The J‐Express package has been designed to facilitate the analysis of microarray data with an emphasis on efficiency, usability, and comprehensibility. The J‐Express system provides a powerful and integrated platform for the analysis of microarray gene expression data. It is platform‐independent in that it requires only the availability of a Java virtual machine on the system. The system includes a range of analysis tools and a project management system supporting the organization and documentation of an analysis project. This unit describes the J‐Express tool, emphasizing central concepts and principles, and gives examples of how it can be used to explore gene expression data sets. Curr. Protoc. Bioinform. 21:7.3.1‐7.3.25. © 2008 by John Wiley & Sons, Inc.

a b s t r a c tSubstrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.

Figure 1: Demonstration of our system capabilities from three different zooming levels (left to right). We showcase a scene containing 10 6 diffusing and reacting molecules in real-time at 30 FPS. AbstractIn this paper we propose a new type of a particle systems, tailored for illustrative visualization purposes, in particular for visualizing molecular reactions in biological networks. Previous visualizations of biochemical processes were exploiting the results of agent-based modeling. Such modeling aims at reproducing accurately the stochastic nature of molecular interactions. However, it is impossible to expect events of interest happening at a certain time and location, which is impractical for storytelling. To obtain the means of controlling molecular interactions, we propose to govern passive agents with an omniscient intelligence, instead of giving to the agents the freedom of initiating reaction autonomously. This makes it possible to generate illustrative animated stories that communicate the functioning of the molecular machinery. The rendering performance delivers for interactive framerates of massive amounts of data, based on the dynamic tessellation capabilities of modern graphics cards. Finally, we report an informal expert feedback we obtained from the potential users.

Background: The COVID-19 pandemic has led to major social and economic changes that could impact public mental health. The main aim of the current study was to investigate mental health in Norway during the COVID-19 outbreak (since the first confirmed case on 26 February 2020). Methods: The results are from the first wave of the data collection (1 April–2 June 2020), which took place during the outbreak along with its initial restrictions. A total of 19,372 (11,883 students) people participated in a cross-sectional web-based survey. Results: A total of 21.8% scored above the cut-off for depression and 23.7% for anxiety. Severity of symptoms was associated with the accumulation of risk factors, such as possible/confirmed infection for oneself or one’s family, female/other sex, students, having mental health problems, increased use of tobacco, increased use of alcohol, less exercise, losing one’s job, suffering economic impact and lower education. Conclusions: COVID-19 could have a negative association with public mental health, especially for certain risk groups. Future data-collection waves will provide further insight into the development of symptoms following the pandemic.