Cargando…

Networks of ribosome flow models for modeling and analyzing intracellular traffic

The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The input of the RFMIO controls its initiation rate and the output represents the ribosome exit rate (and thus the protein production r...

Descripción completa

Detalles Bibliográficos
Autores principales: Nanikashvili, Itzik, Zarai, Yoram, Ovseevich, Alexander, Tuller, Tamir, Margaliot, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368613/
https://www.ncbi.nlm.nih.gov/pubmed/30737417
http://dx.doi.org/10.1038/s41598-018-37864-1
_version_ 1783394019899867136
author Nanikashvili, Itzik
Zarai, Yoram
Ovseevich, Alexander
Tuller, Tamir
Margaliot, Michael
author_facet Nanikashvili, Itzik
Zarai, Yoram
Ovseevich, Alexander
Tuller, Tamir
Margaliot, Michael
author_sort Nanikashvili, Itzik
collection PubMed
description The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The input of the RFMIO controls its initiation rate and the output represents the ribosome exit rate (and thus the protein production rate) at the 3′ end of the mRNA molecule. The RFMIO and its variants encapsulate important properties that are relevant to modeling ribosome flow such as the possible evolution of “traffic jams” and non-homogeneous elongation rates along the mRNA molecule, and can also be used for studying additional intracellular processes such as transcription, transport, and more. Here we consider networks of interconnected RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the complex mechanisms of protein production. In these networks, the output of each RFMIO may be divided, using connection weights, between several inputs of other RFMIOs. We show that under quite general feedback connections the network has two important properties: (1) it admits a unique steady-state and every trajectory converges to this steady-state; and (2) the problem of how to determine the connection weights so that the network steady-state output is maximized is a convex optimization problem. These mathematical properties make these networks highly suitable as models of various phenomena: property (1) means that the behavior is predictable and ordered, and property (2) means that determining the optimal weights is numerically tractable even for large-scale networks. For the specific case of a feed-forward network of RFMIOs we prove an additional useful property, namely, that there exists a spectral representation for the network steady-state, and thus it can be determined without any numerical simulations of the dynamics. We describe the implications of these results to several fundamental biological phenomena and biotechnological objectives.
format Online
Article
Text
id pubmed-6368613
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-63686132019-02-14 Networks of ribosome flow models for modeling and analyzing intracellular traffic Nanikashvili, Itzik Zarai, Yoram Ovseevich, Alexander Tuller, Tamir Margaliot, Michael Sci Rep Article The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The input of the RFMIO controls its initiation rate and the output represents the ribosome exit rate (and thus the protein production rate) at the 3′ end of the mRNA molecule. The RFMIO and its variants encapsulate important properties that are relevant to modeling ribosome flow such as the possible evolution of “traffic jams” and non-homogeneous elongation rates along the mRNA molecule, and can also be used for studying additional intracellular processes such as transcription, transport, and more. Here we consider networks of interconnected RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the complex mechanisms of protein production. In these networks, the output of each RFMIO may be divided, using connection weights, between several inputs of other RFMIOs. We show that under quite general feedback connections the network has two important properties: (1) it admits a unique steady-state and every trajectory converges to this steady-state; and (2) the problem of how to determine the connection weights so that the network steady-state output is maximized is a convex optimization problem. These mathematical properties make these networks highly suitable as models of various phenomena: property (1) means that the behavior is predictable and ordered, and property (2) means that determining the optimal weights is numerically tractable even for large-scale networks. For the specific case of a feed-forward network of RFMIOs we prove an additional useful property, namely, that there exists a spectral representation for the network steady-state, and thus it can be determined without any numerical simulations of the dynamics. We describe the implications of these results to several fundamental biological phenomena and biotechnological objectives. Nature Publishing Group UK 2019-02-08 /pmc/articles/PMC6368613/ /pubmed/30737417 http://dx.doi.org/10.1038/s41598-018-37864-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Nanikashvili, Itzik
Zarai, Yoram
Ovseevich, Alexander
Tuller, Tamir
Margaliot, Michael
Networks of ribosome flow models for modeling and analyzing intracellular traffic
title Networks of ribosome flow models for modeling and analyzing intracellular traffic
title_full Networks of ribosome flow models for modeling and analyzing intracellular traffic
title_fullStr Networks of ribosome flow models for modeling and analyzing intracellular traffic
title_full_unstemmed Networks of ribosome flow models for modeling and analyzing intracellular traffic
title_short Networks of ribosome flow models for modeling and analyzing intracellular traffic
title_sort networks of ribosome flow models for modeling and analyzing intracellular traffic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368613/
https://www.ncbi.nlm.nih.gov/pubmed/30737417
http://dx.doi.org/10.1038/s41598-018-37864-1
work_keys_str_mv AT nanikashviliitzik networksofribosomeflowmodelsformodelingandanalyzingintracellulartraffic
AT zaraiyoram networksofribosomeflowmodelsformodelingandanalyzingintracellulartraffic
AT ovseevichalexander networksofribosomeflowmodelsformodelingandanalyzingintracellulartraffic
AT tullertamir networksofribosomeflowmodelsformodelingandanalyzingintracellulartraffic
AT margaliotmichael networksofribosomeflowmodelsformodelingandanalyzingintracellulartraffic