The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals.

The biclustering method can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. This is because the biclustering approach, in contrast to the conventional clustering techniques, focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. However, the biclustering problem is inherently intractable, and it is often computationally costly to find biclusters with high levels of coherence.

Glioblastoma multiforme (GBM) is,the most common and lethal primary brain tumor in adults. We combined neuroimaging and DNA microarray analysis to create a multidimensional map of gene-expression patterns in GBM that provided clinically relevant insights into tumor biology. Tumor contrast enhancement and mass effect predicted activation of specific hypoxia and proliferation gene-expression programs, respectively.

Biotechnology is an area of great innovations that promises to have deep impact on everyday life thanks to profound changes in biology, medicine, and health care. This article will span from the description of the biochemical principles of molecular biology to the definition of the physics that supports the technology and to the devices and algorithms necessary to observe molecular events in a controlled, portable, and highly parallel manner.

The importance of circuits and systems for high-throughput biological data acquisition in biomedical research are discussed. High-throughput biological data acquisition and processing technologies have shifted the focus of biological research from the the traditional experimental science to that of information science. Powerful computation and communication means can be applied to a very large amount of apparently incoherent data coming from biomedical research.

Understanding the complexity of the cellular machinery represents a grand challenge in molecular biology. To contribute to the deconvolution of this complexity, a novel inference algorithm based on linear ordinary differential equations is proposed, based solely on high-throughput gene expression data. The algorithm can infer (i) gene-gene interactions from steady state expression profiles and (ii) mode-of-action of the components that can trigger changes in the system.