Evaluation of amikacin use and comparison of the models implemented in two Bayesian forecasting software packages to guide dosing.

Aims: Bayesian forecasting software can assist in guiding therapeutic drug monitoring (TDM)-based dose adjustments for amikacin to achieve therapeutic targets. This study aimed to evaluate amikacin prescribing and TDM practices, and to determine the suitability of the amikacin model incorporated into the DoseMeRx® software as a replacement for the previously available software (Abbottbase®).

Methods: Patient demographics, pathology, amikacin dosing history, amikacin concentrations and Abbottbase® predicted TDM targets (area under the curve up to 24 hours, maximum concentration and trough concentration) were collected for adults receiving intravenous amikacin (2012-2017).

Voriconazole: an audit of hospital-based dosing and monitoring and evaluation of the predictive performance of a dose-prediction software package.

Background: Therapeutic drug monitoring (TDM) is recommended to guide voriconazole therapy.

Objectives: To determine compliance of hospital-based voriconazole dosing and TDM with the Australian national guidelines and evaluate the predictive performance of a one-compartment population pharmacokinetic voriconazole model available in a commercial dose-prediction software package.

Methods: A retrospective audit of voriconazole therapy at an Australian public hospital (1 January to 31 December 2016) was undertaken.

NFIX regulates neural progenitor cell differentiation during hippocampal morphogenesis.

Neural progenitor cells have the ability to give rise to neurons and glia in the embryonic, postnatal and adult brain. During development, the program regulating whether these cells divide and self-renew or exit the cell cycle and differentiate is tightly controlled, and imbalances to the normal trajectory of this process can lead to severe functional consequences. However, our understanding of the molecular regulation of these fundamental events remains limited.

Genome-wide in silico prediction of gene expression.

Motivation: Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang et al. in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using in vivo ChIP-seq measurements of TF binding.

Expression of nuclear factor one A and -B in the olfactory bulb.

The nuclear factor one (NFI) family of transcription factors consists of four members in vertebrates, NFIA, NFIB, NFIC, and NFIX, which share a highly conserved N-terminal DNA-binding domain. NFI genes are widely expressed in the developing mouse brain, and mouse mutants lacking NFIA, NFIB, or NFIX exhibit developmental deficits in several areas, including the cortex, hippocampus, pons, and cerebellum. Here we analyzed the expression of NFIA and NFIB in the developing and adult olfactory bulb (OB), rostral migratory stream (RMS), and subventricular zone (SVZ).

Epigenetic priors for identifying active transcription factor binding sites.

Motivation: Accurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored.

Results: We describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs).

Tissue-specific prediction of directly regulated genes.

Unlabelled: Direct binding by a transcription factor (TF) to the proximal promoter of a gene is a strong evidence that the TF regulates the gene. Assaying the genome-wide binding of every TF in every cell type and condition is currently impractical. Histone modifications correlate with tissue/cell/condition-specific ('tissue specific') TF binding, so histone ChIP-seq data can be combined with traditional position weight matrix (PWM) methods to make tissue-specific predictions of TF-promoter interactions.

Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data.

Background: A major goal of molecular biology is determining the mechanisms that control the transcription of genes. Motif Enrichment Analysis (MEA) seeks to determine which DNA-binding transcription factors control the transcription of a set of genes by detecting enrichment of known binding motifs in the genes' regulatory regions. Typically, the biologist specifies a set of genes believed to be co-regulated and a library of known DNA-binding models for transcription factors, and MEA determines which (if any) of the factors may be direct regulators of the genes.