Computing within bacteria: Programming of bacterial behavior by means of a plasmid encoding a perceptron neural network

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

doi:10.1016/j.biosystems.2022.104608

Cited by 11 publications

(9 citation statements)

References 58 publications

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“…These include a combination of a graph neural network and CNN for efficient breast cancer classification [ 50 ]; deep learning and transfer learning through regional CNN for white blood cell detection [ 51 ]; and a fine-tuned neural network and long-term short-term memory-based neural network for skin disease [ 52 ]. They also include a convolutional autoencoder and transfer learning-based scheme for Alzheimer’s disease visualization [ 53 ]; a perceptron neural network for bacterial behavior programming [ 54 ]; and a deep neural architecture with generative adversarial network for brain tumor classification [ 55 ]. In addition, they include a deep neural network for epidemic prediction of COVID disease [ 56 ]; deep learning for sequential analysis of biomolecules [ 57 ]; elastic net and neural networks for the identification of plant genomics [ 58 ]; data mining and machine learning algorithms based on spectral clustering, random forest, and neural networks for cancer diagnosis through gene data [ 8 ]; and a stacking ensemble model based on an auto-regressive integrated moving average, exponential smoothing, a neural network autoregressive, a gradient-boosting regression tree, and extreme gradient boost models for infectious diseases [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

et al. 2023

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The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson’s disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg–Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations.

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Section: Related Workmentioning

confidence: 99%

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

et al. 2023

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“…Bacteria are well known for their capabilities to sense external stimuli and adapt into a wide range of responses ( 1 , 2 ). The interpretation of external signals includes molecules communicated from other microbes as well as changes in environmental conditions (e.g., changes in temperature or pH levels) ( 3 ).…”

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%

“…At present, the application of synthetic biology principles and the use of simulators has allowed the design of artificial microbial communities in which bacteria exhibit programmed behaviors [3,4]. In nature, these microbial communities or consortia are a group of bacteria of different species or the same specie but different strains whose individuals interact with each other by sharing resources and exhibiting different behaviors [10].…”

Section: Introductionmentioning

confidence: 99%

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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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Computing within bacteria: Programming of bacterial behavior by means of a plasmid encoding a perceptron neural network

Cited by 11 publications

References 58 publications

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

Revealing gene regulation-based neural network computing in bacteria

Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria

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