Automated design of thousands of nonrepetitive parts for engineering stable genetic systems

Hossain, Ayaan; Lopez, Eriberto; Halper, Sean M.; Cetnar, Daniel P.; Reis, Alexander C.; Strickland, Devin; Klavins, Eric; Salis, Howard M.

doi:10.1038/s41587-020-0584-2

Cited by 97 publications

(77 citation statements)

References 47 publications

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“…How, then, can we design stable, non-repetitive genetic systems with a limited toolkit of synthetic parts? Researchers in Howard Salis’s lab at Pennsylvania State University set out to address this challenge through the Non-Repetitive Parts Calculator (NRPC), a set of new algorithms described in a recent publication by Hossain et al ( 2 ) and available online ( https://salislab.net/software/ ).…”

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confidence: 99%

Designing for durability: new tools to build stable, non-repetitive DNA

Cárdenas

2020

Synthetic Biology

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confidence: 99%

Designing for durability: new tools to build stable, non-repetitive DNA

Cárdenas

2020

Synthetic Biology

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“…We created a non-repetitive promoter toolbox for synthetic biology encompassing 4350 E. coli σ 70 promoter sequences such that no sequence had more than 10 consecutive nucleotides in common with another. [16] We kept the spacer length constant at 17nt and promoter length constant at 78nt, but varied GC content and sequence throughout the promoter. In particular, we included a variety of sequences deviating from the consensus -35 and -10 hexamer regions of the promoter, ranging from zero to twelve mismatches from consensus in these regions.…”

Section: Predicting Promoter Strength For More Complex Sequencesmentioning

confidence: 99%

“…We next examine the CNN more directly to better understand the features it identified as important for promoter strength. Model performance predicting promoter strength for a non-repetitive promoter library [16] A) Distribution of promoter transcription rate (log10-transformed) in this dataset. B) CNN model predictions for held-out test data versus the observed transcription rate.…”

Section: Predicting Promoter Strength For More Complex Sequencesmentioning

confidence: 99%

“…The non-repetitive σ 70 promoter library was designed and transcription strength was measured in previous work. [16] Briefly, promoter oligos were spliced into vectors and transformed into E. coli. Total RNA and DNA were extracted from 2 biological replicates and sequenced on an Illumina HiSeq.…”

Section: Promoter Strength Datasetsmentioning

confidence: 99%

“…Convolutional kernels were 7x6: the 7 nucleotide channels represent the 4 DNA nucleotides plus J for start, O for end, and X for padding, and a filter width of 6 was chosen to fully capture a sequence hexamer motif within a kernel. The promoter sequence was therefore modeled as a 7-channel matrix with width N. The N dimension is the promoter length-78 for [16] and 150 for [11] -plus one full convolutional filter-width of padding and a start/end nucleotide marker on both sides of the sequence. The log10 transform of the transcription rate was used as the independent variable.…”

Section: Cnn Model Architecture and Evaluationmentioning

confidence: 99%

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Convolutional neural net learns promoter sequence features driving transcription strength

Leiby¹,

Hossain

Salis

EPiC Series in Computing

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Promoters drive gene expression and help regulate cellular responses to the environment. In recent research, machine learning models have been developed to predict a bacterial promoter’s transcriptional initiation rate, although these models utilize expert-labeled sequence elements across a defined set of DNA building blocks. The generalizability of these methods is therefore limited by the necessary labeling of the specific components studied. As a result, current models have not been used to predict the transcriptional initiation rates of promoters with generalized nucleotide sequences. If generalizable models existed, they could greatly facilitate the design of synthetic genetic circuits with well-controlled transcription rates in bacteria.To address these limitations, we used a convolutional neural network (CNN) to predict a promoter’s transcriptional initiation rate directly from its DNA nucleotide sequence. We first evaluated the model on a published promoter component dataset. Trained using only the sequence as input, our model fits held-out test data with R2 = 0.90, comparable to published models that fit expert-labeled sequence elements.We produced a new promoter strength dataset including non-repetitive promoters with high sequence variation and not limited to combinations of discrete expert-labeled components. Our CNN trained on this more varied dataset fits held-out promoter strength with R2 = 0.61. Previously-published models are intractable on a dataset like this with highly diverse inputs. The CNN outperforms classical approach baselines like LASSO on a bag of words for promoter sequence elements (R2 = 0.42).We applied recent machine learning approaches to quantify the contribution of individual nucleotides to the CNN's promoter strength prediction. Learning directly from DNA sequence, our model identified the consensus -35 and -10 hexamer regions as well as the discriminator element as keycontributorstoσ70promoterstrength.Italsoreplicatedafindingthataperfectconsensus sequence match does not yield the strongest promoter.The model's ability to independently learn biologically-relevant information directly from sequence, while performing similarly to or better than classical methods, makes it appealing for further prediction optimization and research into generalizability. This approach may be useful for synthetic promoter design, as well as for sequence feature identification.

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Exploring the Application and Prospects of Synthetic Biology in Engineered Living Materials

Wang,

Hu,

et al. 2023

Advanced Materials

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At the intersection of synthetic biology and materials science, engineered living materials (ELMs) exhibit unprecedented potential. Possessing unique “living” attributes, ELMs represent a significant paradigm shift in material design, showcasing self‐organization, self‐repair, adaptability, and evolvability, surpassing conventional synthetic materials. This review focuses on reviewing the applications of ELMs derived from bacteria, fungi, and plants in environmental remediation, eco‐friendly architecture, and sustainable energy. The review provides a comprehensive overview of the latest research progress and emerging design strategies for ELMs in various application fields from the perspectives of synthetic biology and materials science. In addition, the review provides valuable references for the design of novel ELMs, extending the potential applications of future ELMs. The investigation into the synergistic application possibilities amongst different species of ELMs offers beneficial reference information for researchers and practitioners in this field. Finally, future trends and development challenges of synthetic biology for ELMs in the coming years are discussed in detail.This article is protected by copyright. All rights reserved

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Automated design of thousands of nonrepetitive parts for engineering stable genetic systems

Cited by 97 publications

References 47 publications

Designing for durability: new tools to build stable, non-repetitive DNA

Designing for durability: new tools to build stable, non-repetitive DNA

Convolutional neural net learns promoter sequence features driving transcription strength

Exploring the Application and Prospects of Synthetic Biology in Engineered Living Materials

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