Biology subjects

Densmore, D.

Publications and source records attributed to Densmore, D..

4 recordsLinked to original sources

bioRxiv2024

Cell-Free Recombinase Integrated Boolean Output System (CRIBOS)

Cell-free gene expression systems are increasingly important in fundamental research and biomanufacturing, offering a versatile platform for studying gene circuits and bio-computation. We present the Cell-Free Recombinase Integrated Boolean Output System (CRIBOS), a site-specific recombinase-based multiplex genetic circuit platform designed for cell-free environments. With CRIBOS, we build over 20 multi-input-multi-output circuits, including 2-input-1-output genetic circuits and a 2-input-4-output decoder. Combined with allosteric transcription factors (aTFs)-based sensors, the circuits demonstrate multiplex environmental sensing. Moreover, utilizing paper-based CRIBOS, which demonstrates remarkable portability and stability in a paper-based setting, we present a biological memory storage device that can preserve DNA-based biological information for over four months with minimal resources, energy costs, and maintenance requirements. Implementing CRIBOS not only expands the application of multiplex Boolean logic gates from cellular systems to the cell-free environment but also augments their overall versatility, opening new avenues for designing and applying sophisticated genetic circuits.

synthetic biology↗

bioRxiv2021

ICOR: Improving codon optimization with recurrent neural networks

BackgroundIn protein sequences--as there are 61 sense codons but only 20 standard amino acids-- most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli. ResultsWe demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICORs performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution. ConclusionsThe results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

synthetic biology↗

bioRxiv2020

Algorithms for the selection of fluorescent reporters

Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as excitation and emission spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through.

bioinformatics↗

bioRxiv2019

Repository-based plasmid design

There was an explosion in the amount of commercially available DNA in sequence repositories over the last decade. The number of such plasmids increased from 12,000 to over 300,000 among three of the largest repositories: iGEM, Addgene, and DNASU. The challenge in biodesign remains how to use these and other repository-based sequences effectively, correctly, and seamlessly. This work describes an approach to plasmid design where a plasmid is specified as a DNA sequence or list of features. The proposed software then finds the most cost-effective combination of synthetic and PCR-prepared repository fragments to build the plasmid via Gibson Assembly. It finds existing DNA sequences in user-specified and public DNA databases: iGEM, Addgene, and DNASU. Such a software application is introduced and characterized against all post-2005 iGEM composite parts and all Addgene vectors submitted in 2018 and found to reduce costs by 34% versus a purely synthetic plasmid design approach. The described software will improve current plasmid assembly workflows by shortening design times, improving build quality, and reducing costs.

bioinformatics↗