Parameters estimation

Pleione’s parameterization methods rely on Computational Load Distribution. The naïve approach is to use the python’s multiprocessing API and each simulation distributed within the Pool of available (minus one) cores. This approach would make pleione’s methods compatible with Microsoft Windows and Apple OS X. However, to take fully advantage of High-Performance Computing architectures, pleione’s methods rely on SLURM –Simple Linux Utility for Resource Management– (SLURM) to distribute simulations through your infrastructure, remote infrastructures, and cloud services like Google Compute Engine, Microsoft Azure, and Amazon Elastic Compute Cloud.

Up to date, pleione’s parameterization methods rely on 4 simulations engines: KaSim and PISKaS simulate kappa language models. Unlike KaSim, PISKaS is able to simulate multiple compartment models distributing the calculation of each compartment through multiple cores. In the other hand, BioNetGen2 and NFsim simulate BioNetGen language models. Despite KaSim and PISKaS, BioNetGen2 does not provide a Command-Line Interface to especify simulation parameters and rather, the simulation parameters (e.g. time to simulation, number of points to report, …) must be given inside the model specification. Moreover, you need to especify the simulation engine to use: Deterministic simulation through CVODE, the Stochastic Simulation Algorithm SSA, Exact Hybrid Particle/Population Algorithm HPP, and the Partition-Leap Algorithm PLA. Moreover, NFsim could be used by BioNetGen2 to simulate models or called externally after creating the model xml especification with BioNetGen2 –xml option.

Because the software requirements and differences, we provide specific documentation to all of them rather than provide common guidelines and then stating the differences.

Parameterization of kappa-language Rule-Based Models

• Parameterization with KaSim
• Parameterization with PISKaS

Parameterization of BioNetGen language Rule-Based Models

• Parameterization with BioNetGen2
• Parameterization with NFsim

Common to all parameterization methods, there are 9 algebraic objective functions and one statistical function already implemented in the code. Moreover, the code sort the models by their rank and therefore, ranks can be added and sorted again, making the possibility to use a Multiple Objective Genetic Algorithm.

• Model Validation
• Objective Functions
  • Algebraic Objective Functions
  • Statistical Objective Functions
  • Multiple Objective Functions
  • Add a fitness function to Pleione

Note

Installation instructions: Instructions to install KaSim, BioNetGen, NFsim, and PISKaS are available in their source code webpages. Nonetheless, here you will find basic information to clone using git or download the software and install it.

To install SLURM, you should have admin access to your infrastructure and an UNIX-based OS. Detailed instructions are provided here: Installing SLURM in your machine