We investigate performance and optimization of the "digital" bioanalytical response. Specifically, we consider the recently introduced approach of a partial input conversion into inactive compounds, resulting in the "branch point effect" similar to that encountered in biological systems. This corresponds to an "intensity filter," which can yield a binary-type sigmoid-response output signal of interest in information and signal processing and in biosensing applications. We define measures for optimizing the response for information processing applications based on the kinetic modeling of the enzymatic reactions involved, and apply the developed approach to the recently published data for glucose detection.

Polymer molecular weight distributions are targeted through kinetic modeled with high fidelity based on the temporal control of chain initiation in anionic polymerizations.

Summary PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. Availability and implementation PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). Supplementary information Supplementary data are available at Bioinformatics

Background: Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq, however, has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq. This makes difficult not only the clustering of cells, but also the assignment of the resulting clusters into predefined cell types based on known molecular signatures, such as the expression of characteristic cell surface markers. Results:We present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for biases due to dropout errors and imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a recently-published manually-curated cell marker database [1], and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a python package at https://github.com/sdomanskyi/DigitalCellSorterConclusions: This methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.

Saliva omics has immense potential for non-invasive diagnostics, including monitoring very young or elderly populations, or individuals in remote locations. In this study, multiple saliva omics from an individual were monitored over three periods (100 timepoints) involving: (1) hourly sampling over 24 h without intervention, (2) hourly sampling over 24 h including immune system activation using the standard 23-valent pneumococcal polysaccharide vaccine, (3) daily sampling for 33 days profiling the post-vaccination response. At each timepoint total saliva transcriptome and proteome, and small RNA from salivary extracellular vesicles were profiled, including mRNA, miRNA, piRNA and bacterial RNA. The two 24-h periods were used in a paired analysis to remove daily variation and reveal vaccination responses. Over 18,000 omics longitudinal series had statistically significant temporal trends compared to a healthy baseline. Various immune response and regulation pathways were activated following vaccination, including interferon and cytokine signaling, and MHC antigen presentation. Immune response timeframes were concordant with innate and adaptive immunity development, and coincided with vaccination and reported fever. Overall, mRNA results appeared more specific and sensitive (timewise) to vaccination compared to other omics. The results suggest saliva omics can be consistently assessed for non-invasive personalized monitoring and immune response diagnostics.

We study the mechanisms involved in the release, triggered by the application of glucose, of insulin entrapped in Fe -cross-linked alginate hydrogel particles further stabilized with a polyelectrolyte. Platelet-shaped alginate particles are synthesized containing enzyme glucose oxidase conjugated with silica nanoparticles, which are also entrapped in the hydrogel. Glucose diffuses in from solution, and production of hydrogen peroxide is catalyzed by the enzyme within the hydrogel. We argue that, specifically for the Fe -cross-linked systems, the produced hydrogen peroxide is further converted to free radicals via a Fenton-type reaction catalyzed by the iron cations. The activity of free radicals, as well as the reduction of Fe by the enzyme, and other mechanisms contribute to the decrease in density of the hydrogel. As a result, while the particles remain intact, void sizes increase and release of insulin ensues and is followed experimentally. A theoretical description of the involved processes is proposed and utilized to fit the data. It is then used to study the long-time properties of the release process that offers a model for designing new drug-release systems.

A recently experimentally observed biochemical "threshold filtering" mechanism by processes catalyzed by the enzyme malate dehydrogenase is explained in terms of a model that incorporates an unusual mechanism of inhibition of this enzyme that has a reversible mechanism of action. Experimental data for a system in which the output signal is produced by biocatalytic processes of the enzyme glucose dehydrogenase are analyzed to verify the model's validity. We also establish that fast reversible conversion of the output product to another compound, without the additional inhibition, cannot on its own result in filtering.

Biocatalytic cascades involving more than one or two enzyme‐catalyzed step are inefficient inside alginate hydrogel prepared on an electrode surface. The problem originates from slow diffusion of intermediate products through the hydrogel from one enzyme to another. However, enzyme activity can be improved by surface immobilization. We demonstrate that a complex cascade of four consecutive biocatalytic reactions can be designed, with the enzymes immobilized in an LBL‐assembled polymeric layer at the alginate‐modified electrode surface. The product, hydrogen peroxide, then induces dissolution of iron‐cross‐linked alginate, which results in release process of entrapped biomolecular species, here fluorescently marked oligonucleotides, denoted F‐DNA. The enzymatic cascade can be viewed as a biocomputing network of concatenated AND gates, activated by combinations of four chemical input signals, which trigger the release of F‐DNA. The reactions, and diffusion/release processes were investigated by means of theoretical modeling. A bottleneck reaction step associated with one of the enzymes was observed. The developed system provides a model for biochemical actuation triggered by a biocomputing network of reactions.