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Biology subjects

Aquilina, M.

Publications and source records attributed to Aquilina, M..

4 recordsLinked to original sources

OrthoSeq: A Design Workflow for Thermodynamically Orthogonal DNA Sequence-Pair Libraries

Programmable DNA hybridization underlies many technologies in DNA nanotechnology, fluorescence imaging, and synthetic DNA sequence assembly. A common design challenge is to generate large sequence libraries in which each strand binds strongly to its intended partner while avoiding cross-hybridization and self-folding. Here, we introduce OrthoSeq, a workflow for designing thermodynamically orthogonal DNA sequence-pair libraries under user-defined experimental conditions. OrthoSeq uses NUPACK to evaluate intended binding, cross-hybridization, and self-folding. Within OrthoSeq, candidate sequence pairs form vertices in a conflict graph, while pairwise cross-hybridization conflicts define the edges. Library selection is then formulated as an independent-set problem and addressed using search strategies tailored to the computational regime considered here, in which thermodynamic evaluations dominate the computational cost. In benchmark comparisons, these strategies identify larger sequence-pair libraries than the commonly employed sequential candidate-addition strategy under the same thermodynamic constraints and computational budget. We further show that sequence-level barcode libraries can serve as candidate pools for thermodynamic refinement with OrthoSeq. To support practical use, OrthoSeq provides a graphical user interface that implements the complete workflow. Altogether, OrthoSeq provides an application-agnostic framework for designing DNA sequence-pair libraries under explicit thermodynamic constraints.

bioinformatics↗

A computational framework for designing micron-scale crisscross DNA megastructures

Crisscross polymerization enables the assembly of hundreds of unique DNA origami slats into micron-sized structures with nanoscale precision. To design these megastructures, thousands of handle sequences from a fixed library must be assigned to individual slats to encode the desired binding architecture. This complexity presents two major challenges: handles must be selected to minimize parasitic interactions that compete with desired assembly, and the fabrication of hundreds of unique slats creates a substantial logistical burden. Here, we develop a unified framework that standardizes the design and fabrication of crisscross megastructures. We use an evolutionary algorithm to optimize handle assignment and minimize parasitic binding between slats, paired with a graph-based algorithm that expands the handle library. Together, these algorithms enable the assembly of large, multi-layered megastructures that otherwise would be produced at negligible yields. We have released this framework as #-CAD, an open-source graphical application that integrates these algorithms, streamlines laboratory workflows, and makes crisscross DNA origami more broadly accessible.

synthetic biology↗

GelGenie: an AI-powered framework for gel electrophoresis image analysis

Gel electrophoresis is a ubiquitous laboratory method for the separation and semi-quantitative analysis of biomolecules. However, gel image analysis principles have barely advanced for decades, in stark contrast to other fields where AI has revolutionised data processing. Here, we show that an AI-based system can automatically identify gel bands in seconds for a wide range of experimental conditions, far surpassing the capabilities of current software. We used a dataset containing 500+ images of manually-labelled gels to train various U-Nets to accurately identify bands through segmentation, i.e. classifying pixels as band or background. When applied to gel electrophoresis data from other laboratories, our system generated results that quantitatively matched those of the original authors. We have publicly released our models through GelGenie, an open-source application that allows users to extract bands from gel images on their own devices, with no expert knowledge or experience required.

molecular biology↗

Multiplexed Label-Free Biomarker Detection by Targeted Disassembly of Variable-Length DNA Payload Chains

Simultaneously studying different types of biomarkers (DNA, RNA, proteins, metabolites) has the potential to significantly improve understanding and diagnosis for many complex diseases. However, extracting biomarkers of different types involves using several technically complex or expensive methodologies, often requiring specialized laboratories and personnel. Streamlining detection through the use of a single multiplexed assay would greatly facilitate the process of accessing and interpreting patient biomarker data. In this work, we present a method for multiplexed biomarker detection based on variable-length DNA payload chains, which are systematically disassembled in the presence of specific biomolecular targets, leading to fragments of different sizes that yield characteristic band patterns in gel electrophoresis. This strategy has enabled us to detect with high sensitivity and specificity DNA sequences including BRCA1, an RNA sequence (miR-141) and the steroid aldosterone. We show that our assay can be multiplexed, enabling simultaneous detection of different types of biomarker. Furthermore, we show that our method suffers no loss of sensitivity when conducted in fetal bovine serum and can be applied using capillary electrophoresis, which may be more amenable to automation and integration in healthcare settings. ToC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/485867v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@12b4169org.highwire.dtl.DTLVardef@d36ebdorg.highwire.dtl.DTLVardef@10b69e7org.highwire.dtl.DTLVardef@55dc00_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗