A Genetic Circuit Compiler: Generating Combinatorial Genetic Circuits with Web Semantics and Inference

A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating κ-language simulations from semantic descriptions of genetic circuits

A genetic circuit compiler : generating combinatorial genetic circuits with web semantics and inference

A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice, and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating ?-language simulations from semantic descriptions of genetic circuits

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Modelling and measurement in synthetic biology

Synthetic biology applies engineering principles to make progress in the study of complex biological phenomena. The aim is to develop understanding through the praxis of construction and design. The computational branch of this endeavour explicitly brings the tools of abstraction and modularity to bear. This thesis pursues two distinct lines of inquiry concerning the application of computational tools in the setting of synthetic biology. One thread traces a narrative through multi-paradigm computational simulations, interpretation of results, and quantification of biological order. The other develops computational infrastructure for describing, simulating and discovering, synthetic genetic circuits. The emergence of structure in biological organisms, morphogenesis, is critically important for understanding both normal and pathological development of tissues. Here, we focus on epithelial tissues because models of two dimensional cellular monolayers are computationally tractable. We use a vertex model that consists of a potential energy minimisation process interwoven with topological changes in the graph structure of the tissue. To make this interweaving precise, we define a language for propagators from which an unambiguous description of the simulation methodology can be constructed. The vertex model is then used to reproduce laboratory results of patterning in engineered mammalian cells. The assertion that the claim of reproduction is justified is based on a novel measure of structure on coloured graphs which we call path entropy. This measure is then extended to the setting of continuous regions and used to quantify the development of structure in house mouse (Mus musculus) embryos using three dimensional segmented anatomical models. While it is recognised that DNA can be considered a powerful computational environment, it is far from obvious how to program with nucleic acids. Using rule-based modelling of modular biological parts, we develop a method for discovering synthetic genetic programs that meet a specification provided by the user. This method rests on the concept of annotation as applied to rule-based programs. We begin with annotating rules and proceed to generating entire rule-based programs from annotations themselves. Building on those tools we describe an evolutionary algorithm for discovering genetic circuits from specifications provided in terms of probability distributions. This strategy provides a dual benefit: using stochastic simulation captures circuit behaviour at low copy numbers as well as complex properties such as oscillations, and using standard biological parts produces results that are implementable in the laboratory

DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack

Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier to their deployment on resource-constrained devices. Since such devices are where many emerging deep learning applications lie (e.g., drones, vision-based medical technology), significant bodies of work from both the machine learning and systems communities have attempted to provide optimizations to accelerate DNNs. To help unify these two perspectives, in this paper we combine machine learning and systems techniques within the Deep Learning Acceleration Stack (DLAS), and demonstrate how these layers can be tightly dependent on each other with an across-stack perturbation study. We evaluate the impact on accuracy and inference time when varying different parameters of DLAS across two datasets, seven popular DNN architectures, four DNN compression techniques, three algorithmic primitives with sparse and dense variants, untuned and auto-scheduled code generation, and four hardware platforms. Our evaluation highlights how perturbations across DLAS parameters can cause significant variation and across-stack interactions. The highest level observation from our evaluation is that the model size, accuracy, and inference time are not guaranteed to be correlated. Overall we make 13 key observations, including that speedups provided by compression techniques are very hardware dependent, and that compiler auto-tuning can significantly alter what the best algorithm to use for a given configuration is. With DLAS, we aim to provide a reference framework to aid machine learning and systems practitioners in reasoning about the context in which their respective DNN acceleration solutions exist in. With our evaluation strongly motivating the need for co-design, we believe that DLAS can be a valuable concept for exploring the next generation of co-designed accelerated deep learning solutions

Design and application of reconfigurable circuits and systems

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