scholarly journals Codon optimization: a mathematical programing approach

2020 ◽  
Vol 36 (13) ◽  
pp. 4012-4020 ◽  
Author(s):  
Alper Şen ◽  
Kamyar Kargar ◽  
Esma Akgün ◽  
Mustafa Ç Pınar
Keyword(s):  
Codon Optimization ◽  
Numerical Study ◽  
Synonymous Codon ◽  
Efficient Frontier ◽  
Amino Acid Sequences ◽  
Mixed Integer ◽  
Codon Pair Bias ◽  
Optimal Codon ◽  
Codon Pair ◽  
Commercial Solver

Abstract Motivation Synthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions. Results We develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations. Availability and implementation http://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.

scholarly journals Codon pair optimization (CPO): a software tool for synthetic gene design based on codon pair bias to improve the expression of recombinant proteins in Pichia pastoris

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yide Huang ◽  
Ting Lin ◽  
Lingfang Lu ◽  
Fan Cai ◽  
Jie Lin ◽  
...  
Keyword(s):  
Pichia Pastoris ◽  
Protein Expression ◽  
Codon Usage ◽  
Codon Optimization ◽  
Software Tool ◽  
Expression Levels ◽  
Gene Design ◽  
Codon Pair Bias ◽  
Codon Pair ◽  
Synthetic Gene Design

Abstract Background Codon optimization is a common method to improve protein expression levels in Pichia pastoris and the current strategy is to replace rare codons with preferred codons to match the codon usage bias. However, codon-pair contexts have a profound effect on translation efficiency by influencing both translational elongation rates and accuracy. Until now, it remains untested whether optimized genes based on codon pair bias results in higher protein expression levels compared to codon usage bias. Results In this study, an algorithm based on dynamic programming was introduced to develop codon pair optimization (CPO) which is a software tool to provide simple and efficient codon pair optimization for synthetic gene design in Pichia pastoris. Two reporters (MT1-MMP E2C6 and ADAM17 A9B8 scFvs) were employed to test the effects of codon pair bias and CPO optimization on their protein expression levels. Four variants of MT1-MMP E2C6 and ADAM17 A9B8 for each were generated, one variant with the best codon-pair context, one with the worst codon-pair context, one with unbiased codon-pair context, and another optimized based on codon usage. The expression levels of variants with the worst codon-pair context were almost undetectable by Western blot and the variants with the best codon-pair context were expressed well. The expression levels on MT1-MMP E2C6 and ADAM17 A9B8 were more than five times and seven times higher in the optimized sequences based on codon-pair context compared to that based on codon usage, respectively. The results indicated that the codon-pair context-based codon optimization is more effective in enhancing expression of protein in Pichia pastoris. Conclusions Codon-pair context plays an important role on the protein expression in Pichia pastoris. The codon pair optimization (CPO) software developed in this study efficiently improved the protein expression levels of exogenous genes in Pichia pastoris, suggesting gene design based on codon pair bias is an alternative strategy for high expression of recombinant proteins in Pichia pastoris.

scholarly journals Development of a rabies virus-based retrograde tracer with high trans-monosynaptic efficiency by reshuffling glycoprotein

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Fan Jia ◽  
Li Li ◽  
Haizhou Liu ◽  
Pei Lv ◽  
Xiangwei Shi ◽  
...  
Keyword(s):  
Experimental Data ◽  
Rabies Virus ◽  
Oncolytic Virus ◽  
Neural Circuits ◽  
Bioinformatic Analysis ◽  
Cell Engineering ◽  
Bias Score ◽  
Codon Pair Bias ◽  
Codon Pair

AbstractRabies virus (RV) is the most widely used vector for mapping neural circuits. Previous studies have shown that the RV glycoprotein can be a target to improve the retrograde transsynaptic tracing efficiency. However, the current versions still label only a small portion of all presynaptic neurons. Here, we reshuffled the oG sequence, a chimeric glycoprotein, with positive codon pair bias score (CPBS) based on bioinformatic analysis of mouse codon pair bias, generating ooG, a further optimized glycoprotein. Our experimental data reveal that the ooG has a higher expression level than the oG in vivo, which significantly increases the tracing efficiency by up to 12.6 and 62.1-fold compared to oG and B19G, respectively. The new tool can be used for labeling neural circuits Therefore, the approach reported here provides a convenient, efficient and universal strategy to improve protein expression for various application scenarios such as trans-synaptic tracing efficiency, cell engineering, and vaccine and oncolytic virus designs.

Numerical Study on Control of Sunroof Buffeting

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
K. Vijaykumar ◽  
S. Poonkodi ◽  
A.T. Sriram
Keyword(s):  
Numerical Study ◽  
Leading Edge ◽  
Dominant Frequency ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Ansys Fluent ◽  
Flow Modification ◽  
Numerical Result ◽  
Modification Technique ◽  
Commercial Solver

Sunroof has become one of the essential features of a luxury car, and it provides natural air circulation and good illumination into the car. But the primary problem associated with it is the buffeting noise which causes discomfort to the passengers. Though adequate studies were carried out on sunroof buffeting, efficient control techniques are needed to be developed from fundamental mechanism. To reduce the buffeting noise, flow modifications at the entrance of the sunroof is considered in this study. The internal portion of the car with sunroof is simplified into a shear driven open cavity, and two-dimensional numerical simulations are carried out using commercial solver, ANSYS Fluent. Reynolds averaged Navier-Stokes equation is used with the realizable k-? turbulence model. The unsteady numerical result obtained in this study is validated with the available experimental results for the dominant frequency. The prediction is good agreement with experiment. Flow modification technique is proposed to control the sunroof buffeting by implementing geometric modifications. A hump has been placed near the leading edge of the cavity which resulted in significant reduction of pressure oscillations. Parametric studies have been performed by varying the height of hump and the distance of hump from the leading edge. There is no prominent difference when the height of the hump is varied. As the distance of the hump from the leading edge is reduced, the sound pressure level decreases.

Dynamic resource allocation and coordination for high-load crisis volunteer management

2018 ◽  
Vol 8 (4) ◽  
pp. 533-556 ◽  
Author(s):  
Christopher Garcia ◽  
Ghaith Rabadi ◽  
Femida Handy
Keyword(s):  
Resource Allocation ◽  
Renewable Resources ◽  
High Load ◽  
Mixed Integer ◽  
Volunteer Management ◽  
Large Set ◽  
Content Type ◽  
Dynamic Resource ◽  
Crisis Scenarios ◽  
Commercial Solver

Purpose Every year volunteers play a crucial role in disaster responses around the world. Volunteer management is known to be more complex than managing a paid workforce, and this is only made worse by the uncertainty of rapidly changing conditions of crisis scenarios. The purpose of this paper is to address the critical problem of assigning tasks to volunteers and other renewable and non-renewable resources simultaneously, particularly under high-load conditions. These conditions are described by a significant mismatch between available volunteer resources and demands or by frequent changes in requirements. Design/methodology/approach Through a combination of literature reviews and interviews with managers from several major volunteer organizations, six key characteristics of crisis volunteer resource allocation problems are identified. These characteristics are then used to develop a general mixed integer programming framework for modeling these problems. Rather than relying on probabilistic resource or demand characterizations, this framework addresses the constantly changing conditions inherent to this class of problems through a dynamic resource reallocation-based approach that minimizes the undesirable impacts of changes while meeting the desired and changing objectives. The viability of this approach for solving problems of realistic size and scale is demonstrated through a large set of computational experiments. Findings Using a common commercial solver, optimal solutions to the allocation and reallocation problems were consistently obtained in short timespans for a wide variety of problems that have realistic sizes and characteristics. Originality/value The proposed approach has not been previously addressed in the literature and represents a computationally tractable method to allocate volunteer, renewable and non-renewable resources to tasks in highly volatile crisis scenarios without requiring probabilistic resource or demand characterizations.

scholarly journals Energy Versus Throughput Optimisation for Machine-to-Machine Communication

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4122
Author(s):  
Emma Fitzgerald ◽  
Michał Pióro ◽  
Artur Tomaszewski
Keyword(s):  
Mesh Networks ◽  
Numerical Study ◽  
Minimum Energy ◽  
Mixed Integer ◽  
Energy Usage ◽  
Maximum Throughput ◽  
Machine To Machine ◽  
Machine To Machine Communication ◽  
Optimisation Model ◽  
Machine Communication

We investigate the trade-off between energy usage and (packet) throughput in wireless mesh networks performing machine-to-machine communication. For this we provide a novel mixed-integer programming formulation to maximise the throughput while maintaining minimal energy usage, together with an effective price-and-branch solution algorithm based on column generation. The resulting optimisation model is the main original contribution of the presented paper. We conducted a numerical study using network examples from 10 to 40 nodes, in which periodic sensor measurements are aggregated and disseminated to actuators. In almost all cases, we were able to achieve maximum throughput and minimum energy usage simultaneously, and in those cases where this was not possible, the costs incurred in one objective in order to achieve the other were typically low. The solution times for all network sizes were of the order of seconds, showing that our optimisation model is feasible to use in practice.

scholarly journals Multimodal Capacitated Hub Location Problems with Multi-Commodities: An Application in Freight Transport

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Alan Osorio-Mora ◽  
Francisco Núñez-Cerda ◽  
Gustavo Gatica ◽  
Rodrigo Linfati
Keyword(s):  
Latin American ◽  
Location Problems ◽  
Transport Cost ◽  
Mixed Integer ◽  
Hub Location ◽  
Proposed Model ◽  
Multimodal Transport ◽  
Two Factors ◽  
Commercial Solver

Hub location problems (HLPs) support decision making on multimodal transport strategic planning. It is related to the location of hubs and the allocation of origin/destination (O/D) flow in a system. Classical formulations assume that these flows are predefined paths and direct delivery is not available. This applied research presents a mixed integer linear programming (MILP) model for a capacitated multimodal, multi-commodity HLP. Furthermore, an application on the export process in a Latin American country is detailed. The new proposed model, unlike the traditional HLP, allows direct shipment, and its O/D flows are part of the decision model. Situations with up to 100 nodes, six products, and two transport modes are used, working with initial and projected flows. All instances can be solved optimally using the commercial solver, Gurobi 7.5.0, in computational times less than a minute. Results indicate that only one hub is profitable for the case study, both for the initial and projected scenarios. The installation of a hub generates transport savings over 1% per year. Two factors affect the location decision: low concentration and distance between the hubs and destinations. Long distances involve an exhaustive use of trains instead of trucks, which leads to lower transport cost per unit.

scholarly journals Implementation of a Mathematical Model to Improve Sustainability in the Handling of Transport Costs in a Distribution Network

2019 ◽  
Vol 12 (1) ◽  
pp. 63
Author(s):  
José Manuel Velarde ◽  
Susana García ◽  
Mauricio López ◽  
Alfredo Bueno-Solano
Keyword(s):  
Mathematical Model ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Numerical Study ◽  
Distribution Network ◽  
Transport Cost ◽  
Mixed Integer ◽  
Distribution Area ◽  
Use Of Resources

This work considers the application of a mathematical model using mixed-integer linear programming for the vehicle routing problem. The model aims at establishing the distribution routes departing from a distribution center to each customer in order to reduce the transport cost associated with these routes. The study considers the use of a fleet of different capacities in the distribution network, which presents the special characteristic of a star network and which must meet different efficiency criteria, such as the fulfillment of each customer’s demand, the vehicle carrying capacity, work schedule, and sustainable use of resources. The intention is to find the amount of equipment suitable to satisfy the demand, thus improving the level of customer service, optimizing the use of both human and economic resources in the distribution area, and leveraging maximum vehicle capacity usage. The MILP mixed-integer linear programming mathematical model of the case study is presented, as well as the corresponding numerical study.

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