Balaji Veeramani

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Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices(More)
UNLABELLED Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library(More)
We present a novel signal processing methodology to determine the direction and the strength of coupling between coupled nonlinear systems. The methodology is based on multivariate local linear prediction in the reconstructed state spaces of the observed variables from each multivariable nonlinear system. Application of the method is illustrated with(More)
Ratiometric quantification of CFP/YFP FRET enables live-cell time-series detection of molecular interactions, without the need for acceptor photobleaching or specialized equipment for determining fluorescence lifetime. Although popular in widefield applications, its implementation on a confocal microscope, which would enable sub-cellular resolution, has met(More)
Many diseases are caused by failures of metabolic enzymes. These enzymes exist in the context of networks defined by the static topology of enzyme-metabolite interactions and by the reaction fluxes that are feasible at steady state. We use the local topology and the flux correlations to identify how failures in the metabolic network may lead to disease.(More)
Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations(More)
Directional information flow between coupled nonlinear systems is of practical interest in many areas like bioengineering, chemistry, physics and electrical engineering. Due to the high complexity and nonlinearity of the coupled chaotic systems, linear modeling approaches may fail to capture the proper dynamics and thus the proper directional information(More)
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