On the gain of entrainment in the n-dimensional ribosome flow model

The ribosome flow model (RFM) is a phenomenological model for the flow of particles along a one-dimensional chain of n sites. It has been extensively used to study ribosome flow along the mRNA molecule during translation. When the transition rates along the chain are time-varying and jointly T-periodic the RFM entrains, i.e. every trajectory of the RFM converges to a unique T-periodic solution that depends on the transition rates, but not on the initial condition. In general, entrainment to periodic excitations like the 24 h solar day or the 50 Hz frequency of the electric grid is important in numerous natural and artificial systems. An interesting question, called the gain of entrainment (GOE) in the RFM, is whether proper coordination of the periodic translation rates along the mRNA can lead to a larger average protein production rate. Analysing the GOE in the RFM is non-trivial and partial results exist only for the RFM with dimensions n = 1, 2. We use a new approach to derive several results on the GOE in the general n-dimensional RFM. Perhaps surprisingly, we rigorously characterize several cases where there is no GOE, so to maximize the average production rate in these cases, the best choice is to use constant transition rates all along the chain.

[1]  G. Szederkényi,et al.  Persistence and stability of generalized ribosome flow models with time-varying transition rates , 2022, PLoS ONE.

[2]  Alain Rapaport,et al.  Singular Arcs in Optimal Periodic Controls for Scalar Dynamics and Integral Input Constraint , 2022, Journal of Optimization Theory and Applications.

[3]  A. Gupta,et al.  Modeling mRNA Translation With Ribosome Abortions , 2022, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[4]  M. Margaliot,et al.  Translation in the cell under fierce competition for shared resources: a mathematical model , 2022, bioRxiv.

[5]  G. Szederkényi,et al.  Hamiltonian representation of generalized ribosome flow models , 2022, 2022 European Control Conference (ECC).

[6]  A. Gupta,et al.  Modeling transport of extended interacting objects with drop-off phenomenon , 2022, PloS one.

[7]  G. Szederkenyi,et al.  Persistence and stability of a class of kinetic compartmental models , 2022, Journal of Mathematical Chemistry.

[8]  M. Margaliot,et al.  Large-scale mRNA translation and the intricate effects of competition for the finite pool of ribosomes , 2021, bioRxiv.

[9]  A. van Oudenaarden,et al.  Single-cell Ribo-seq reveals cell cycle-dependent translational pausing , 2021, Nature.

[10]  Tamir Tuller,et al.  Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate , 2020, Scientific Reports.

[11]  Michael Margaliot,et al.  Variability in mRNA translation: a random matrix theory approach , 2020, Scientific Reports.

[12]  Eduardo D. Sontag,et al.  Mediating Ribosomal Competition by Splitting Pools , 2020, IEEE Control Systems Letters.

[13]  L. Grüne,et al.  Random Attraction in the TASEP Model , 2020, SIAM J. Appl. Dyn. Syst..

[14]  Michael Margaliot,et al.  Maximizing average throughput in oscillatory biochemical synthesis systems: an optimal control approach , 2019, Royal Society Open Science.

[15]  G. Katriel Optimality of constant arrival rate for a linear system with a bottleneck entrance , 2019, Syst. Control. Lett..

[16]  Michael Margaliot,et al.  No Switching Policy Is Optimal for a Positive Linear System With a Bottleneck Entrance , 2019, IEEE Control Systems Letters.

[17]  Michael Margaliot,et al.  Ribosome Flow Model with Different Site Sizes , 2019, SIAM J. Appl. Dyn. Syst..

[18]  A. Ovseevich,et al.  Networks of ribosome flow models for modeling and analyzing intracellular traffic , 2018, Scientific Reports.

[19]  M. Martinez,et al.  The calendar of epidemics: Seasonal cycles of infectious diseases , 2018, PLoS pathogens.

[20]  Eduardo D Sontag,et al.  Subharmonics and Chaos in Simple Periodically Forced Biomolecular Models. , 2018, Biophysical journal.

[21]  Michael Margaliot,et al.  Revisiting totally positive differential systems: A tutorial and new results , 2018, Autom..

[22]  Anna Feldman,et al.  The extent of ribosome queuing in budding yeast , 2018, PLoS Comput. Biol..

[23]  G. Petsko,et al.  Endosomal Traffic Jams Represent a Pathogenic Hub and Therapeutic Target in Alzheimer’s Disease , 2017, Trends in Neurosciences.

[24]  Michael Margaliot,et al.  Optimal Translation Along a Circular mRNA , 2017, Scientific Reports.

[25]  Benjamín J. Sánchez,et al.  Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast. , 2017, Cell systems.

[26]  Wolfgang Halter,et al.  A resource dependent protein synthesis model for evaluating synthetic circuits. , 2016, Journal of theoretical biology.

[27]  Jan Maximilian Montenbruck,et al.  Geometric stability considerations of the ribosome flow model with pool , 2016, 1610.03986.

[28]  Domitilla Del Vecchio,et al.  Control theory meets synthetic biology , 2016, Journal of The Royal Society Interface.

[29]  M. Margaliot,et al.  Optimal Down Regulation of mRNA Translation , 2016, Scientific Reports.

[30]  Lars Grüne,et al.  On the relation between strict dissipativity and turnpike properties , 2016, Syst. Control. Lett..

[31]  Michael Margaliot,et al.  Ribosome Flow Model on a Ring , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[32]  Michael Margaliot,et al.  A model for competition for ribosomes in the cell , 2015, Journal of The Royal Society Interface.

[33]  R. Aramayo,et al.  Translate to divide: сontrol of the cell cycle by protein synthesis , 2015, Microbial cell.

[34]  Zahra Aminzarey,et al.  Contraction methods for nonlinear systems: A brief introduction and some open problems , 2014, 53rd IEEE Conference on Decision and Control.

[35]  M. Margaliot,et al.  Sensitivity of mRNA Translation , 2014, Scientific Reports.

[36]  M. Margaliot,et al.  Maximizing protein translation rate in the non-homogeneous ribosome flow model: a convex optimization approach , 2014, Journal of The Royal Society Interface.

[37]  Michael Margaliot,et al.  Entrainment to Periodic Initiation and Transition Rates in a Computational Model for Gene Translation , 2014, PloS one.

[38]  Achim Tresch,et al.  Periodic mRNA synthesis and degradation co‐operate during cell cycle gene expression , 2014, Molecular systems biology.

[39]  Michael Margaliot,et al.  Ribosome flow model with positive feedback , 2013, Journal of The Royal Society Interface.

[40]  P. Bressloff,et al.  Stochastic models of intracellular transport , 2013 .

[41]  N. Sonenberg,et al.  Principles of translational control: an overview. , 2012, Cold Spring Harbor perspectives in biology.

[42]  Ashish Patil,et al.  Increased tRNA modification and gene-specific codon usage regulate cell cycle progression during the DNA damage response , 2012, Cell cycle.

[43]  Michael Margaliot,et al.  Stability Analysis of the Ribosome Flow Model , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[44]  H. Hilhorst,et al.  A multi-lane TASEP model for crossing pedestrian traffic flows , 2012, 1205.1653.

[45]  Milana Frenkel-Morgenstern,et al.  Genes adopt non-optimal codon usage to generate cell cycle-dependent oscillations in protein levels , 2012, Molecular systems biology.

[46]  Isaac Meilijson,et al.  Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model , 2011, PLoS Comput. Biol..

[47]  B. Schmittmann,et al.  Modeling Translation in Protein Synthesis with TASEP: A Tutorial and Recent Developments , 2011, 1108.3312.

[48]  Debashish Chowdhury,et al.  Stochastic Transport in Complex Systems: From Molecules to Vehicles , 2010 .

[49]  A. E. Higareda-Mendoza,et al.  Expression of human eukaryotic initiation factor 3f oscillates with cell cycle in A549 cells and is essential for cell viability , 2010, Cell Division.

[50]  Y. Pilpel,et al.  An Evolutionarily Conserved Mechanism for Controlling the Efficiency of Protein Translation , 2010, Cell.

[51]  Mario di Bernardo,et al.  Global Entrainment of Transcriptional Systems to Periodic Inputs , 2009, PLoS Comput. Biol..

[52]  T. Kriecherbauer,et al.  A pedestrian's view on interacting particle systems, KPZ universality and random matrices , 2008, 0803.2796.

[53]  Nathan van de Wouw,et al.  Frequency Response Functions for Nonlinear Convergent Systems , 2007, IEEE Transactions on Automatic Control.

[54]  R. A. Blythe,et al.  Nonequilibrium steady states of matrix-product form: a solver's guide , 2007, 0706.1678.

[55]  Nicolas Tabareau,et al.  A Contraction Theory Approach to Stochastic Incremental Stability , 2007, IEEE Transactions on Automatic Control.

[56]  N. Sonenberg,et al.  Cell-cycle-dependent translational control. , 2001, Current opinion in genetics & development.

[57]  D. Earn,et al.  Opposite patterns of synchrony in sympatric disease metapopulations. , 1999, Science.

[58]  Jean-Jacques E. Slotine,et al.  On Contraction Analysis for Non-linear Systems , 1998, Autom..

[59]  C. Maffezzoni Hamilton-Jacobi theory for periodic control problems , 1974 .

[60]  Alain Rapaport,et al.  Optimal periodic control for scalar dynamics under integral constraint on the input , 2020 .

[61]  Lars Grüne,et al.  On the relation between strict dissipativity and the turnpike property ∗ , 2015 .

[62]  Vladimir M. Veliov,et al.  Constant Versus Periodic Fishing: Age Structured Optimal Control Approach , 2014 .

[63]  D. Selkoe Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[64]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[65]  N. Dubin Mathematical Model , 2022 .