Research Article
A Scalable Parallel Framework for Multicellular Communication in Bacterial Quorum Sensing
@INPROCEEDINGS{10.1007/978-3-030-24202-2_14, author={Satyaki Roy and Mohammad Islam and Dipak Barua and Sajal Das}, title={A Scalable Parallel Framework for Multicellular Communication in Bacterial Quorum Sensing}, proceedings={Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13--14, 2019, Proceedings}, proceedings_a={BICT}, year={2019}, month={7}, keywords={Autoinducer Quorum Sensing Gillespie Multicellular system Noise analysis Population evolution Scalability}, doi={10.1007/978-3-030-24202-2_14} }- Satyaki Roy
Mohammad Islam
Dipak Barua
Sajal Das
Year: 2019
A Scalable Parallel Framework for Multicellular Communication in Bacterial Quorum Sensing
BICT
Springer
DOI: 10.1007/978-3-030-24202-2_14
Abstract
Certain species of bacteria are capable of communicating through a mechanism called wherein they release and sense signaling molecules, called , to and from the environment. Despite stochastic fluctuations, bacteria gradually achieve coordinated gene expression through QS, which in turn, help them better adapt to environmental adversities. Existing sequential approaches for modeling information exchange via QS for large cell populations are time and computational resource intensive, because the advancement in simulation time becomes significantly slower with the increase in molecular concentration. This paper presents a scalable parallel framework for modeling multicellular communication. Simulations show that our framework accurately models the molecular concentration dynamics of QS system, yielding better speed-up and CPU utilization than the existing sequential model that uses the exact Gillespie algorithm. We also discuss how our framework accommodates evolving population due to cell birth, death and heterogeneity due to noise. Furthermore, we analyze the performance of our framework vis-á-vis the effects of its data sampling interval and Gillespie computation time. Finally, we validate the scalability of the proposed framework by modeling population size up to 2000 bacterial cells.