Fusion genes are chromosomal aberrations that are found in many cancers and can be used as prognostic markers and drug targets in clinical practice. Fusions can lead to production of oncogenic fusion proteins or to enhanced expression of oncogenes. Several recent studies have reported that some fusion genes can escape microRNA regulation via 3′-untranslated region (3′-UTR) deletion. We performed whole transcriptome sequencing to identify fusion genes in glioma and discovered

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

Automated image analysis software, CellC, was developed and validated for quantification of bacterial cells from digital microscope images. CellC enables automated enumeration of bacterial cells, comparison of total count and specific count images [e.g., 4',6-diamino-2-phenylindole (DAPI) and fluorescence in situ hybridization (FISH) images], and provides quantitative estimates of cell morphology. The software includes an intuitive graphical user interface that enables easy usage as well as sequential analysis of multiple images without user intervention. Validation of enumeration reveals correlation to be better than 0.98 when total bacterial counts by CellC are compared with manual enumeration, with all validated image types. The software is freely available and modifiable: the executable files and MATLAB source codes can be obtained at www. cs. tut.fi/sgn/csb/cellc.

Fluorescence microscopy combined with digital imaging constructs a basic platform for numerous biomedical studies in the field of cellular imaging. As the studies relying on analysis of digital images have become popular, the validation of image processing methods used in automated image cytometry has become an important topic. Especially, the need for efficient validation has arisen from emerging high-throughput microscopy systems where manual validation is impractical. We present a simulation platform for generating synthetic images of fluorescence-stained cell populations with realistic properties. Moreover, we show that the synthetic images enable the validation of analysis methods for automated image cytometry and comparison of their performance. Finally, we suggest additional usage scenarios for the simulator. The presented simulation framework, with several user-controllable parameters, forms a versatile tool for many kinds of validation tasks, and is freely available at http://www.cs.tut.fi/sgn/csb/simcep.

Cells are dynamical systems of biomolecular interactions that process information from their environment to mount diverse yet specific responses. A key property of many self-organized systems is that of criticality: a state of a system in which, on average, perturbations are neither dampened nor amplified, but are propagated over long temporal or spatial scales. Criticality enables the coordination of complex macroscopic behaviors that strike an optimal balance between stability and adaptability. It has long been hypothesized that biological systems are critical. Here, we address this hypothesis experimentally for system-wide gene expression dynamics in the macrophage. To this end, we have developed a method, based on algorithmic information theory, to assess macrophage criticality, and we have validated the method on networks with known properties. Using global gene expression data from macrophages stimulated with a variety of Toll-like receptor agonists, we found that macrophage dynamics are indeed critical, providing the most compelling evidence to date for this general principle of dynamics in biological systems.complex systems ͉ normalized compression distance ͉ information theory M any complex systems are capable of undergoing a phase transition between a disorganized and an organized state. This phenomenon has been observed in enzyme kinetics (1), growth of bacterial populations (2), foraging in ant colonies (3), brain activity (4), and traffic flow on the Internet (5). A system that is operating near such a phase transition is said to be critical. At equilibrium, this transition will occur at a critical value of a system parameter, such as the Curie temperature in a ferromagnet, below which the system can maintain spontaneous magnetization. Nonequilibrium systems, however, are capable of selforganizing to such a critical state, whereby complex behavior can emerge in a robust manner without fine-tuning the details of the system (6, 7).A hallmark of critical behavior is the spontaneous emergence of complex and coordinated macroscopic behavior in the form of long-range spatial or temporal correlations. Such coordination across many scales enables information to propagate over time from one part of the system to another with a high degree of specificity and sensitivity. For example, measurements of human brain oscillations revealed such critical dynamics of neural networks, implying their ability to effectively propagate information and rapidly reorganize (8). Similarly, measurements of computer network traffic indicate that the Internet exhibits critical dynamics, accordingly, suggesting optimal information transfer (9, 10). Many other complex systems, such as financial markets (11), forest fires (12), neuronal networks supporting our senses (13), and biological macroevolution (14) have been shown to self-organize to a critical state.A living cell is a complex dynamical system of interacting biomolecules. While this system exhibits stability even in varying environments, it is also capable of changing state...