Synthetic Biology is an engineering discipline where parts of DNA sequences are composed into novel, complex systems that execute a desired biological function. Functioning and well-behaving biological systems adhere to a certain set of biological "rules". Data exchange standards and Bio-Design Automation (BDA) tools support the organization of part libraries and the exploration of rule-compliant compositions. In this work, we formally define a design specification language, enabling the integration of biological rules into the Synthetic Biology engineering process. The supported rules are divided into five categories: Counting, Pairing, Positioning, Orientation, and Interactions. We formally define the semantics of each rule, characterize the language's expressive power, and perform a case study in that we iteratively design a genetic Priority Encoder circuit following two alternative paradigms-rule-based and template-driven. Ultimately, we touch a method to approximate the complexity and time to computationally enumerate all rule-compliant designs. Our specification language may or may not be expressive enough to capture all designs that a Synthetic Biologist might want to describe, or the complexity one might find through experiments. However, computational support for the acquisition, specification, management, and application of biological rules is inevitable to understand the functioning of biology.
ACM Reference Format:Ernst Oberortner, Swapnil Bhatia, Erik Lindgren, and Douglas Densmore. 2014. A rule-based design specification language for synthetic biology.
X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence (e.g., deep learning) have been proposed as solutions. However, typically, such solutions are overconfident when subjected to new data far from the training data, so-called out-of-distribution (OOD) data; we claim that safe automatic interpretation of industrial X-ray images, as part of quality control of critical products, requires a robust confidence estimation with respect to OOD data. We explored if such a confidence estimation, an OOD detector, can be achieved by explicit modeling of the training data distribution, and the accepted images. For this, we derived an autoencoder model trained unsupervised on a public dataset with X-ray images of metal fusion welds and synthetic data. We explicitly demonstrate the dangers with a conventional supervised learning-based approach and compare it to the OOD detector. We achieve true positive rates of around 90% at false positive rates of around 0.1% on samples similar to the training data and correctly detect some example OOD data.
This article presents an algorithm that handles the detection, positioning, and sizing of submillimeter-sized pores in welds using radiographic inspection and tracking. The possibility to detect, position, and size pores which have a low contrast-to-noise ratio increases the value of the nondestructive evaluation of welds by facilitating fatigue life predictions with lower uncertainty. In this article, a multiple hypothesis tracker with an extended Kalman filter is used to track an unknown number of pore indications in a sequence of radiographs as an object is rotated. Each pore is not required to be detected in all radiographs. In addition, in the tracking step, three-dimensional (3-D) positions of pore defects are calculated. To optimize, set up, and pre-evaluate the algorithm, the article explores a design of experimental approach in combination with synthetic radiographs of titanium laser welds containing pore defects. The pre-evaluation on synthetic radiographs at industrially reasonable contrast-to-noise ratios indicate less than 1% false detection rates at high detection rates and less than 0.1 mm of positioning errors for more than 90% of the pores. A comparison between experimental results of the presented algorithm and a computerized tomography reference measurement shows qualitatively good agreement in the 3-D positions of approximately 0.1-mm diameter pores in 5-mm-thick Ti-6242.
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