The Open Physiology workflow: modeling processes over physiology circuitboards of interoperable tissue units.

A key challenge for the physiology modeling community is to enable the searching, objective comparison and, ultimately, re-use of models and associated data that are interoperable in terms of their physiological meaning. In this work, we outline the development of a workflow to modularize the simulation of tissue-level processes in physiology. In particular, we show how, via this approach, we can systematically extract, parcellate and annotate tissue histology data to represent component units of tissue function.

Gene expression analysis identifies global gene dosage sensitivity in cancer.

Many cancer-associated somatic copy number alterations (SCNAs) are known. Currently, one of the challenges is to identify the molecular downstream effects of these variants. Although several SCNAs are known to change gene expression levels, it is not clear whether each individual SCNA affects gene expression.

Ultra high content image analysis and phenotype profiling of 3D cultured micro-tissues.

In many situations, 3D cell cultures mimic the natural organization of tissues more closely than 2D cultures. Conventional methods for phenotyping such 3D cultures use either single or multiple simple parameters based on morphology and fluorescence staining intensity. However, due to their simplicity many details are not taken into account which limits system-level study of phenotype characteristics.

Structuring research methods and data with the research object model: genomics workflows as a case study.

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment.

Learning Bayesian networks for clinical time series analysis.

Introduction: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.

Benefits of a short, practical questionnaire to measure subjective perception of nasal appearance after aesthetic rhinoplasty.

Background: The authors tested a short, practically designed questionnaire to assess changes in subjective perception of nasal appearance in patients before and after aesthetic rhinoplasty.

Methods: A prospective cohort study was conducted in a group of 121 patients who desired aesthetic rhinoplasty and were operated on by one surgeon. The questionnaire contained five questions (E1-E5) based on a five-point Likert scale and a visual analogue scale (range, 0 to 10).

Modeling innate immune response to early Mycobacterium infection.

In the study of complex patterns in biology, mathematical and computational models are emerging as important tools. In addition to experimental approaches, these modeling tools have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to mycobacterial infection and tuberculous granuloma formation. We present an approach in which a computational model represents the interaction of the Mycobacterium infection with the innate immune system in zebrafish at a high level of abstraction.

Predicting genes involved in human cancer using network contextual information.

Protein-Protein Interaction (PPI) networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1) Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself); and (2) Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance) based measure.

Mixed integer evolution strategies for parameter optimization.

Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems.

Identification of common carp innate immune genes with whole-genome sequencing and RNA-Seq data.

The common carp is a candidate model system for immunology research. Using next-generation sequencing technology, we have generated a huge amount of sequence reads from the carp genome and transcriptome. Currently, our aim is to identify carp genes involved in the development of the innate immune response, particularly TIR domain-containing genes, from a preliminary genome assembly.

Analysis of cardiac development in the turtle Emys orbicularis (Testudines: Emidydae) using 3-D computer modeling from histological sections.

In this article we present a 3-D modeling study of cardiac development in the European pond turtle, Emys orbicularis (of the reptilian order Testudines). The study is aimed at elucidating the embryonic development of the horizontal septum in the ventricle and underscoring the importance of 3-D reconstructions in studying morphogenesis. Turtles possess one common ventricle, partly divided into three cava by a vertical and a horizontal septum, of which the embryonic origins have so far not been described.

Comparison and integration of target prediction algorithms for microRNA studies.

microRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions.

Mining and analysing spatio-temporal patterns of gene expression in an integrative database framework.

Mining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining results.

ZebraFISH: fluorescent in situ hybridization protocol and three-dimensional imaging of gene expression patterns.

We present a method and protocol for fluorescent in situ hybridization (FISH) in zebrafish embryos to enable three-dimensional imaging of patterns of gene expression using confocal laser scanning microscopy. We describe the development of our protocol and the processing workflow of the three-dimensional images from the confocal microscope. We refer to this protocol as zebraFISH.

An efficient, versatile and scalable pattern growth approach to mine frequent patterns in unaligned protein sequences.

Motivation: Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may be particularly difficult for a set of deviating sequences. In contrast, two algorithms, PRATT2 (http//www.

How to complete performance graphs in content-based image retrieval: add generality and normalize scope.

The performance of a Content-Based Image Retrieval (CBIR) system, presented in the form of Precision-Recall or Precision-Scope graphs, offers an incomplete overview of the system under study: The influence of the irrelevant items (embedding) is obscured. In this paper, we propose a comprehensive and well-normalized description of the ranking performance compared to the performance of an Ideal Retrieval System defined by ground-truth for a large number of predefined queries. We advocate normalization with respect to relevant class size and restriction to specific normalized scope values (the number of retrieved items).