Recombinant cell-detecting RaDR-GFP in mice reveals an association between genomic instability and radiation-induced-thymic lymphoma.

In this study, we aimed to investigate how homologous recombinant (HR)-related genomic instability is involved in ionizing radiation (IR)-induced thymic lymphoma in mice. We divided five-week-old Rosa26 Direct Repeat-GFP (RaDR-GFP) transgenic mice into non-IR control and IR groups and exposed the mice in the IR group to a 7.2 Gy dose of γ-rays, delivered in 1.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology.

Sm-Like Protein-Mediated RNA Metabolism Is Required for Heat Stress Tolerance in Arabidopsis.

Sm-like proteins play multiple functions in RNA metabolism, which is essential for biological processes such as stress responses in eukaryotes. The Arabidopsis thaliana sad1 mutant has a mutation of sm-like protein 5 (LSM5) and shows impaired drought and salt stress tolerances. The lsm5/sad1 mutant also showed hypersensitivity to heat stress.

Genetic Network Inference Using Hierarchical Structure.

Many methods for inferring genetic networks have been proposed, but the regulations they infer often include false-positives. Several researchers have attempted to reduce these erroneous regulations by proposing the use of a priori knowledge about the properties of genetic networks such as their sparseness, scale-free structure, and so on. This study focuses on another piece of a priori knowledge, namely, that biochemical networks exhibit hierarchical structures.

Identifying problematic drugs based on the characteristics of their targets.

Identifying promising compounds during the early stages of drug development is a major challenge for both academia and the pharmaceutical industry. The difficulties are even more pronounced when we consider multi-target pharmacology, where the compounds often target more than one protein, or multiple compounds are used together. Here, we address this problem by using machine learning and network analysis to process sequence and interaction data from human proteins to identify promising compounds.