A queueing approach to multi-site enzyme kinetics

Multi-site enzymes, defined as where multiple substrate molecules can bind simultaneously to the same enzyme molecule, play a key role in a number of biological networks, with the Escherichia coli protease ClpXP a well-studied example. These enzymes can form a low latency ‘waiting line’ of substrate to the enzyme's catalytic core, such that the enzyme molecule can continue to collect substrate even when the catalytic core is occupied. To understand multi-site enzyme kinetics, we study a discrete stochastic model that includes a single catalytic core fed by a fixed number of substrate binding sites. A natural queueing systems analogy is found to provide substantial insight into the dynamics of the model. From this, we derive exact results for the probability distribution of the enzyme configuration and for the distribution of substrate departure times in the case of identical but distinguishable classes of substrate molecules. Comments are also provided for the case when different classes of substrate molecules are not processed identically.

[1]  Gábor Lente,et al.  Stochastic mapping of the Michaelis-Menten mechanism. , 2012, The Journal of chemical physics.

[2]  Jaime E. Santos,et al.  Renewal processes and fluctuation analysis of molecular motor stepping , 2005, Physical biology.

[3]  B. Bukau,et al.  Targeted delivery of an ssrA-tagged substrate by the adaptor protein SspB to its cognate AAA+ protein ClpX. , 2003, Molecular cell.

[4]  Wei Min,et al.  Single-molecule Michaelis-Menten equations. , 2005, The journal of physical chemistry. B.

[5]  James R. Jackson,et al.  Jobshop-Like Queueing Systems , 2004, Manag. Sci..

[6]  Jeff Hasty,et al.  Fast stochastic algorithm for simulating evolutionary population dynamics , 2012, Bioinform..

[7]  G. Briggs,et al.  A Note on the Kinetics of Enzyme Action. , 1925, The Biochemical journal.

[8]  T. Baker,et al.  Versatile modes of peptide recognition by the ClpX N domain mediate alternative adaptor‐binding specificities in different bacterial species , 2010, Protein science : a publication of the Protein Society.

[9]  Robert T Sauer,et al.  AAA+ proteases: ATP-fueled machines of protein destruction. , 2011, Annual review of biochemistry.

[10]  T. Baker,et al.  Nucleotide-dependent substrate handoff from the SspB adaptor to the AAA+ ClpXP protease. , 2004, Molecular cell.

[11]  Soma Saha,et al.  Single-molecule enzyme kinetics in the presence of inhibitors. , 2012, The Journal of chemical physics.

[12]  Sanghyuk Lee,et al.  PubMine: An Ontology-Based Text Mining System for Deducing Relationships among Biological Entities , 2011 .

[13]  P. Burke The Output of a Queuing System , 1956 .

[14]  Tao Jia,et al.  Intrinsic noise in stochastic models of gene expression with molecular memory and bursting. , 2011, Physical review letters.

[15]  Carl M. Harris,et al.  Fundamentals of queueing theory , 1975 .

[16]  Frank Kelly,et al.  Reversibility and Stochastic Networks , 1979 .

[17]  M. Bennett,et al.  Transient dynamics of genetic regulatory networks. , 2007, Biophysical journal.

[18]  E. Cox,et al.  Real-Time Kinetics of Gene Activity in Individual Bacteria , 2005, Cell.

[19]  C. Kandemir-Cavas,et al.  An Application of Queueing Theory to the Relationship Between Insulin Level and Number of Insulin Receptors , 2007 .

[20]  J. Peccoud,et al.  Markovian Modeling of Gene-Product Synthesis , 1995 .

[21]  C. Georgopoulos,et al.  Isolation and characterization of ClpX, a new ATP-dependent specificity component of the Clp protease of Escherichia coli. , 1993, The Journal of biological chemistry.

[22]  T. Baker,et al.  Flexible linkers leash the substrate binding domain of SspB to a peptide module that stabilizes delivery complexes with the AAA+ ClpXP protease. , 2003, Molecular cell.

[23]  Antoine M. van Oijen,et al.  Ever-fluctuating single enzyme molecules: Michaelis-Menten equation revisited , 2006, Nature chemical biology.

[24]  Frank Kelly,et al.  Networks of queues with customers of different types , 1975, Journal of Applied Probability.

[25]  Erol Gelenbe,et al.  G-Networks Based Two Layer Stochastic Modeling of Gene Regulatory Networks with Post-Translational Processes , 2011 .

[26]  J. Tóth,et al.  A full stochastic description of the Michaelis-Menten reaction for small systems. , 1977, Acta biochimica et biophysica; Academiae Scientiarum Hungaricae.

[27]  Ruth J. Williams,et al.  Correlation resonance generated by coupled enzymatic processing. , 2010, Biophysical journal.

[28]  Robert T Sauer,et al.  SspB delivery of substrates for ClpXP proteolysis probed by the design of improved degradation tags. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[29]  C. Gardiner Handbook of Stochastic Methods , 1983 .

[30]  T. Schweder,et al.  Regulation of Escherichia coli starvation sigma factor (sigma s) by ClpXP protease , 1996, Journal of bacteriology.

[31]  F. Kelly,et al.  Networks of queues , 1976, Advances in Applied Probability.

[32]  T. Baker,et al.  Proteomic discovery of cellular substrates of the ClpXP protease reveals five classes of ClpX-recognition signals. , 2003, Molecular cell.

[33]  Hong Qian,et al.  Single-molecule enzymology: stochastic Michaelis-Menten kinetics. , 2002, Biophysical chemistry.

[34]  M. Eck,et al.  Structural basis of degradation signal recognition by SspB, a specificity-enhancing factor for the ClpXP proteolytic machine. , 2003, Molecular cell.

[35]  Jeff Hasty,et al.  Factorized time-dependent distributions for certain multiclass queueing networks and an application to enzymatic processing networks , 2011, Queueing Syst. Theory Appl..

[36]  T. Hwa,et al.  Stochastic fluctuations in metabolic pathways , 2007, Proceedings of the National Academy of Sciences.

[37]  J. Raser,et al.  Control of Stochasticity in Eukaryotic Gene Expression , 2004, Science.

[38]  Tao Jia,et al.  Applications of Little's Law to stochastic models of gene expression. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[39]  F. A. Davidson,et al.  Quasi-steady state assumptions for non-isolated enzyme-catalysed reactions , 2004, Journal of mathematical biology.

[40]  Lee A. Segel,et al.  On the validity of the steady state assumption of enzyme kinetics , 1988 .

[41]  T. Baker,et al.  Altered Tethering of the SspB Adaptor to the ClpXP Protease Causes Changes in Substrate Delivery* , 2007, Journal of Biological Chemistry.

[42]  Ming Yi,et al.  Michaelis-Menten mechanism for single-enzyme and multi-enzyme system under stochastic noise and spatial diffusion , 2010 .

[44]  Jeff Hasty,et al.  Antagonistic gene transcripts regulate adaptation to new growth environments , 2011, Proceedings of the National Academy of Sciences.

[45]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[46]  Animesh Agarwal,et al.  On the precision of quasi steady state assumptions in stochastic dynamics. , 2012, The Journal of chemical physics.

[47]  Kevin R. Sanft,et al.  Legitimacy of the stochastic Michaelis-Menten approximation. , 2011, IET systems biology.

[48]  LieJune Shiau,et al.  Stochastic Delay Accelerates Signaling in Gene Networks , 2011, PLoS Comput. Biol..

[49]  K. Banasiewicz Economic and organizational effects of different legal and organizational forms of enterprises , 2006 .

[50]  Hong Qian,et al.  Generalized Haldane equation and fluctuation theorem in the steady-state cycle kinetics of single enzymes. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.