2018/11/19 by Itzik Nanikashvili, Nanikashvili, Itzik, Yoram Zarai +7
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Molecular Networks (q-bio.MN) #RNA Research and Splicing #RNA and protein synthesis mechanisms #RNA modifications and cancer
paper · pdf · doi:10.48550/arxiv.1811.07661
openalex publication_date 2018/11/19 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
The ribosome flow model with input and output (RFMIO) is a deterministic\ndynamical system that has been used to study the flow of ribosomes during mRNA\ntranslation. The RFMIO and its variants encapsulate important properties that\nare relevant to modeling ribosome flow such as the possible evolution of\n"traffic jams" and non-homogeneous elongation rates along the mRNA molecule,\nand can also be used for studying additional intracellular processes such as\ntranscription, transport, and more. Here we consider networks of interconnected\nRFMIOs as a fundamental tool for modeling, analyzing and re-engineering the\ncomplex mechanisms of protein production. In these networks, the output of each\nRFMIO may be divided, using connection weights, between several inputs of other\nRFMIOs. We show that under quite general feedback connections the network has\ntwo important properties: (1) it admits a unique steady-state and every\ntrajectory converges to this steady-state, and (2) the problem of how to\ndetermine the connection weights so that the network steady-state output is\nmaximized is a convex optimization problem. These mathematical properties make\nthese networks highly suitable as models of various phenomena: property (1)\nmeans that the behavior is predictable and ordered, and property (2) means that\ndetermining the optimal weights is numerically tractable even for large-scale\nnetworks. For the specific case of a feed-forward network of RFMIOs we prove an\nadditional useful property, namely, that there exists a spectral representation\nfor the network steady-state, and thus it can be determined without any\nnumerical simulations of the dynamics. We describe the implications of these\nresult to several fundamental biological phenomena and biotechnological\nobjectives.\n