range correlations in rabbit brain neural activity. Annals of Biomedical Engineering 34: 295–299.
[47]	Goldbeter A, Lefever R (1972) Dissipative structures for an allosteric model. Application to glycolytic oscillations.Biophys. J. 12: 1302–1315.
[48]	Goldbeter A (1990) Rythmes et chaos dans les systemes biochimiques et cellulaires. Masson, Paris.
[49]	Peretto P (1992) An Introduction to the Modeling of Neural Networks. Cambridge University Press.
[50]	Amit DJ (1992) Modeling Brain Function: The World of Attractor Neural Networks. Cambridge University Press.
[51]	Hertz JA, Krogh AS, Palmer RG (1991) Introduction to the Theory of Neural Computation. Santa Fe Institute Series.
[52]	Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci USA 79(8): 2554–2558.
[53]	Amit DJ, Gutfreund H, Sompolinsky H (1987) Statistical Mechanics of Neural Networks Near Saturation. Ann Phys 173: 30–67.
[54]	Ackley DH, Hinton GE, Sejnowski TJ (1985) A Learning Algorithm for Boltzmann Machines. Cognitive Sci 9 (1): 147–169.
[55]	Hinton GH (2007) Scholarpedia. 2: 1668.
[56]	Roudi Y, Tyrcha J, Hertz J (2009) The ising model for neural data: model quality and approximate methods for extracting functional connectivity. Phys Rev E 79: 051915.
[57]	Kappen HJ, Rodriguez FB (1998) Efficient Learning in Boltzmann Machines Using Linear Response Theory. Neural Comput 10: 1137–1156.
[58]	Cover TM, Thomas JA (2006) Elements of Information Theory. Wiley.
[59]	Nocedal J, Wright SJ, Mit DJ (1999) Numerical Optimization. Springer-Verlag New York, Inc.
[60]	Boyd S, Vandenberghe L (2004) Convex Optimization. Cambridge University Press, New York.
[61]	Cormen TH, Leiserson CE, Rivest LR, Stein C (2009) Introduction to Algorithms. The MIT. Press.
[62]	Ashlock D (2006) Evolutionary Computation for Modeling and Optimization. Springer.
[63]	Willshaw DJ, Buneman OP, Longuet-Higgins HC (1969) Non-holographic associative memory. Nature: 222, 960–962.
[64]	Kohonen T (1972) Correlation Matrix Memories. IEEE Trans Comput C-21: 353–359.
[65]	Sejnowski TJ (1977) Storing Covariance with Nonlinearly Interacting Neurons. J Math Biol 4: 203–211.
[66]	Sejnowski TJ (1977) Storing Covariance with Nonlinearly Interacting Neurons. J Math Biol 4: 203–211.
[67]	Wills TJ, Lever C, Cacucci F, Burgess N, O’Keefe J (2005) Attractor Dynamics in the Hippocampal Representation of the Local Environment. Science 308: 873–876.
[68]	Marro J, Dickman R (1999) Nonequilibrium Phase Transitions in Lattice Models. Cambridge University Press, Cambridge.
[69]	Greve A, Davidson DI, van Rossum MCW (2010) A single-trace dual-process model of episodic memory: a novel computational account of familiarity and recollection. Hippocampus 20: 235–251.
[70]	Bogacz R, Brown MW (2003) Comparison of computational models of familiarity discrimination in the perirhinal cortex. Hippocampus 13: 494–524.
[71]	Greve A, Sterratt DC, Donaldson DI, Willshaw DJ, van Rossum MCW (2009) Optimal learning rules for familiarity detection. Biological Cybernetics 100: 11–19.
[72]	Cortes JM, Greve A, Barrett AB, van Rossum MCW (2010) Dynamics and robustness of familiarity memory. Neural Computation 22: 448–466.
[73]	Weiner JH, Li L (2008) Proteome of the Escherichia coli envelope and technological challenges in membrane proteome analysis. Biochim Biophys Acta 1778(9): 1698–713.
[74]	Csaba P, Balázs P, Martin J, Lercher P, Csermely S, et al. (2006) Chance and necessity in the evolution of minimal metabolic networks. Nature 440: 667–670.
[75]	Karp PD (2001) Pathway Databases: A Case Study in Computational Symbolic Theories. Science 293: 2040–2044.
[76]	Milo R, Jorgensen P, Moran U, Weber G, Springer M (2010) BioNumbers-the database of key numbers in molecular and cell biology. Nucleic Acids Res. 38 (Database issue): D750– D753. Available: http://bionumbers.hms.harvard.edu/bionum？ber.aspx?&id=100084&ver=3.
[77]	Bremer H, Dennis PP (1996) Modulation of chemical composition and other parameters of the cell by growth rate. In: Frederick C, Neidhart ED, editors. Escherichia coli and Salmonella.
[78]	Cech T (2000) Structural biology. The ribosome is a ribozyme. Science 289(5481): 878–9.
[79]	Bremer H, Dennis PP (1996) Modulation of chemical composition and other parameters of the cell by growth rate. In: Escherichia coli and Salmonella: Cellular and Molecular Biology. ASM Press. Washington DC.
[80]	Van Bogelen RA, Abshire KZ, Pertsemlidis A, Clark RL (1996) In Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology, Edited by F.C. Neidhardt, American Society for Microbiology (Washington, D.C.).
[81]	Russell PJ (2002) iGenetics, Chapter 5,16. Benjamin Cumming, San Francisco.
[82]	Cai SJ, Inouye M (2002) EnvZ-OmpR interaction and osmoregulation in Escherichia coli. J Biol Chem. 277(27): 24155–61.
[83]	Stein LD (2004) Human Genome: End of the Beginning. Nature 431: 915–916.
[84]	Lane N, Martin W (2010) The energetics of genome complexity. Nature. 467(7318): 929–34.
[85]	Shirakawa T, Gunji YP (2007) Emergence of morphological order in the network formation of Physarum polycephalum. Biophys Chem. 128(2–3): 253–60.
[86]	Kessler D (1982) Plasmodial structure and motility. In Cell biology of Physarum and Didymium, eds H. C. Aldrich and J. W. Daniel. Australia: Academic Press. Sydney.
[87]	Ueda T, Matsumoto K, Kobatake Y (1986) Spatial and temporal organization of intracellular adenine nucleotides and cyclic nucleotides in relation to rhythmic motility in physarum plasmodium. Experimental Cell Research162 (2): 486–494.
[88]	Nakagaki T, Yamada H, Toth A (2000) Maze-solving by an amoeboid organism. Nature 407: 470–470.
[89]	Nakagaki T, Yamada H, Toth A (2001) Path-finding by tube morphogenesis in an amoeboid organism. Biophys. Chem. 92: 47–52.
[90]	Miyaji T, Ohnishi I (2008) Physarum can solve the shortest path problem on riemannian surface mathematically rigourously. International Journal of Pure and Applied Mathematics 47(3): 353–369.
[91]	Nakagaki T, Yamada H, Hara M (2004) Smart network solutions in an amoeboid organism. Biophys Chem. 107(1): 1–5.
[92]	Nakagaki T, Kobayashi R, Ueda T, Nishiura Y (2004) Obtainning multiple separate food sources: Behavioural intelligence in the Physarum plasmodium. Proc. R. Soc. Lond. B 271: 2305–2310.
[93]	Saigusa T, Tero A, Nakagaki T, Kuramoto Y (2008) Amoebae anticipate periodic events. Phys Rev Lett. 100(1): 018101.
[94]	Tsuda S, Zauner KP, Gunji YP (2007) Robot control with biological cells. Biosystems. 87(2–3): 215–223.
[95]	Gough J, Jones G, Lovell C, Macey P, Morgan H, et al. (2009) Integration of Cellular Biological Structures Into Robotic Systems. European Space Agency Acta Futura 3: 43–49.
[96]	Dussutour A, Latty T, Beekman M, Simpson SJ (2010) Amoeboid organism solves complex nutritional challenges. Proc. Natl. Acad. Sci. 107(10): 4607–4611.
[97]	Bonner JT (2010) Brainless behavior: a myxomycete chooses a balanced diet. Proc. Natl. Acad. Sci. 107(12): 5267–8.
[98]	Latty T, Beekman M (2011) Speed-accuracy trade-offs during foraging decisions in the acellular slime mould Physarum polycephalum. Proc. Biol. Sci. 278(1705): 539–45.
[99]	Tero A, Takagi S, Saigusa T, Ito K, Bebber DP, et al. (2010) Rules for Biologically Inspired Adaptive Network Design. Science 327: 439–442.
[100]	Marwan W (2010) Amoeba-inspired network design. Science 327: 419–20.
[101]	Pershin YV, La Fontaine S, Di Ventra M (2009) Memristive model of amoeba learning. Phys Rev E Stat Nonlin Soft Matter Phys. 80: 021926.
[102]	Nakagaki T (2001) Smart behavior of true slime mold in a labyrinth. Res Microbiol. 152(9): 767–70.
[103]	Nakagaki T, Guy RD (2008) Intelligent behaviors of amoeboid movement based on complex dynamics of soft matter. Soft Matter, Vol. 4, 57–67.
[104]	Jennings HS (1905) Behavior of Lower Organisms. Reprint Edition, Indiana University Press, Bloomington.
[105]	Albrecht E, Petty RH (1998) Cellular memory: Neutrophil orientation reverses during temporally decreasing chemoattractant concentrations. Proc. Natl. Acad. Sci. USA. 95: 5039–5044.
[106]	Li L, N？rrelykke SF, Cox EC (2008) Persistent Cell Motion in the Absence of External Signals: A Search Strategy for Eukaryotic Cells. PLoS ONE 3(5): e2093.
[107]	Sidiropoulou K, Lu FM, Fowler MA, Xiao R, Phillips C, et al. (2009) Dopamine modulates an mGluR5-mediated depolarization underlying prefrontal persistent activity. Nat Neurosci. 12(2): 190–9.

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