A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

Sarkar, Kathakali; Bonnerjee, Deepro; Srivastava, Rajkamal; Bagh, Sangram

doi:10.1039/d1sc01505b

Cited by 31 publications

(58 citation statements)

References 51 publications

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“…As an application, they designed signaling networks that theoretically behave as linear and nonlinear classifiers. In a study by Sarkar et al, 96 a single-layer ANN was experimentally implemented in Escherichia coli cells, demonstrating the use of engineered bacteria as ANN-enabled wetware that can perform complex computing functions such as multiplexing, de-multiplexing, encoding, decoding, majority functions, or Feynman and Fredkin gates. In Li et al, 97 ANNs were implemented in consortia of bacteria communicating through quorum-sensing molecules.…”

Section: Synthetic Biology Applicationsmentioning

confidence: 99%

Deep Learning Concepts and Applications for Synthetic Biology

Stan

Dunlop

2022

GEN Biotechnology

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Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.

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Section: Synthetic Biology Applicationsmentioning

confidence: 99%

Deep Learning Concepts and Applications for Synthetic Biology

Stan

Dunlop

2022

GEN Biotechnology

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“…Recently we have demonstrated logical reversibility in living cells by implementing Feynman and Fredkin gates in bacteria by using an artificial neural network (ANN) type architecture made from a mixed population of engineered bacteria connected by chemical wires. 23 Thus, logical reversibility in living cells was achieved through distributive computing where various populations of cells carried out various component functions and as a mixed population, they showed the complete function. However, to expand the capability of reversible logic circuits in living cells, it is important to create reversible logic gates in single cells.…”

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confidence: 99%

Synthetic Genetic Reversible Feynman Gate in a Single E. coli Cell and Its Application in Bacterial to Mammalian Cell Information Transfer

et al. 2022

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Reversible computing is a nonconventional form of computing where the inputs and outputs are mapped in a unique one-to-one fashion. Reversible logic gates in single living cells have not been demonstrated. Here, we constructed a synthetic genetic reversible Feynman gate in single E. coli cells, and the input− output relations were measured in a clonal population. The inputs were extracellular chemicals, isopropyl β-D-1-thiogalactopyranoside (IPTG), and anhydrotetracycline (aTc), and the outputs were two fluorescence proteins. We developed a simple mathematical model and simulation to capture the essential features of the circuit and experimentally demonstrated that the behavior of the circuit was ultrasensitive and predictive. We showed an application by creating an intercellular Feynman gate, where input information from bacteria was computed and transferred to HeLa cells through shRNAs delivery and the output signals were observed as silencing of native AKT1 and CTNNB1 genes. The introduction of reversible logics in synthetic biology is new, and given that one-to-one input−output mapping, such reversible genetic systems might have applications in sensing, diagnostics, cellular computing, and synthetic biology.

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“…Animal brains are powerful decision-making devices able to learn by themselves using reinforcement learning. Computational design methods have been used to experimentally implement biological adaptive behaviors(1-7), but not advanced decision making, which was achieved artificially using physical and chemical systems by engineering memory units with neural network computational capabilities (8)(9)(10)(11)(12)(13)(14). Engineered gene circuits could endow living cells with decision making capabilities, although their reprogramming has focused on modifying the encoding DNA, such as mutating and recombining regulatory regions (5,15).…”

mentioning

confidence: 99%

“…Animal brains are powerful decision-making devices able to learn autonomously. Computational design of gene circuits has been used to experimentally implement biological adaptive behaviors( 1-8 ), but not advanced decision making, which was achieved artificially using physical and chemical systems by engineering memory units with neuromorphic computing capabilities( 9-15 ). Engineered gene circuits endow living cells with new decision making skills and their reprogramming could be used to improve the quality of decisions to a given problem.…”

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confidence: 99%

Engineered sensor bacteria evolve master-level gameplay through accelerated adaptation

Racovita

Prakash

Varela

et al. 2022

Preprint

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The engineering of living cells able to learn algorithms by themselves, such as playing board games —a classic challenge for artificial intelligence— will allow complex ecosystems and tissues to be chemically reprogrammed to learn complex decisions. However, current engineered gene circuits encoding decision-making algorithms have failed to implement self-programmability and they require supervised tuning. We show a strategy for engineering gene circuits to rewire themselves by reinforcement learning. We created a scalable general-purpose library of Escherichia coli strains encoding elementary adaptive genetic systems capable of persistently adjusting their relative levels of expression according to their previous behavior. Our strains can learn the mastery of 3×3 board games such as tic-tac-toe, even when starting from a completely ignorant state. We provide a general genetic mechanism for the autonomous learning of decisions in changeable environments.One-Sentence SummaryWe propose a scalable strategy to engineer gene circuits capable of autonomously learning decision-making in complex environments.

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A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

Abstract: We created artificial neural network type architecture with engineered bacteria to perform reversible and irreversible computation. This may work as new computing system for performing complex cellular computation.

Cited by 31 publications

References 51 publications

Deep Learning Concepts and Applications for Synthetic Biology

Deep Learning Concepts and Applications for Synthetic Biology

Synthetic Genetic Reversible Feynman Gate in a Single E. coli Cell and Its Application in Bacterial to Mammalian Cell Information Transfer

Engineered sensor bacteria evolve master-level gameplay through accelerated adaptation

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