Trade-offs between tRNA abundance and mRNA secondary structure support smoothing of translation elongation rate

Translation of protein from mRNA is a complex multi-step process that occurs at a non-uniform rate. Variability in ribosome speed along an mRNA enables refinement of the proteome and plays a critical role in protein biogenesis. Detailed single protein studies have found both tRNA abundance and mRNA secondary structure as key modulators of translation elongation rate, but recent genome-wide ribosome profiling experiments have not observed significant influence of either on translation efficiency. Here we provide evidence that this results from an inherent trade-off between these factors. We find codons pairing to high-abundance tRNAs are preferentially used in regions of high secondary structure content, while codons read by significantly less abundant tRNAs are located in lowly structured regions. By considering long stretches of high and low mRNA secondary structure in Saccharomyces cerevisiae and Escherichia coli and comparing them to randomized-gene models and experimental expression data, we were able to distinguish clear selective pressures and increased protein expression for specific codon choices. The trade-off between secondary structure and tRNA-concentration based codon choice allows for compensation of their independent effects on translation, helping to smooth overall translational speed and reducing the chance of potentially detrimental points of excessively slow or fast ribosome movement.European Commission (Marie-Curie Actions Initial Training Network for Integrated Cellular Homeostasis (NICHE) project 289384

Service - Level Variability and Impatience in Call Centers

Koole, G.M. [Promotor

Polling models with multi-phase gated service

In this paper we introduce and analyze a new class of service policies called multi-phase gated service. This policy is a generalization of the classical single-phase and two-phase gated policies and works as follows. Each customer that arrives at queue i will have to wait K_i cycles before it receives service. The aim of this policy is to provide an interleaving scheme to avoid monopolization of the system by heavily loaded queues, by choosing the proper values of interleaving levels Ki. In this paper, we analyze the effectiveness of the interleaving scheme on the queueing behavior of the system, and consider the problem of identifying the proper combination of interleaving levels (K_1,...,K_N) that minimizes a weighted sum of the mean waiting times at each of the N queues. Obviously, the proper choice of the interleaving levels is most critical when the system is heavily loaded. For this reason, we to obtain closed-form expressions for the asymptotic waiting-time distributions in heavy trafficc, and use these expressions to derive simple heuristics for approximating the optimal interleaving scheme. Numerical results with simulations demonstrate that the accuracy of these approximations is extremely high

Translational sensitivity of the Escherichia coli genome to fluctuating tRNA availability

The synthesis of protein from messenger RNA during translation is a highly dynamic process that plays a key role in controlling the efficiency and fidelity of genome-wide protein expression. The availability of aminoacylated transfer RNA (tRNA) is a major factor influencing the speed of ribosomal movement, which depending on codon choices, varies considerably along a transcript. Furthermore, it has been shown experimentally that tRNA availability can vary signifi-cantly under different growth and stress conditions, offering the cell a way to adapt translational dynamics across the genome. Existing models of translation have neglected fluctuations of tRNA pools, instead assuming fixed tRNA availabilities over time. This has lead to an incomplete under-standing of this process. Here, we show for the entire Escherichia coli genome how and to what extent translational speed profiles, which capture local aspects of translational elongation, respond to measured shifts in tRNA availability. We find that translational profiles across the genome are affected to differing degrees, with genes that are essential or related to fundamental processes such as transla-tion, being more robust than those linked to regula-tion. Furthermore, we reveal how fluctuating tRNA availability influences profiles of specific sequences known to play a significant role in translational control of gene expression

Efficient multiplexed gene regulation in Saccharomyces cerevisiae using dCas12a

CRISPR Cas12a is an RNA-programmable endonuclease particularly suitable for gene regulation. This is due to its preference for T-rich PAMs that allows it to more easily target AT-rich promoter sequences, and built-in RNase activity which can process a single CRISPR RNA array encoding multiple spacers into individual guide RNAs (gRNAs), thereby simplifying multiplexed gene regulation. Here, we develop a flexible dCas12a-based CRISPRi system for Saccharomyces cerevisiae and systematically evaluate its design features. This includes the role of the NLS position, use of repression domains, and the position of the gRNA target. Our optimal system is comprised of dCas12a E925A with a single C-terminal NLS and a Mxi1 or a MIG1 repression domain, which enables up to 97% downregulation of a reporter gene. We also extend this system to allow for inducible regulation via an RNAP II-controlled promoter, demonstrate position-dependent effects in crRNA arrays, and use multiplexed regulation to stringently control a heterologous β-carotene pathway. Together these findings offer valuable insights into the design constraints of dCas12a-based CRISPRi and enable new avenues for flexible and efficient gene regulation in S. cerevisiae

Minimizing bed occupancy variance by scheduling patients under uncertainty

International audienceIn this paper we consider the problem of scheduling patients in allocated surgery blocks in a Master Surgical Schedule. We pay attention to both the available surgery blocks and the bed occupancy in the hospital wards. More specifically, large probabilities of overtime in each surgery block are undesirable and costly, while large fluctuations in the number of used beds requires extra buffer capacity and makes the staff planning more challenging. The stochastic nature of surgery durations and length of stay on a ward hinders the use of classical techniques. Transforming the stochastic problem into a deterministic problem does not result into practically feasible solutions. In this paper we develop a technique to solve the stochastic scheduling problem, whose primary objective it to minimize variation in the necessary bed capacity, while maximizing the number of patients operated, and minimizing the maximum waiting time, and guaranteeing a small probability of overtime in surgery blocks. The method starts with solving an Integer Linear Programming (ILP) formulation of the problem, and then simulation and local search techniques are applied to guarantee small probabilities of overtime and to improve upon the ILP solution. Numerical experiments applied to a Dutch hospital show promising results

Holistic assessment of call centre performance

In modern call centres 60–70% of the operational costs come in the form of the human agents who take the calls. Ensuring that the call centre operates at lowest cost and maximum efficiency involves a trade‐off of the cost of agents against lost revenue and increased customer dissatisfaction due to lost calls. Modelling the performance characteristics of a call centre in terms of the agent queue alone misses key performance influencers, specifically the interaction between channel availability at the media gateway and the time a call is queued. A blocking probability at the media gateway, as low as 0.45%, has a significant impact on the degree of queuing observed and therefore the cost and performance of the call centre. Our analysis also shows how abandonment impacts queuing delay. However, the call centre manager has less control over this than the level of contention at the media gateway. Our commercial assessment provides an evaluation of the balance between abandonment and contention, and shows that the difference in cost between the best and worst strategy is £130K per annum, however this must be balanced against a possible additional £2.98 m exposure in lost calls if abandonment alone is used

A Minimal Model of Ribosome Allocation Dynamics Captures Trade-offs in Expression between Endogenous and Synthetic Genes

Cells contain a finite set of resources that must be distributed across many processes to ensure survival. Among them, the largest proportion of cellular resources is dedicated to protein translation. Synthetic biology often exploits these resources to execute orthogonal genetic circuits, yet the burden this places on the cell is rarely considered. Here, we develop a minimal model of ribosome allocation dynamics capturing the demands on translation when expressing a synthetic construct together with endogenous genes required for maintenance of cell physiology. Critically, it contains three key variables related to design parameters of the synthetic construct covering: transcript abundance, translation initiation rate, and elongation time. We show that model-predicted changes in ribosome allocation closely match experimental shifts in synthetic protein expression rate and cellular growth. Intriguingly, the model is also able to accurately infer transcript levels and translation times after further exposure to additional ambient stress. Our results demonstrate that a simple model of resource allocation faithfully captures the redistribution of protein synthesis resources when faced with the burden of synthetic gene expression and environmental stress. The tractable nature of the model makes it a versatile tool for exploring the guiding principles of efficient heterologous expression and the indirect interactions that can arise between synthetic circuits and their host chassis due to competition for shared translational resources

Evolutionary algorithms for optimal control in fed-batch fermentation processes

In this work, Evolutionary Algorithms (EAs) are used to achieve optimal feedforward control in a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. Three diferent aspects are the target of the optimization procedure: the feeding trajectory (the amount of substrate introduced in a bioreactor per time unit), the duration of the fermentation and the initial conditions of the process. A novel EA with variable size chromosomes and using real-valued representations is proposed that is capable of simultaneously optimizing the aforementioned aspects. Outstanding productivity levels were achieved and the results are validated by practice