Hi-C implementation of genome structure for in silico models of radiation-induced DNA damage

Developments in the genome organisation field has resulted in the recent methodology to infer spatial conformations of the genome directly from experimentally measured genome contacts (Hi-C data). This provides a detailed description of both intra- and inter-chromosomal arrangements. Chromosomal intermingling is an important driver for radiation-induced DNA mis-repair. Which is a key biological endpoint of relevance to the fields of cancer therapy (radiotherapy), public health (biodosimetry) and space travel. For the first time, we leverage these methods of inferring genome organisation and couple them to nano-dosimetric radiation track structure modelling to predict quantities and distribution of DNA damage within cell-type specific geometries. These nano-dosimetric simulations are highly dependent on geometry and are benefited from the inclusion of experimentally driven chromosome conformations. We show how the changes in Hi-C contract maps impact the inferred geometries resulting in significant differences in chromosomal intermingling. We demonstrate how these differences propagate through to significant changes in the distribution of DNA damage throughout the cell nucleus, suggesting implications for DNA repair fidelity and subsequent cell fate. We suggest that differences in the geometric clustering for the chromosomes between the cell-types are a plausible factor leading to changes in cellular radiosensitivity. Furthermore, we investigate changes in cell shape, such as flattening, and show that this greatly impacts the distribution of DNA damage. This should be considered when comparing in vitro results to in vivo systems. The effect may be especially important when attempting to translate radiosensitivity measurements at the experimental in vitro level to the patient or human level.

The Information Bottleneck and Geometric Clustering

The information bottleneck (IB) approach to clustering takes a joint distribution [Formula: see text] and maps the data [Formula: see text] to cluster labels [Formula: see text], which retain maximal information about [Formula: see text] (Tishby, Pereira, & Bialek, 1999 ). This objective results in an algorithm that clusters data points based on the similarity of their conditional distributions [Formula: see text]. This is in contrast to classic geometric clustering algorithms such as [Formula: see text]-means and gaussian mixture models (GMMs), which take a set of observed data points [Formula: see text] and cluster them based on their geometric (typically Euclidean) distance from one another. Here, we show how to use the deterministic information bottleneck (DIB) (Strouse & Schwab, 2017 ), a variant of IB, to perform geometric clustering by choosing cluster labels that preserve information about data point location on a smoothed data set. We also introduce a novel intuitive method to choose the number of clusters via kinks in the information curve. We apply this approach to a variety of simple clustering problems, showing that DIB with our model selection procedure recovers the generative cluster labels. We also show that, in particular limits of our model parameters, clustering with DIB and IB is equivalent to [Formula: see text]-means and EM fitting of a GMM with hard and soft assignments, respectively. Thus, clustering with (D)IB generalizes and provides an information-theoretic perspective on these classic algorithms.

Dispersion of High Concentrations of Carbon Nanofibers in Portland Cement Mortars

This research focuses on creating and maintaining a stable dispersion of carbon nanofibers (CNFs) in portland cement based materials. A microfine cement is used in conjunction with an untraditional dispersion method to encourage and stabilize the dispersion of CNFs in concentrations up to 5% by mass of cement. A computational simulation was utilized to examine an effect called geometric clustering on the dispersion of CNFs among Type I/II and microfine cement grains. The geometric clustering simulation revealed a higher achievable dispersion for microfine cement than for Type I/II cement. Scanning electron microscopy (SEM) was used to quantify the dispersion of CNFs among Type I/II and microfine cement grains. SEM image analysis indicated excessive CNF clumping among Type I/II cement grains, while the dispersion of hybrid microfine cement mortar continued to improve as the concentration of CNFs increased up to 5% by mass of cement. Mortar cube elastic stiffness and mortar prism flexure tests revealed that high concentrations of CNFs had detrimental effects in hybrid Type I/II cement mortar, whereas similar concentrations of CNFs had negligible or beneficial effects in hybrid microfine cement mortar.

Role of arginine 209 in the conformational transition of the protein kinase A regulatory subunit RIα A-domain

Using the combination of molecular dynamics (MD) simulations and geometric clustering we analyzed the role of arginine at 209 position in the transition of protein kinase A Iα (PKA Iα) regulatory subunit A-domain from H- to B-conformation and stabilization of the latter. The mechanism underlying the role of the residue at position 209 in the realization of B-conformation includes: (1) possibility to bind the ligand tightly (if transition happens in the presence of cAMP), (2) capability to hold β2β3-loop in the correct conformation, (3) tendency of residue at 209 position to stabilize B-conformation in the absence and in presence of the ligand. In terms of the effect produced on transition of A-domain from H- to B-conformation in the presence of cAMP, mutational substitutions for R209 can be arranged in the following order: Glu(Gly)>Lys>Ile. In the absence of cAMP the order is different Lys>Gly>Glu>Ile. Thus, our results allow us to presume that the role of arginine at 209 position can be important though not crucial.