Mediating Ribosomal Competition by Splitting Pools

Synthetic biology constructs often rely upon the introduction of “circuit” genes into host cells, in order to express novel proteins and thus endow the host with a desired behavior. The expression of these new genes “consumes” existing resources in the cell, such as ATP, RNA polymerase, amino acids, and ribosomes. Ribosomal competition among strands of mRNA may be described by a system of nonlinear ODEs called the Ribosomal Flow Model (RFM). The competition for resources between host and circuit genes can be ameliorated by splitting the ribosome pool by use of orthogonal ribosomes, where the circuit genes are exclusively translated by mutated ribosomes. In this letter, the RFM system is extended to include orthogonal ribosome competition. This Orthogonal Ribosomal Flow Model (ORFM) is proven to be stable through the use of Robust Lyapunov Functions. The optimization problem of maximizing the weighted protein translation rate by adjusting allocation of ribosomal species is formulated.

[1]  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.

[2]  Franco Blanchini,et al.  Polyhedral Lyapunov functions structurally ensure global asymptotic stability of dynamical networks iff the Jacobian is non-singular , 2017, Autom..

[3]  Eduardo Sontag,et al.  A Petri net approach to the study of persistence in chemical reaction networks. , 2006, Mathematical biosciences.

[4]  Oliver P. T. Barrett,et al.  Evolved orthogonal ribosome purification for in vitro characterization , 2010, Nucleic acids research.

[5]  Christopher V. Rao,et al.  Computational design of orthogonal ribosomes , 2008, Nucleic acids research.

[6]  A. Pipkin,et al.  Kinetics of biopolymerization on nucleic acid templates , 1968, Biopolymers.

[7]  Mustafa Khammash,et al.  Characterization and mitigation of gene expression burden in mammalian cells , 2020, Nature Communications.

[8]  J. H. Gibbs,et al.  Concerning the kinetics of polypeptide synthesis on polyribosomes , 1969 .

[9]  David Angeli,et al.  New Approach to the Stability of Chemical Reaction Networks: Piecewise Linear in Rates Lyapunov Functions , 2014, IEEE Transactions on Automatic Control.

[10]  Alan Costello,et al.  Synthetic Biological Circuits within an Orthogonal Central Dogma. , 2020, Trends in biotechnology.

[11]  David Angeli,et al.  Robust Lyapunov functions for Complex Reaction Networks: An uncertain system framework , 2014, 53rd IEEE Conference on Decision and Control.

[12]  Michael Margaliot,et al.  Controllability Analysis and Control Synthesis for the Ribosome Flow Model , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[13]  J. Chin,et al.  A network of orthogonal ribosome·mRNA pairs , 2005, Nature chemical biology.

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

[15]  Franco Blanchini,et al.  Piecewise-linear Lyapunov functions for structural stability of biochemical networks , 2014, Autom..

[16]  Muhammad Ali Al-Radhawi,et al.  Mediating Ribosomal Competition by Splitting Pools , 2021, 2021 American Control Conference (ACC).

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

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

[19]  David Angeli,et al.  Piecewise Linear in rates Lyapunov functions for Complex Reaction Networks , 2013, 52nd IEEE Conference on Decision and Control.

[20]  Declan G. Bates,et al.  Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes , 2017, Nature Communications.

[21]  David Angeli,et al.  A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks , 2019, bioRxiv.

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