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<em>In Silico</em> Approach to Find an Optimal Strategy in Selective Targeting of Cancer Cells | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

In Silico Approach to Find an Optimal Strategy in Selective Targeting of Cancer Cells

Subhadip Raychaudhuri*

Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi, India

*Corresponding Author:
Subhadip Raychaudhuri
Center for Computational Biology
Indraprastha Institute of Information Technology, Delhi, India
Tel: +911126907438
E-mail: [email protected]

Received date: April 26, 2016; Accepted date: June 10, 2016; Published date: June 15, 2016

Citation: Raychaudhuri S (2016) In Silico Approach to Find an Optimal Strategy in Selective Targeting of Cancer Cells. J Comput Sci Syst Biol 9:112-118. doi:10.4172/jcsb.1000228

Copyright: © 2016 Raychaudhuri S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Cancer chemoresistance (including adaptive resistance) has emerged as a barrier in developing successful chemotherapeutic strategies. We use Monte Carlo simulation based single cell analysis to provide insights into the regulatory mechanisms for generating chemoresistance under TRAIL (death ligand) induction. Based on stochastic computer simulations we elucidate systems biology of cancer cell apoptosis (at the level of single cells) and search for an optimal death ligand from a group of recently studied TRAIL affinity variants. In addition to assessing the population level behavior in cell death activation under induction of TRAIL/TRAIL-variant, Monte Carlo approach allows us to analyze cell-to-cell stochastic fluctuations in time-to-death that has implications for generating resistant cancer cells. We discuss application of Monte Carlo simulations in the context of developing more personalized approaches in treating various cancers. Initial findings indicate single cell in silico approaches can be utilized for disease subtype classification and in characterizing a given tumor, and, for finding an optimal strategy (such as network modules to target and ligands needed) in targeting a given tumor.


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