Towards Efficient Algorithms for Modulating Ribosome Traffic Jams

Background: mRNA translation is the process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host`s growth rate.

Methods: We developed a generic approach for modulating the growth rate (and thus fitness) of any host/organism by introducing silent/synonymous mutations based on comprehensive computational models. We specifically studied various algorithms that introduce silent mutations that improve/worsen the allocation of ribosomes in the cells via the reduction/expansion of their traffic jams during translation respectively.

Results: more/less resources are available, respectively, for the cell, promoting improved/reduced growth-rate.

Conclusion: The approach can be employed for improving the growth rate of any organism with data for inferring models, and with relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.