BackgroundSynthetic biological systems are currently created by an ad-hoc, iterative
process of specification, design, and assembly. These systems would greatly
benefit from a more formalized and rigorous specification of the desired
system components as well as constraints on their composition. Therefore,
the creation of robust and efficient design flows and tools is imperative.
We present a human readable language (Eugene) that allows for the
specification of synthetic biological designs based on biological parts, as
well as provides a very expressive constraint system to drive the automatic
creation of composite Parts (Devices) from a collection of individual
Parts.ResultsWe illustrate Eugene's capabilities in three different areas: Device
specification, design space exploration, and assembly and simulation
integration. These results highlight Eugene's ability to create
combinatorial design spaces and prune these spaces for simulation or
physical assembly. Eugene creates functional designs quickly and
cost-effectively.ConclusionsEugene is intended for forward engineering of DNA-based devices, and through
its data types and execution semantics, reflects the desired abstraction
hierarchy in synthetic biology. Eugene provides a powerful constraint system
which can be used to drive the creation of new devices at runtime. It
accomplishes all of this while being part of a larger tool chain which
includes support for design, simulation, and physical device assembly.
This paper investigates the ability of image profiles, pixelintensity sums across subsets of a video stream, to support the crucial robotic skill of place recognition through visual information alone. Building from work in which image profiles are the fundamental image representation for a model of biological neural processing [3,4,5], this paper offers a conceptually simpler approach to simultaneous localization and mapping via a single camera (monocular SLAM). In contrast to feature-based approaches in which extraction and statistical postprocessing dominate the computation, this work uses a representation suitable even for very simple autonomous platforms. Experiments demonstrate the ability of our profilebased path segments to compensate for the inevitable inaccuracies in odometry when creating consistent world maps.
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