Nonlinear limits to the information capacity of optical fiber communications

The exponential growth in the rate at which information can be communicated through an optical fiber is a key element in the so called information revolution. However, like all exponential growth laws, there are physical limits to be considered. The nonlinear nature of the propagation of light in optical fiber has made these limits difficult to elucidate. Here we obtain basic insights into the limits to the information capacity of an optical fiber arising from these nonlinearities. The key simplification lies in relating the nonlinear channel to a linear channel with multiplicative noise, for which we are able to obtain analytical results. In fundamental distinction to the linear additive noise case, the capacity does not grow indefinitely with increasing signal power, but has a maximal value. The ideas presented here have broader implications for other nonlinear information channels, such as those involved in sensory transduction in neurobiology. These have been often examined using additive noise linear channel models, and as we show here, nonlinearities can change the picture qualitatively.Comment: 1 figure, 7 pages, submitted to Natur

Computational neuroanatomy and co-expression of genes in the adult mouse brain, analysis tools for the Allen Brain Atlas

We review quantitative methods and software developed to analyze genome-scale, brain-wide spatially-mapped gene-expression data. We expose new methods based on the underlying high-dimensional geometry of voxel space and gene space, and on simulations of the distribution of co-expression networks of a given size. We apply them to the Allen Atlas of the adult mouse brain, and to the co-expression network of a set of genes related to nicotine addiction retrieved from the NicSNP database. The computational methods are implemented in {\ttfamily{BrainGeneExpressionAnalysis}}, a Matlab toolbox available for download.Comment: 25 pages, 8 figures, accepted in Quantitative Biology (2012) 000

Drug discovery in tuberculosis: a molecular approach

Despite unquestionable success of the combination drug therapy, tuberculosis (TB) very recently has drawn major attention because of the global upsurge of MDR-TB, XDR -TB and HIV-TB co-infection cases. In the last four decades, only one compound is added to the treatment regimen leaving ample opportunities to find out a new generation of TB drugs. The modern concept of drug discovery utilizes the integrated knowledge of genomics, proteomics, molecular biology and systems biology to identify more specific targets. The purpose of this review is to revisit the field of tuberculosis drug discovery based on those new concepts to identify novel targets

Regulation of Mammalian Gene Expression

Regulation of mammalian gene expression has been an ever growing subject in the field of Biology and the biomedical science research. In the last several decades, extensive amount of research together with the implementation of the latest technologies revealed that the whole process is regulated at the multiple stages with a series of interconnected complex biochemical and molecular pathways. Unearthing this complexity in one hand helps us in understanding the concerted effort put by the respective cellular machinery to regulate the whole process, and on the other hand, it provides a new insight about the development of several diseases where gene expressions play a pivotal role. Discussions here focus on the involvement of transcription factors or cofactors and the linkage of the transcription network with the signal transduction pathways. Besides proteins as a regulator, the role of the nucleic acids such as miRNA, chromosomal conformation and the modification of DNA bases or core histone proteins, in gene expression has also been explored. The purpose of this chapter is to provide the big picture of the diverse regulatory network and the phenomenal complexity of the regulation of gene expression