Bacterial computing with engineered populations

Amos, Martyn; Axmann, Ilka M.; Blüthgen, Nils; Cruz, Fernando de la; Jaramillo, Alfonso; Rodríguez‐Patón, Alfonso; Simmel, Friedrich C.

doi:10.1098/rsta.2014.0218

Cited by 12 publications

(14 citation statements)

References 56 publications

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“…Amos et al [7] provide an overview of the results from their EU FET BACTOCOM project, addressing this issue. Their aim is to develop a method to design bacterial component computers, through exploiting the exchange of plasmid genetic material through biological conjugation, which can be regarded as a communication protocol.…”

Section: Biological Computingmentioning

confidence: 99%

Heterotic computing: exploiting hybrid computational devices

Kendon

Sebald

Stepney

2015

Phil. Trans. R. Soc. A.

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Current computational theory deals almost exclusively with single models: classical, neural, analogue, quantum, etc. In practice, researchers use ad hoc combinations, realizing only recently that they can be fundamentally more powerful than the individual parts. A Theo Murphy meeting brought together theorists and practitioners of various types of computing, to engage in combining the individual strengths to produce powerful new heterotic devices. 'Heterotic computing' is defined as a combination of two or more computational systems such that they provide an advantage over either substrate used separately. This post-meeting collection of articles provides a wide-ranging survey of the state of the art in diverse computational paradigms, together with reflections on their future combination into powerful and practical applications. OverviewPractical computation has long used different types of computational components in combination. Everyday examples include the graphics co-processing unit that has cooperated with the central processing unit in desktop and laptop computers for two decades, and the GPS chips included in most mobile phones and digital cameras.Our vision for hybrid computational systems [1-3] is far broader than this, however, encompassing novel substrates: from the exquisitely controlled quantum systems prototyping a new breed of faster computation based on quantum logic, to the biological and chemical computational experiments in laboratories around

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Section: Biological Computingmentioning

confidence: 99%

Heterotic computing: exploiting hybrid computational devices

Kendon

Sebald

Stepney

2015

Phil. Trans. R. Soc. A.

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“…The optimisation training problem to be solved is that of minimising (9) for a population of size S, subject to voltage bound constraints V j ∈ [V min , V max ], R ∈ [R min , R max ], electrode assignment p and classification rule (5). V min = 0 Volts, V max = 4 Volts, R min = 0.05 and R max = 15 .…”

Section: Classification Datasetsmentioning

confidence: 99%

“…Different materials have been used, biological and nonbiological. Examples include slime moulds [4], bacterial consortia [5] and cells (neurons) [6]. In [7] it is argued that non-biological materials make a better medium for unconventional computing exploration.…”

Section: Introductionmentioning

confidence: 99%

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers

Vissol-Gaudin

Kotsialos

Groves

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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“…Biochemistry can be used to engineer novel types of computers based on biological components. Examples include, DNA based computers [2,3], robots controlled by slimemolds [4], or logic gates implemented in living cells [5,6,7,8]. Beside this technological importance of biochemical computers, there is now also an increasing appreciation that information processing may be an important fitness contributing function for natural organisms [9,10].…”

Section: Introductionmentioning

confidence: 99%

Performance limits and trade-offs in entropy-driven biochemical computers

Chu

2018

Journal of Theoretical Biology

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Chu, Dominique (2018) Performance limits and trade-offs in entropy-driven biochemical computers.Journal As of yet, research in this topic is based on case-studies. There exists no generally accepted criterion to distinguish between chemical processes that compute and those that do not. Here, the concept of entropy driven computer (EDC) is proposed as a general model of chemical computation. It is found that entropy driven computation is subject to a trade-off between accuracy and entropy production, but unlike many biological systems, there are no trade-offs involving time. The latter only arise when it is taken into account that the observation of the state of the EDC is not energy neutral, but comes at a cost. The significance of this conclusion in relation to biological systems is discussed. Three examples of biological computers, including an implementation of a neural network as an EDC are given.

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Bacterial computing with engineered populations

Cited by 12 publications

References 56 publications

Heterotic computing: exploiting hybrid computational devices

Heterotic computing: exploiting hybrid computational devices

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers

Performance limits and trade-offs in entropy-driven biochemical computers

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