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In our previous work, we have shown that time lags can be incorporated in information theory based metrics to further improve the efficiency of gene regulatory network inference. In particular, we have studied the mutual information metric where we found that mutual information saturates after a certain data size. We also proposed the time lagged mutual information metric and showed that the accuracy...
In Alzheimer's disease (AD), the aggregates of amyloid-β (Aβ) peptide form large fibrillar deposits and are believed to be the cause for cognitive decline. Hence, the nucleation dependent Aβ aggregation process has been subject to intense investigation for biophysical understanding. Here, we present a molecular-level simulation model to understand and predict the dynamics of this process supported...
The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This paradigm requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use...
This paper presents a parametric model to estimate the DNA-protein binding time using the DNA and protein structures and details of the binding site. To understand the stochastic behavior of biological systems, we propose an "in silico" stochastic event based simulation that determines the temporal dynamics of different molecules. This paper presents a parametric model to determine the execution...
Quantum mechanics and molecular dynamic simulation provide important insights into structural configurations and molecular interaction data today. To extend this atomic/molecular level capability to system level understanding, we propose an "in silico" stochastic event based simulation technique. This simulation characterizes the time domain events as random variables represented by probabilities...
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