Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO’s Performance

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10834)


The simulation and analysis of mathematical models of biological systems require a complete knowledge of the reaction kinetic constants. Unfortunately, these values are often difficult to measure, but they can be inferred from experimental data in a process known as Parameter Estimation (PE). In this work, we tackle the PE problem using Particle Swarm Optimization (PSO) coupled with three different reboot strategies, which aim to reinitialize particle positions to avoid local optima. In particular, we highlight the better performance of PSO coupled with the reboot strategies with respect to standard PSO. Finally, since the PE requires a huge number of simulations at each iteration of PSO, we exploit cupSODA, a GPU-powered deterministic simulator, which performs all simulations and fitness evaluations in parallel.


Particle Swarm Optimization Parameter Estimation GPGPU computing cupSODA Systems Biology 



We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce GTX Titan X GPU used for this research.

This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN, USA.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanoItaly
  2. 2.Institute of Molecular Bioimaging and PhysiologyItalian National Research CouncilCefalúItaly
  3. 3.SYSBIO.IT Centre of Systems BiologyMilanoItaly
  4. 4.Department of Human and Social SciencesUniversity of BergamoBergamoItaly

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