A synthetic biology approach for the design of genetic algorithms with bacterial agents

Becerra, A. Gargantilla; Gutiérrez, Martín; Lahoz-Beltrá, Rafael

doi:10.1080/17445760.2021.1879072

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…The evolution of the bacteria was simulated by programming the synthetic bacteria in Gro cell programming language [5], which is based on the BAGA evolutionary algorithm introduced in [6]. The GADY model script is shown in the Appendix.…”

Section: Methodsmentioning

confidence: 99%

GADY algorithm: Towards an evolutionary protocol for bottom-up design of synthetic bacteria

Iñiguez,

Villaescusa,

Lahoz-Beltra

2023

Preprint

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Synthetic biology applications are currently based on the programming of bacteria with tailor-made circuits designedad hocby applying a top-down strategy. We introduce a novel algorithm oriented to design synthetic bacteria according to a bottom-up approach, i.e. via an ‘evolutionary programming’ algorithm. The proposed algorithm has been referred to as GADY: an acronym forGet signal,Antidote,Die when a killer gene is expressed, emitsYellow fluorescence. We include in this technical note a script of the algorithm in Gro cellular programming language, programming a colony of synthetic bacteria and illustrating its usefulness in a case of bioremediation or elimination of a strain of pathogenic bacteria.

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Section: Methodsmentioning

confidence: 99%

GADY algorithm: Towards an evolutionary protocol for bottom-up design of synthetic bacteria

Iñiguez,

Villaescusa,

Lahoz-Beltra

2023

Preprint

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“…All of these models mainly infer that the bacterial cells do computing not just based on the single input-output combinations but they can integrate several incoming signals to produce outputs. Moreover, recent research has demonstrated promising cell engineering approaches ( 31 , 32 , 33 , 34 , 35 ), especially application-specific synthetic biological circuits with neural network properties ( 36 , 37 , 38 ). However, the state of the art has pointed out that the process of genetic circuit designing and implementation with the possibility of performing specific tasks is a relatively complex and costly process due to the requirement of developing tools, expertise, and use of specialized materials and equipment ( 39 , 40 ) compared to an approach that harnesses existing circuits within the GRN.…”

Section: Introductionmentioning

confidence: 99%

Revealing gene regulation-based neural network computing in bacteria

Samitha

Balasubramaniam

Martins³

et al. 2023

Biophysical Reports

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“…In this example, the bacteria would be playing the role of a desktop computer case housing the biological hardware, i.e., the program 'written' in these biological components or biobricks. As a result of this approach, it is feasible to program bacteria in silico as if they were a computer [3,4]. Based on this principle, it is possible to go a step further by programming not isolated cells, such as bacteria, but rather groups of cells, e.g., the so-called xenobots [5].…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms 2023, 16, x FOR PEER REVIEW 2 silico as if they were a computer [3,4]. Based on this principle, it is possible to go a further by programming not isolated cells, such as bacteria, but rather groups of cells, the so-called xenobots [5].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria

Villaescusa,

Lahoz-Beltra

2023

Algorithms

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At present, synthetic biology applications are based on the programming of synthetic bacteria with custom-designed genetic circuits through the application of a top-down strategy. These genetic circuits are the programs that implement a certain algorithm, the bacterium being the agent or shell responsible for the execution of the program in a given environment. In this work, we study the possibility that instead of programming synthesized bacteria through a custom-designed genetic circuit, it is the circuit itself which emerges as a result of the evolution simulated through an evolutionary algorithm. This study is conducted by performing in silico experiments in a community composed of synthetic bacteria in which one species or strain behaves as pathogenic bacteria against the rest of the non-pathogenic bacteria that are also part of the bacterial consortium. The goal is the eradication of the pathogenic strain through the evolutionary programming of the agents or synthetic bacteria. The results obtained suggest the plausibility of the evolutionary design of the appropriate genetic circuit resulting from the application of a bottom-up strategy and therefore the experimental feasibility of the evolutionary programming of synthetic bacteria.

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A synthetic biology approach for the design of genetic algorithms with bacterial agents

Cited by 6 publications

References 29 publications

GADY algorithm: Towards an evolutionary protocol for bottom-up design of synthetic bacteria

GADY algorithm: Towards an evolutionary protocol for bottom-up design of synthetic bacteria

Revealing gene regulation-based neural network computing in bacteria

Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria

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