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Designs for scaffolded DNA origami nanostructures are commonly and minimally published as the list of DNA staple and scaffold sequences required. In nearly all cases, high-level editable design files (e.g. caDNAno) which generated the low-level sequences are not made available. This de facto ‘raw sequence’ exchange format allows published origami designs to be re-attempted in the laboratory by other groups, but effectively stops designs from being significantly modified or re-purposed for new future applications. To make the raw sequence exchange format more accessible to further design and engineering, in this work we propose the first algorithmic solution to the inverse problem of converting staple/scaffold sequences back to a ‘guide schematic’ resembling the original origami schematic. The guide schematic can be used to aid the manual re-input of an origami into a CAD tool like caDNAno, hence recovering a high-level editable design file. Creation of a guide schematic can also be used to double check that a list of staple strand sequences does not have errors and indeed does assemble into a desired origami nanostructure prior to costly laboratory experimentation. We tested our reverse algorithm on 36 diverse origami designs from the literature and found that 29 origamis (81 %) had a good quality guide schematic recovered from raw sequences. Our software is made available at https://revnano.readthedocs.io.

Full traceability and transparency are important to establish trust in engineered cell lines. Here the authors argue that version control for cell engineering marks a significant step toward more open, reproducible, traceable and ultimately more trustworthy engineering biology.

DNA is becoming increasingly used as a medium to store non-genetic information. Here the authors present a dynamic stack data structure implemented as a DNA polymer chemistry able to record and retrieve signals in a last-in first-out order.

Binary light-up aptamers are intriguing and emerging tools with potential in different fields. Herein, we demonstrate the versatility of a split Broccoli aptamer system able to turn on the fluorescence signal only in the presence of a complementary sequence. First, an RNA three-way junction harbouring the split system is assembled in an E. coli-based cell-free TX-TL system where the folding of the functional aptamer is demonstrated. Then, the same strategy is introduced into a ‘bio-orthogonal’ hybrid RNA/DNA rectangle origami characterized by atomic force microscopy: the activation of the split system through the origami self-assembly is demonstrated. Finally, our system is successfully used to detect the femtomoles of a Campylobacter spp. DNA target sequence. Potential applications of our system include the real-time monitoring of the self-assembly of nucleic-acid-based devices in vivo and of the intracellular delivery of therapeutic nanostructures, as well as the in vitro and in vivo detection of different DNA/RNA targets.

Partitioning proteins are well studied as molecular organizers of chromosome and plasmid segregation during division, however little is known about the roles partitioning proteins can play within type IV secretion systems. The single-stranded DNA (ssDNA)-secreting gonococcal T4SS has two partitioning proteins, ParA and ParB. These proteins work in collaboration with the relaxase TraI as essential facilitators of type IV secretion. Bacterial two-hybrid experiments identified interactions between each partitioning protein and the relaxase. Subcellular fractionation demonstrated that ParA is found in the cellular membrane, whereas ParB is primarily in the membrane, but some of the protein is in the soluble fraction. Since TraI is known to be membrane-associated, these data suggest that the gonococcal relaxosome is a membrane-associated complex. In addition, we found that translation of ParA and ParB is controlled by an RNA switch. Different mutations within the stem-loop sequence predicted to alter folding of this RNA structure greatly increased or decreased levels of the partitioning proteins.

Mutations in pre-mRNA processing factors cause autosomal dominant retinitis pigmentosa. Here the authors provide insights into the pathophysiological mechanisms underlying non-syndromic retinal disease caused by heterozygous mutations in genes encoding ubiquitously expressed splicing factors.

Abstract RNA presents intringuing roles in many cellular processes and its versatility underpins many different applications in synthetic biology. Nonetheless, RNA origami as a method for nanofabrication is not yet fully explored and the majority of RNA nanostructures are based on natural pre-folded RNA. Here we describe a biologically inert and uniquely addressable RNA origami scaffold that self-assembles into a nanoribbon by seven staple strands. An algorithm is applied to generate a synthetic De Bruijn scaffold sequence that is characterized by the lack of biologically active sites and repetitions larger than a predetermined design parameter. This RNA scaffold and the complementary staples fold in a physiologically compatible isothermal condition. In order to monitor the folding, we designed a new split Broccoli aptamer system. The aptamer is divided into two nonfunctional sequences each of which is integrated into the 5′ or 3′ end of two staple strands complementary to the RNA scaffold. Using fluorescence measurements and in-gel imaging, we demonstrate that once RNA origami assembly occurs, the split aptamer sequences are brought into close proximity forming the aptamer and turning on the fluorescence. This light-up ‘bio-orthogonal’ RNA origami provides a prototype that can have potential for in vivo origami applications.

An intriguing question in evolution is what would happen if one could "replay" life's tape. Here, we explore the following hypothesis: when replaying the tape, the details ("decorations") of the outcomes would vary but certain "invariants" might emerge across different life-tapes sharing similar initial conditions. We use large-scale simulations of an in silico model of pre-biotic evolution called GARD (Graded Autocatalysis Replication Domain) to test this hypothesis. GARD models the temporal evolution of molecular assemblies, governed by a rates matrix (i.e. network) that biases different molecules' likelihood of joining or leaving a dynamically growing and splitting assembly. Previous studies have shown the emergence of so called compotypes, i.e., species capable of replication and selection response. Here, we apply networks' science to ascertain the degree to which invariants emerge across different life-tapes under GARD dynamics and whether one can predict these invariant from the chemistry specification alone (i.e. GARD's rates network representing initial conditions). We analysed the (complex) rates' network communities and asked whether communities are related (and how) to the emerging species under GARD's dynamic, and found that the communities correspond to the species emerging from the simulations. Importantly, we show how to use the set of communities detected to predict species emergence without performing any simulations. The analysis developed here may impact complex systems simulations in general.

MC-PSC (Multi-Criteria Protein Structure Comparison) is one of the GCAs (Grand Challenge Applications) in the field of structural proteomics. The solution of the MC-PSC grand challenge requires the use of distributed algorithms, architectures and environments. This paper is aimed at the analysis of the scalability of our newly developed distributed algorithm for MC-PSC in the grid environment. The scalability in the grid environment indicates the capacity of the distributed algorithm to effectively utilize an increasing number of processors across multiple sites. The results of the experiments conducted on the UK's NGS (National Grid Service) infrastructure are reported in terms of speedup, efficiency and cross-site communication overhead.

Autocatalytic sets are self-sustaining and collectively catalytic chemical reaction networks which are believed to have played an important role in the origin of life. Simulation studies have shown that autocatalytic sets are, in principle, evolvable if multiple autocatalytic subsets can exist in different combinations within compartments, i.e., so-called protocells. However, these previous studies have so far not explicitly modeled the emergence and dynamics of autocatalytic sets in populations of compartments in a spatial environment. Here, we use a recently developed software tool to simulate exactly this scenario, as an important first step towards more realistic simulations and experiments on autocatalytic sets in protocells.

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly.

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

The 3-dimensional representation and inspection of complex data is a frequently used strategy in many data analysis domains. Existing data mining software often lacks functionality that would enable users to explore 3D data interactively, especially if one wishes to make dynamic graphical representations directly viewable on the web.In this paper we present vrmlgen, a software package for the statistical programming language R to create 3D data visualizations in web formats like the Virtual Reality Markup Language (VRML) and LiveGraphics3D. vrmlgen can be used to generate 3D charts and bar plots, scatter plots with density estimation contour surfaces, and visualizations of height maps, 3D object models and parametric functions. For greater flexibility, the user can also access low-level plotting methods through a unified interface and freely group different function calls together to create new higher-level plotting methods. Additionally, we present a web tool allowing users to visualize 3D data online and test some of vrmlgen's features without the need to install any software on their computer.