A framework supporting multi-compartment stochastic simulation and parameter optimisation for investigating biological system development

TL;DR: The main contribution of this paper is the enhancement of a stochastic, multi-compartments simulator by means of a metaheuristic-based module for parameter estimation, the first such application available in literature, where the subject of parameter estimation is well-established only for deterministic single-compartment models.

Abstract: In this paper we propose a simulation framework specifically suited for developmental biology studies. It is mainly composed of three parts. First, it is based on a multiscale computational model, and related logic-oriented specification language compiler, supporting large-scale networks of compartments and an enhanced model of chemical reactions addressing molecule transfer. Second, we rely on a simulation engine based on an optimised version of the Gillespie stochastic simulation algorithm, which is able to simulate fine events at intracellular and multicellular level. Third, a metaheuristic-based module for automatically calibrating model parameters such as reaction rates is exploited. As a case study we model the first stages of Drosophila melanogaster development, which generate the early spatial pattern of gap gene expression. Results show the formation of a precise spatial pattern which has been successfully compared with observations acquired from the real embryo gene expressions. In particular, adopting the Covariance Matrix Adaptation Evolution Strategy for parameter estimation is crucial for the quality of the results achieved, reducing the error of a 60% from the initial formulation of parameters. The main contribution of this paper is the enhancement of a stochastic, multi-compartment simulator by means of a metaheuristic-based module for parameter estimation. This is the first such application available in literature, where the subject of parameter estimation is well-established only for deterministic single-compartment models. Moreover this is the first work demonstrating the ability of the Gillespie stochastic simulation algorithm, when properly equipped with additional parameter optimisation techniques, to model large-scale, complex biological systems.