Pathways to cellular supremacy in biocomputing

Grozinger, Lewis; Amos, Martyn; Gorochowski, Thomas E.; Carbonell, Pablo; Oyarzún, Diego A.; Stoof, Ruud; Fellermann, Harold; Zuliani, Paolo; Tas, Huseyin; Goñi-Moreno, Ángel

doi:10.1038/s41467-019-13232-z

Cited by 136 publications

(123 citation statements)

References 154 publications

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“…We therefore argue that presuming neurons to be simple processing elements underestimates their computational power. We believe that the computational capacity of individual neurons stems as much from their rich intracellular signaling pathways as from their interconnectedness, especially for computations in the regime of cellular supremacy, where biomolecular computations are proposed to be more efficient than implementations in the classical von Neumann and Turing frameworks 58 . We are confident that studying the molecular/electrical interface in relatively simple systems like the Crz network will provide still more guiding principles for linking the operations of neurons to our thoughts, emotions, percepts, and actions.…”

Section: Discussionmentioning

confidence: 99%

Biochemical computation underlying behavioral decision-making

Thornquist

et al. 2020

Preprint

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7Computations in the brain are broadly assumed to emerge from patterns of fast electrical 8 activity. Challenging this view, we show that a male fly's decision to persist in mating, 9 even through a potentially lethal threat, hinges on biochemical computations that enable 10 processing over minutes to hours. Each neuron in a recurrent network measuring time 11 into mating contains slightly different internal molecular estimates of elapsed time. 12Protein Kinase A (PKA) activity contrasts this internal measurement with input from the 13 other neurons to represent evidence that the network's goal has been achieved. When 14 consensus is reached, PKA pushes the network toward a large-scale and synchronized 15 burst of calcium influx, which we call an eruption. The eruption functions like an action 16 potential at the level of the network, transforming deliberation within the network into an 17 all-or-nothing output, after which the male will no longer sacrifice his life to continue 18 mating. We detail the continuous transformation between interwoven molecular and 19 electrical information over long timescales in this system, showing how biochemical 20 activity, invisible to most large scale recording techniques, is the key computational 21 currency directing a life-or-death decision. 22 23 24 25 32 33A fundamental problem in neural computation stems from the need for neurons within a 34 network to communicate without triggering premature responses in downstream 35 circuitry 1-3 . This problem is compounded when the computations are performed over 36 long timescales, as in many decision-making paradigms 4 . In single neurons, the action 37 potential solves an analogous problem by transforming accumulated positive and 38 negative inputs into a transient, all-or-nothing output that is broadcasted to postsynaptic 39 targets. Here we identify a similar mechanism acting at the network level and over 40 timescales ranging from seconds to hours, which we call an eruption. 42The Corazonin-expressing (Crz) neurons of the male abdominal ganglion comprise an 43 exceptionally tractable system for investigating neuronal networks and behavioral 44 control. These four neurons drive two simultaneous and crucial events in the lives of the 45 male and his mating partner: i) the transfer of sperm from the male to the female 5 and ii) 46 a transition out of a period of insurmountably high motivation to continue mating 6 . Both of 47 these events occur six minutes after a mating begins and are under the control of a 48 molecular timer encoded by the slowly-decaying autophosphorylation of the kinase 49 CaMKII 6 . We show that the Crz neurons use cyclic AMP (cAMP) signaling to average 50 evidence about the passage of time across the network and generate an eruption that 51 signals to downstream circuitry only when a consensus is reached. In addition to 52 revealing a new network phenomenon, these results explain the function of CaMKII in 53 the only mechanism for interval timing yet to be described. 55 RESULTS 57The Crz neurons form a recu...

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Section: Discussionmentioning

confidence: 99%

Biochemical computation underlying behavioral decision-making

Thornquist

et al. 2020

Preprint

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“…The current renewed interest in AI, and its need of powerful computational resources, offers a huge opportunity for directing the potential of Synthetic Biology towards satisfying those needs and providing an alternative paradigm (and more natural, since inspiration for most MH actually comes from biology) for solving difficult problems. The goal of this ongoing and future research is to reach the definition of a global AI framework 52 . Characterizing CA computational power in cell colonies is another interesting and important topic to study.…”

Section: Future Workmentioning

confidence: 99%

A framework for implementing metaheuristic algorithms using intercellular communication

Gutiérrez

Ortiz

Carrión

2020

Preprint

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Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH are used to approximate the solution to difficult problems but require a large amount of computational resources. Cell colonies harboring synthetic distributed circuits using intercell communication offer a direction for tackling this problem, as they process information in a massively parallel fashion. In this work, we propose a framework that maps MH elements to synthetic circuits in growing cell colonies. The framework relies on cell-cell communication mechanisms such as quorum sensing (QS) and bacterial conjugation. As a proofof-concept, we also implemented the workflow associated to the framework, and tested the execution of two specific MH (Genetic Algorithms and Simulated Annealing) encoded as synthetic 2 circuits on the gro simulator. Furthermore, we show an example of how our framework can be extended by implementing another kind of computational model: The Cellular Automaton. This work seeks to lay the foundations of mappings for implementing AI algorithms in a general manner using Synthetic Biology constructs in cell colonies. MAIN TEXTEvolution is a key element involved in all microbiology processes. It is the process that drives organism adaptation to better survive and thrive in their surrounding environment. This process occurs at a genetic level, involving mainly genetic recombination and mutation. The genetic diversity produced by evolution is studied and used as inspiration in computational methods such as Evolutionary Algorithms (EAs) 1,2 . These algorithms are generally used for approximating solutions to optimization problems. Since evolution is a standard occurring process, it is natural to relate EAs to microbiology experiments, and more specifically, to Synthetic Biology constructs. This relationship has already been addressed by Directed Evolution 3,4 . However, the control level of Directed Evolution is not as specific as the one reached in EAs. Furthermore, several other computational methods can be translated to Synthetic Biology constructs that emulate their operation. Metaheuristic procedures (MH) 5-7 are a larger class of procedures that contain EAs.Inspiration upon which these techniques are designed range from metallurgy processes 8-10 through bird flock movement patterns [11][12][13] , and ant colony food foraging 14,15 . A general mapping, relating Synthetic Biology constructs to MH elements can be proposed such that any procedure of that class can be modeled as a synthetic circuit. This is due to MH sharing common elements and similarities that can be generalized.

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“…Water presence in the underground and multiple sources of geological evidence reveal a planet that used to be wet and a global picture of the planetary limits and potentials is emerging. Similarly, the last decades have witnessed the development of synthetic biology as an engineering avenue for modifying cells to perform novel functionalities [47]. This area promises the modification of living systems in ways that could overcome the design limitations resulting from evolutionary trade-offs.…”

Section: Microbial Hypercyclesmentioning

confidence: 99%

Synthetic Biology for Terraformation: Lessons from Mars, Earth and the Microbiome

Conde-Pueyo¹,

Vidiella²,

Sardanyés³

et al. 2019

Preprint

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What is the potential for synthetic biology as a way of engineering, on a large scale, complex ecosystems? Can it be used to change endangered ecological communities and rescue them to prevent their collapse? What are the best strategies for such ecological engineering paths to succeed? Is it possible to create stable, diverse synthetic ecosystems capable of persisting in closed environments? Can synthetic communities be created to thrive on planets different from ours? These and other questions pervade major future developments within synthetic biology. The goal of engineering ecosystems is plagued with all kinds of technological, scientific and ethic problems. In this paper we consider the requirements for Terraformation, i. e. for changing a given environment to make it hospitable to some given class of life forms. Although the standard use of this term involved strategies for planetary terraformation, it has been recently suggested that this approach could be applied to a very different context: ecological communities within our own planet. As discussed here, this includes multiple scales, from the gut microbiome to the entire biosphere.

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Pathways to cellular supremacy in biocomputing

Cited by 136 publications

References 154 publications

Biochemical computation underlying behavioral decision-making

Biochemical computation underlying behavioral decision-making

A framework for implementing metaheuristic algorithms using intercellular communication

Synthetic Biology for Terraformation: Lessons from Mars, Earth and the Microbiome

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