1,869 research outputs found
A Realistic Model under which the Genetic Code is Optimal
The genetic code has a high level of error robustness. Using values of
hydrophobicity scales as a proxy for amino acid character, and the Mean Square
measure as a function quantifying error robustness, a value can be obtained for
a genetic code which reflects the error robustness of that code. By comparing
this value with a distribution of values belonging to codes generated by random
permutations of amino acid assignments, the level of error robustness of a
genetic code can be quantified. We present a calculation in which the standard
genetic code is shown to be optimal. We obtain this result by (1) using
recently updated values of polar requirement as input; (2) fixing seven
assignments (Ile, Trp, His, Phe, Tyr, Arg, and Leu) based on aptamer
considerations; and (3) using known biosynthetic relations of the 20 amino
acids. This last point is reflected in an approach of subdivision (restricting
the random reallocation of assignments to amino acid subgroups, the set of 20
being divided in four such subgroups). The three approaches to explain
robustness of the code (specific selection for robustness, amino acid-RNA
interactions leading to assignments, or a slow growth process of assignment
patterns) are reexamined in light of our findings. We offer a comprehensive
hypothesis, stressing the importance of biosynthetic relations, with the code
evolving from an early stage with just glycine and alanine, via intermediate
stages, towards 64 codons carrying todays meaning.Comment: 22 pages, 3 figures, 4 tables Journal of Molecular Evolution, July
201
AVERAGE AND STANDARD DEVIATION OF THE ERROR FUNCTION FOR RANDOM GENETIC CODES WITH STANDARD STOP CODONS
The origin of the genetic code has been attributed in part to an accidental assignment of codons to amino acids. Although several lines of evidence indicate the subsequent expansion and improvement of the genetic code, the hypothesis of Francis Crick concerning a frozen accident occurring at the early stage of genetic code evolution is still widely accepted. Considering Crick’s hypothesis, mathematical descriptions of hypothetical scenarios involving a huge number of possible coexisting random genetic codes could be very important to explain the origin and evolution of a selected genetic code. This work aims to contribute in this regard, that is, it provides a theoretical framework in which statistical parameters of error functions are calculated. Given a genetic code and an amino acid property, the functional code robustness is estimated by means of a known error function. In this work, using analytical calculations, general expressions for the average and standard deviation of the error function distributions of completely random codes with standard stop codons were obtained. As a possible biological application of these results, any set of amino acids and any pure or mixed amino acid properties can be used in the calculations, such that, in case of having to select a set of amino acids to create a genetic code, possible advantages of natural selection of the genetic codes could be discussed
A new competitive implementation of the electromagnetism-like algorithm for global optimization
The Electromagnetism-like (EM) algorithm is a population-
based stochastic global optimization algorithm that uses an attraction-
repulsion mechanism to move sample points towards the optimal. In
this paper, an implementation of the EM algorithm in the Matlab en-
vironment as a useful function for practitioners and for those who want
to experiment a new global optimization solver is proposed. A set of
benchmark problems are solved in order to evaluate the performance of
the implemented method when compared with other stochastic methods
available in the Matlab environment. The results con rm that our imple-
mentation is a competitive alternative both in term of numerical results
and performance. Finally, a case study based on a parameter estimation
problem of a biology system shows that the EM implementation could
be applied with promising results in the control optimization area.Acknowledgments This work has been supported by FCT (Funda¸c˜ao para a Ciˆencia e Tecnologia, Portugal) in the scope of the project PEst-UID/CEC/00319/2013
Multidisciplinary Design Optimization for Space Applications
Multidisciplinary Design Optimization (MDO) has been increasingly studied in aerospace engineering with the main purpose of reducing monetary and schedule costs. The traditional design approach of optimizing each discipline separately and manually iterating to achieve good solutions is substituted by exploiting the interactions between the disciplines and concurrently optimizing every subsystem. The target of the research was the development of a flexible software suite capable of concurrently optimizing the design of a rocket propellant launch vehicle for multiple objectives. The possibility of combining the advantages of global and local searches have been exploited in both the MDO architecture and in the selected and self developed optimization methodologies. Those have been compared according to computational efficiency and performance criteria. Results have been critically analyzed to identify the most suitable optimization approach for the targeted MDO problem
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