The information capacity of the genetic code: Is the natural code optimal?

Kuruoğlu, Erçan E.; Arndt, Peter F.

doi:10.1016/j.jtbi.2017.01.046

Cited by 15 publications

(8 citation statements)

References 41 publications

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“…Second, it (by far) does not do justice to more sophisticated approaches to the information theory of the genetic code. 24 However, using the current empirical frequencies of codons and amino acids, literature values are I DNA ≈ 5.94 25 and I AA ≈ 4.18, 26 not too different of the values obtained for of equiprobable signs used here (6 and 4.3).…”

Section: Perspectivescontrasting

confidence: 64%

Nano, Bits, and Feynman’s Dream: There’s Plenty of Room at the (Molecular) Bottom

Loretan

Müller

2023

J. Chem. Educ.

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The activity presented here is about an approximate or order-of-magnitude approach to information technology on the nano level, dealing in particular with the genetic code. Based on "There's Plenty of Room at the Bottom", the famous and visionary paper by Richard Feynman, the activity provides insight into a core concept of nanoscience and technology (NST): "information" and the extremely high information density in human DNA as a natural example of a nanostructure. Specifically, students calculate several estimates from Feynman's paper, using elementary knowledge about the chemical structure of DNA and basic mathematics. The activity is appropriate for upper high-school and early undergraduate students and nonmajor courses.

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Section: Perspectivescontrasting

confidence: 64%

Nano, Bits, and Feynman’s Dream: There’s Plenty of Room at the (Molecular) Bottom

Loretan

Müller

2023

J. Chem. Educ.

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“…But in none of these investigations did the standard genetic code emerge as the most resistant to mutations, and better codes can be designed from various points of views (Kuruoglu and Arndt, 2017). One can argue that any further optimization of the genetic code would have negligible benefit, and would have -as stated by Crick -a high cost.…”

Section: The Error Minimization Scenariomentioning

confidence: 99%

The evolution of the genetic code: Impasses and challenges

Kun

Radványi

2018

Biosystems

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The origin of the genetic code and translation is a "notoriously difficult problem". In this survey we present a list of questions that a full theory of the genetic code needs to answer. We assess the leading hypotheses according to these criteria. The stereochemical, the coding coenzyme handle, the coevolution, the four-column theory, the error minimization and the frozen accident hypotheses are discussed. The integration of these hypotheses can account for the origin of the genetic code. But experiments are badly needed. Thus we suggest a host of experiments that could (in)validate some of the models. We focus especially on the coding coenzyme handle hypothesis (CCH). The CCH suggests that amino acids attached to RNA handles enhanced catalytic activities of ribozymes. Alternatively, amino acids without handles or with a handle consisting of a single adenine, like in contemporary coenzymes could have been employed. All three scenarios can be tested in in vitro compartmentalized systems.

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“…In this work, we propose a heuristic, non-convex optimization algorithm, namely simulated annealing (SA), for the structure optimization of partially connected neural networks after pruning [47]. The choice of simulated annealing has been motivated by the success of the algorithm in various problems involving network/graph structures with a large number of configurations and complicated cost surfaces with various local minima [48][49][50].…”

Section: Network Optimization Using Simulated Annealingmentioning

confidence: 99%

Neural Network Structure Optimization by Simulated Annealing

Kuo

Kuruoğlu

Chan

2022

Entropy

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A critical problem in large neural networks is over parameterization with a large number of weight parameters, which limits their use on edge devices due to prohibitive computational power and memory/storage requirements. To make neural networks more practical on edge devices and real-time industrial applications, they need to be compressed in advance. Since edge devices cannot train or access trained networks when internet resources are scarce, the preloading of smaller networks is essential. Various works in the literature have shown that the redundant branches can be pruned strategically in a fully connected network without sacrificing the performance significantly. However, majority of these methodologies need high computational resources to integrate weight training via the back-propagation algorithm during the process of network compression. In this work, we draw attention to the optimization of the network structure for preserving performance despite compression by pruning aggressively. The structure optimization is performed using the simulated annealing algorithm only, without utilizing back-propagation for branch weight training. Being a heuristic-based, non-convex optimization method, simulated annealing provides a globally near-optimal solution to this NP-hard problem for a given percentage of branch pruning. Our simulation results have shown that simulated annealing can significantly reduce the complexity of a fully connected network while maintaining the performance without the help of back-propagation.

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The information capacity of the genetic code: Is the natural code optimal?

Cited by 15 publications

References 41 publications

Nano, Bits, and Feynman’s Dream: There’s Plenty of Room at the (Molecular) Bottom

Nano, Bits, and Feynman’s Dream: There’s Plenty of Room at the (Molecular) Bottom

The evolution of the genetic code: Impasses and challenges

Neural Network Structure Optimization by Simulated Annealing

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