Converting a Systems Dynamic Model to an Agent-based model for studying the Bicoid morphogen gradient in Drosophila embryo

TL;DR

This paper compares stochastic and agent-based models for simulating the Bicoid morphogen gradient in Drosophila embryos, highlighting their differences in complexity, results, and applicability in systems biology.

Contribution

It demonstrates how to convert a systems dynamic model into an agent-based model and compares their effectiveness in studying morphogen gradients.

Findings

Stochastic models produce smoother results than agent-based models.

Agent-based models may require additional analysis for detailed insights.

Both models show different results despite simulating the same biological process.

Abstract

The concentration gradient of the Bicoid morphogen, which is established during the early stages of a Drosophila melanogaster embryonic development, determines the differential spatial patterns of gene expression and subsequent cell fate determination. This is mainly achieved by diffusion elicited by the different concentrations of the Bicoid protein in the embryo. Such chemical dynamic progress can be simulated by stochastic models, particularly the Gillespie alogrithm. However, as with various modelling approaches in biology, each technique involves drawing assumptions and reducing the model complexity sometimes limiting the model capability. This is mainly due to the complexity of the software modelling approaches to construct these models. Agent-based modelling is a technique which is becoming increasingly popular for modelling the behaviour of individual molecules or cells in computational biology. This paper attempts to compare these two popular modelling techniques of stochastic and agent-based modelling to show how the model can be studied in detail using the different approaches. This paper presents how to use these techniques with the advantages and disadvantages of using either of these. Through various comparisons, such as computation complexity and results obtained, we show that although the same model is implemented, both approaches can give varying results. The results of the paper show that the stochastic model is able to give smoother results compared to the agent-based model which may need further analysis at a later stage. We discuss the reasons for these results and how these could be rectified in systems biology research.