A Non-Newtonian Fluid Robot

Hachmon, Guy; Mamet, Noam; Sasson, Sapir; Barkai, Tal; Hadar, Nomi; Abu-Horowitz, Almogit; Bachelet, Ido

doi:10.1162/artl_a_00194

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Indeed, an eigenvector analysis on all the β*’s and β’s studied here showed that only a single non-negative eigenvector exists for each. It is tempting to consider an eigenvector with all non-negative elements as representing a molecular composition (as sometimes done [ 39 , 77 , 79 ]), homologue to a compotype. A vector with some negative elements, representing negative molecular counts or concentrations, by definition cannot represent molecular composition.…”

Section: Resultsmentioning

confidence: 99%

Predicting species emergence in simulated complex pre-biotic networks

Markovitch

Krasnogor

2018

PLoS ONE

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An intriguing question in evolution is what would happen if one could “replay” life’s tape. Here, we explore the following hypothesis: when replaying the tape, the details (“decorations”) of the outcomes would vary but certain “invariants” might emerge across different life-tapes sharing similar initial conditions. We use large-scale simulations of an in silico model of pre-biotic evolution called GARD (Graded Autocatalysis Replication Domain) to test this hypothesis. GARD models the temporal evolution of molecular assemblies, governed by a rates matrix (i.e. network) that biases different molecules’ likelihood of joining or leaving a dynamically growing and splitting assembly. Previous studies have shown the emergence of so called compotypes, i.e., species capable of replication and selection response. Here, we apply networks’ science to ascertain the degree to which invariants emerge across different life-tapes under GARD dynamics and whether one can predict these invariant from the chemistry specification alone (i.e. GARD’s rates network representing initial conditions). We analysed the (complex) rates’ network communities and asked whether communities are related (and how) to the emerging species under GARD’s dynamic, and found that the communities correspond to the species emerging from the simulations. Importantly, we show how to use the set of communities detected to predict species emergence without performing any simulations. The analysis developed here may impact complex systems simulations in general.

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Section: Resultsmentioning

confidence: 99%

Predicting species emergence in simulated complex pre-biotic networks

Markovitch

Krasnogor

2018

PLoS ONE

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“…Motion in a non‐Newtonian liquid robot was also demonstrated using an array of acoustic transducers, producing controllable movements, and also the capability of carrying a load …”

Section: System Functionalitiesmentioning

confidence: 99%

Smart Fluid Systems: The Advent of Autonomous Liquid Robotics

Chiolerio

Quadrelli

2017

Advanced Science

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Organic, inorganic or hybrid devices in the liquid state, kept in a fixed volume by surface tension or by a confining membrane that protects them from a harsh environment, could be used as biologically inspired autonomous robotic systems with unique capabilities. They could change shape according to a specific exogenous command or by means of a fully integrated adaptive system, and provide an innovative solution for many future applications, such as space exploration in extreme or otherwise challenging environments, post‐disaster search and rescue in ground applications, compliant wearable devices, and even in the medical field for in vivo applications. This perspective provides an initial assessment of existing capabilities that could be leveraged to pursue the topic of “Smart Fluid Systems” or “Liquid Engineered Systems”.

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“…In the past two decades, robots have been widely used in various fields including monitoring, industrial automation production, visual navigation, automatic image interpretation, human–computer interaction and virtual reality [1]. Machine vision is to simulate the visual function of human eyes using computers, whose objective is to extract information from images or image sequences and then perform morphological and motion recognition for three‐dimensional (3D) scenes and objects in the real world [2–4].…”

Section: Introductionmentioning

confidence: 99%