Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

One of the central elements in systems biology is interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, they further analyzed comprehended by identifying model parameters using experimental data. However, all cannot be found gradient-based optimization methods fitting to data due non-differentiable character problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification dynamic models biology. The code built on top PySCeS that provides an engine run simulations. idea behind methodology provide set derivative-free utilize population candidate solutions find better solution iteratively. Additionally, propose two surrogate-assisted methods, namely, combination k-nearest-neighbor regressor with Reversible Differential Evolution Distribution Algorithm, speeds up convergence. We example well-studied glycolytic pathway Saccharomyces cerevisiae.