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(B) To find the edges, bacteria in the dark produce a communication signal (green circles) that diffuses across the dark/light boundary. The lawn computes the edges, or boundaries between light and dark regions, and visually presents the output.
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(A) Light is projected through a mask onto a large community (lawn) of bacteria grown on a Petri dish. In this design, each bacterium (up to 10 9 individuals for a 90mm Petri dish image) processes a small amount of local information simultaneously, and the population cooperates to find the edges.īacterial edge detection. We aimed to implement a parallel edge detection algorithm wherein each bacterium within a spatially distributed population functions as an independent signal processor.
INVERT PDF COLORS EDGE SERIAL
The serial nature of this search process results in a computation time that increases linearly with the number of pixels in the image. If any of the neighbors is black, the algorithm classifies those pixels as being part of an edge. For a digital black and white image, a typical algorithm operates by scanning for a white pixel and then comparing the intensity of that pixel to its eight neighboring pixels. This process reduces the information content in a complex image and is used in applications ranging from retinal preprocessing ( Maturana and Frenk, 1963) to the analysis of microarray data ( Kim et al., 2001). Predictive models for the design of genetic programs will drive applications in biotechnology and aid bottom-up studies of natural regulatory systems.Įdge detection is a well-studied computational problem used to determine the boundaries of objects within an image ( Suel et al., 2000). The characterization of transfer functions, or the quantitative relationship between circuit input(s) and output(s) ( Bintu et al., 2005a Tabor et al., 2009 Voigt, 2006 Weiss et al., 1999) will aid the development of accurate mathematical models ( Ajo-Franklin et al., 2007 Guido et al., 2006) which will allow complex genetic programs to be examined in silico prior to physical construction. The current challenge is to assemble multiple genetic circuits into larger programs for the engineering of more sophisticated behaviors (P.
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These parts can be combined to construct genetic versions of electronic circuits, including switches ( Atkinson et al., 2003 Gardner et al., 2000 Kramer and Fussenegger, 2005 Kramer et al., 2004), logic ( Anderson et al., 2007 Guet et al., 2002 Rackham and Chin, 2005), memory ( Ajo-Franklin et al., 2007 Gardner et al., 2000 Ham et al., 2006), pulse generators ( Basu et al., 2004), and oscillators ( Atkinson et al., 2003 Elowitz and Leibler, 2000 Fung et al., 2005 Stricker et al., 2008 Tigges et al., 2009). Living cells can be programmed with genetic parts, such as promoters, transcription factors and metabolic genes ( Andrianantoandro et al., 2006 Benner and Sismour, 2005 Canton et al., 2008 Endy, 2005 Haseltine and Arnold, 2007). Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.
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A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. An engineered light sensor enables cells to distinguish between light and dark regions. The algorithm is implemented using multiple genetic circuits. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs.