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      Materials design by synthetic biology

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          The biofilm matrix.

          The microorganisms in biofilms live in a self-produced matrix of hydrated extracellular polymeric substances (EPS) that form their immediate environment. EPS are mainly polysaccharides, proteins, nucleic acids and lipids; they provide the mechanical stability of biofilms, mediate their adhesion to surfaces and form a cohesive, three-dimensional polymer network that interconnects and transiently immobilizes biofilm cells. In addition, the biofilm matrix acts as an external digestive system by keeping extracellular enzymes close to the cells, enabling them to metabolize dissolved, colloidal and solid biopolymers. Here we describe the functions, properties and constituents of the EPS matrix that make biofilms the most successful forms of life on earth.
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            Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression.

            Targeted gene regulation on a genome-wide scale is a powerful strategy for interrogating, perturbing, and engineering cellular systems. Here, we develop a method for controlling gene expression based on Cas9, an RNA-guided DNA endonuclease from a type II CRISPR system. We show that a catalytically dead Cas9 lacking endonuclease activity, when coexpressed with a guide RNA, generates a DNA recognition complex that can specifically interfere with transcriptional elongation, RNA polymerase binding, or transcription factor binding. This system, which we call CRISPR interference (CRISPRi), can efficiently repress expression of targeted genes in Escherichia coli, with no detectable off-target effects. CRISPRi can be used to repress multiple target genes simultaneously, and its effects are reversible. We also show evidence that the system can be adapted for gene repression in mammalian cells. This RNA-guided DNA recognition platform provides a simple approach for selectively perturbing gene expression on a genome-wide scale. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Machine learning for molecular and materials science

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                Author and article information

                Contributors
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                Journal
                Nature Reviews Materials
                Nat Rev Mater
                Springer Science and Business Media LLC
                2058-8437
                December 23 2020
                Article
                10.1038/s41578-020-00265-w
                a280c749-c564-461c-ac6e-19a64d60dd34
                © 2020

                http://www.springer.com/tdm

                http://www.springer.com/tdm

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