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Published in:
2018 | OriginalPaper | Chapter
On the Use of Betweenness Centrality for Selection of Plausible Trajectories in Qualitative Biological Regulatory Networks
Authors : Muhammad Tariq Saeed, Jamil Ahmad, Amjad Ali
Published in: Bioinformatics and Biomedical Engineering
Publisher: Springer International Publishing
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Abstract
Qualitative modeling approach is widely used to study the behavior of Biological Regulatory Networks. The approach uses directed graphs also called as
, to represent system dynamics. As the number of genes increase, the complexity of stategraph increases exponentially. The identification of important trajectories and isolation of more probable dynamics from less significant ones constitutes an important problem in qualitative modeling of biological networks. In this work, we implement a parallel approach for identification of important dynamics in qualitative models. Our implementation uses the concept of
. For parallelization, we used a Java based library MPJ Express to implement our approach. We evaluate the performance of our implementation on well known case study of bacteriophage lambda. We demonstrate the effectiveness of our implementation by selecting important trajectories and correlating with experimental data.
, to represent system dynamics. As the number of genes increase, the complexity of stategraph increases exponentially. The identification of important trajectories and isolation of more probable dynamics from less significant ones constitutes an important problem in qualitative modeling of biological networks. In this work, we implement a parallel approach for identification of important dynamics in qualitative models. Our implementation uses the concept of
. For parallelization, we used a Java based library MPJ Express to implement our approach. We evaluate the performance of our implementation on well known case study of bacteriophage lambda. We demonstrate the effectiveness of our implementation by selecting important trajectories and correlating with experimental data.