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

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.

Modeling Intercellular Communication as a Survival Strategy of Cancer Cells: An in Silico Approach on a Flexible Bioinformatics Framework

“…We are interested in regulating the growth of tumor cells, and in our virtual laboratory, we already have ~325 reactions, including EGF/MAPK/JAK-STAT, 20 PI3K, 29 and caspases 30–32 pathways. We integrate the canonical Wnt pathway and other pathways, such as Notch, 33 Hh, 34 and hypoxia stress pathways (Fig.…”

“…signaling pathways required and previously simulated. We are interested in regulating the growth of tumor cells, and in our virtual laboratory, we already have ∼325 reactions, including EGF/MAPK/JAK-STAT, 20 PI3K, 29 and caspases [30][31][32] pathways. We integrate the canonical Wnt pathway notes: Example of reactions used for the model.…”

“…Thus, they are not suitable to model cells in their social context, along with all those biological mechanisms that involve two or more cells, namely signal transmission that is important in the scenario discussed in this article. Therefore, in recent years, other major requirements in the simulation of intracellular and intercellular systems have emerged, guiding the development of new computational models and tools 19,20 : (1) topology and locality, respectively, used (a) to model complex topologies of signaling networks, which involve feed forward and feed backward relationships between signaling components placed into different intracellular compartments and structures and (b) to provide a global view of the signaling system as a composition of local interactions and (2) considering the amount of each component of the signaling pathways with respect to time, their location, and status (active, inactive, free, bound, etc.). Notable examples of platforms supporting multicompartment models are Bio-PEPA, 21 MCell, 22 COPASI, 23 Virtual Cell, 24 CompuCell 3D, 25 and BTSSOC-Cellulat.…”