We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.
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Sci Rep
December 2024
Department of Neuroscience and Padova Neuroscience Center, Università di Padova, Padova, Italy.
Can focal brain lesions, such as those caused by stroke, disrupt critical brain dynamics? What biological mechanisms drive its recovery? In a recent study, we showed that focal lesions generate a sub-critical state that recovers over time in parallel with behavior (Rocha et al., Nat. Commun.
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December 2024
Department of Microbiology, Faculty of Sciences, CEI·MAR-International Campus of Excellence in Marine Science, University of Malaga, Málaga, Spain.
The inclusion of microalgae in functional fish diets has a notable impact on the welfare, metabolism and physiology of the organism. The microbial communities associated with the fish are directly influenced by the host's diet, and further understanding the impact on mucosal microbiota is needed. This study aimed to analyze the microbiota associated with the skin and gills of Sparus aurata fed a diet containing 10% microalgae.
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December 2024
School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia.
Sponges harbour complex microbiomes and as ancient metazoans and important ecosystem players are emerging as powerful models to understand the evolution and ecology of symbiotic interactions. Metagenomic studies have previously described the functional features of sponge symbionts, however, little is known about the metabolic interactions and processes that occur under different environmental conditions. To address this issue, we construct here constraint-based, genome-scale metabolic networks for the microbiome of the sponge Stylissa sp.
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December 2024
OMICS Laboratory, Department of Biotechnology, University of North Bengal, Siliguri, West Bengal, 734013, India.
Cadmium, a toxic heavy metal, poses significant global concern. A strain of the genus Pseudomonas, CD3, demonstrating significant cadmium resistance (up to 3 mM CdCl.HO) was identified from a pool of 26 cadmium-resistant bacteria isolated from cadmium-contaminated soil samples from Malda, India.
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December 2024
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional confocal fluorescence microscopy. Here, we demonstrate an explainable deep learning-based unsupervised inter-domain transformation of low-resolution unlabeled mid-infrared photoacoustic microscopy images into confocal-like virtually fluorescence-stained high-resolution images.
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