Abstract
Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.
Highlights
Translation and transcription are two of the most important cellular processes
An important aspect of these processes is template polymerization, which leads into mechanistic volume-exclusion kinetics that must take into account the ribosome movement along the messenger RNA chain
As we demonstrate in this paper, this reversibility of ribosome binding does not have any considerable influence on the protein synthesis rate or on most properties of translation; the majority of the simulations are performed using the model described in Eq (1)
Summary
Translation and transcription are two of the most important cellular processes. An important aspect of these processes is template polymerization, which leads into mechanistic volume-exclusion kinetics that must take into account the ribosome movement along the messenger RNA (mRNA) chain. Such analyses were first performed using a TASEP-based approach for a few genes in competition, whereas subsequent studies employed stochastic simulations that encompassed a realistic pool of genes.17,18 These computational studies (of isolated genes or competing genes) considered the resultant average properties of the system and not the properties of noise due to translation. While translation initiation can be described as a 3D kinetic process, elongation and termination are 1D processes (i.e., ribosomes moving irreversibly in one dimension) Another challenge common to the stochastic simulation of biological and physical systems is the scalability of the simulations: the mRNA copies should not be observed in isolation because they compete for the same resources (e.g., ribosomes and tRNA) and a cell-sized system consists of multiple tens of thousands of simultaneous reactions. We compare our full mechanistic model with reduced models commonly used in the literature and demonstrate that such lumped models cannot capture the true noise present in the system
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